File size: 91,235 Bytes
6fa4bc9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 |
{
"paper_id": "2021",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T13:27:43.268980Z"
},
"title": "An End-to-End Approach for Full Bridging Resolution",
"authors": [
{
"first": "Joseph",
"middle": [],
"last": "Renner",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "INRIA Lille",
"location": {
"settlement": "Magnet",
"country": "France"
}
},
"email": "[email protected]"
},
{
"first": "Priyansh",
"middle": [],
"last": "Trivedi",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "INRIA Lille",
"location": {
"settlement": "Magnet",
"country": "France"
}
},
"email": "[email protected]"
},
{
"first": "Gaurav",
"middle": [],
"last": "Maheshwari",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "INRIA Lille",
"location": {
"settlement": "Magnet",
"country": "France"
}
},
"email": "[email protected]"
},
{
"first": "R\u00e9mi",
"middle": [],
"last": "Gilleron",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "INRIA Lille",
"location": {
"settlement": "Magnet",
"country": "France"
}
},
"email": "[email protected]"
},
{
"first": "Pascal",
"middle": [],
"last": "Denis",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "INRIA Lille",
"location": {
"settlement": "Magnet",
"country": "France"
}
},
"email": "[email protected]"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "In this article, we describe our submission to the CODI-CRAC 2021 Shared Task on Anaphora Resolution in Dialogues-Track BR (Gold) 1. We demonstrate the performance of an end-to-end transformer-based higher-order coreference model finetuned for the task of full bridging. We find that while our approach is not effective at modeling the complexities of the task, it performs well on bridging resolution, suggesting a need for investigations into a robust anaphor identification model for future improvements.",
"pdf_parse": {
"paper_id": "2021",
"_pdf_hash": "",
"abstract": [
{
"text": "In this article, we describe our submission to the CODI-CRAC 2021 Shared Task on Anaphora Resolution in Dialogues-Track BR (Gold) 1. We demonstrate the performance of an end-to-end transformer-based higher-order coreference model finetuned for the task of full bridging. We find that while our approach is not effective at modeling the complexities of the task, it performs well on bridging resolution, suggesting a need for investigations into a robust anaphor identification model for future improvements.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Anaphora is a discourse level phenomenon wherein a linguistic entity (referred to as anaphor) is associated with some other linguistic entity (referred to as an antecedent) within a document (Tognini-Bonelli, 2001 ). Broadly, the phenomenon is divided into coreference and bridging anaphora depending on whether the anaphoric references are linked to associated antecedents with an identical (is-a), or a non-identical (\u00ac is-a) relation, respectively. Following is an example of bridging anaphora -the focus of this article:",
"cite_spans": [
{
"start": 191,
"end": 213,
"text": "(Tognini-Bonelli, 2001",
"ref_id": "BIBREF24"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Starbucks has a new take on the unicorn cappuccino. One employee accidentally leaked a picture of the secret new drink.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Here, the noun phrase \"One employee\" (bridging anaphor) is anaphorically linked to the antecedent -\"Starbucks\". In this instance, bridging anaphor can be thought of as an expression with an implicit argument, i.e., one employee (of Starbucks) .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The task of full bridging is that of identifying anaphors from a given set of linguistic entities in the document and linking them with their nonidentical associated antecedent. This task is arguably more difficult and relatively understudied than that of entity coreference resolution -which involves identifying coreferent linguistic entities. This difficulty can be seen from the fact that Bridging anaphors are less likely to conform to syntactic or surface clues (Kobayashi and Ng, 2020) , and the low inter-annotator agreement for bridging annotations (Markert et al., 2012) . Hindrances to rapid progress on the task, however, also include lack of ample gold labeled data (R\u00f6siger, 2018; Hou, 2020) , and standardised evaluation schemes (Kobayashi and Ng, 2020) . The CODI-CRAC 2021 shared task (Khosla et al., 2021) is thus a welcome addition to the existing set of datasets in the field as it provides a consistent benchmark across multiple gold-annotated datasets. However, unlike the task variant that the majority of existing approaches tackle, the shared task also includes identification of the bridging anaphors as a part of the task. That is, most existing approaches (Lassalle and Denis, 2011; Hou et al., 2013; Hou, 2018a,b) assume the anaphor (here, \"one employee\") to be given, and are limited to selecting the correct antecedent for this phrase, amongst a predefined list of linguistic entities. The identification of anaphors compounds the difficulty of the task, owing to, amongst other things, the number of markables (linguistic entities) in a document (See Table 1 ), and low recall of anaphoric noun phrases in existing datasets .",
"cite_spans": [
{
"start": 468,
"end": 492,
"text": "(Kobayashi and Ng, 2020)",
"ref_id": "BIBREF13"
},
{
"start": 558,
"end": 580,
"text": "(Markert et al., 2012)",
"ref_id": "BIBREF16"
},
{
"start": 679,
"end": 694,
"text": "(R\u00f6siger, 2018;",
"ref_id": "BIBREF22"
},
{
"start": 695,
"end": 705,
"text": "Hou, 2020)",
"ref_id": null
},
{
"start": 744,
"end": 768,
"text": "(Kobayashi and Ng, 2020)",
"ref_id": "BIBREF13"
},
{
"start": 802,
"end": 823,
"text": "(Khosla et al., 2021)",
"ref_id": "BIBREF11"
},
{
"start": 1184,
"end": 1210,
"text": "(Lassalle and Denis, 2011;",
"ref_id": "BIBREF14"
},
{
"start": 1211,
"end": 1228,
"text": "Hou et al., 2013;",
"ref_id": "BIBREF8"
},
{
"start": 1229,
"end": 1242,
"text": "Hou, 2018a,b)",
"ref_id": null
}
],
"ref_spans": [
{
"start": 1583,
"end": 1590,
"text": "Table 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "That said, coreference resolution is a more complex task in terms of the decisions to be taken for correct predictions. Coreference chains are variadic, whereas relations representing bridging anaphors are binary. Further, contemporary approaches treat mention detection as a part of the task. The neural architectures thus proposed for coreference resolution are often more expressive than the task of full bridging (with gold markables) requires. Based on this observation, we aim to find whether the aforementioned neural architectures can be adapted for solving a linguistically complex but simpler (in the above stated terms) task of full bridging. To that end, we experiment with the independent variant of the transformer based higher-order coreference model (Joshi et al., 2019) .",
"cite_spans": [
{
"start": 766,
"end": 786,
"text": "(Joshi et al., 2019)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "We empirically find that the approach is inadequate in solving the task, and posit that the currently available amount of gold-labeled data is insufficient for this family of approaches. This suggests a need for more data, or the use of external information in solving the full bridging task when using this family of approaches. However, we observe that when tasked with only the resolution of bridging anaphors (i.e., the anaphoric markables are provided as a part of the task), this approach performs significantly better, suggesting that a twostep identification and resolution approach might be beneficial for the task.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this section, we introduce domain specific terms used throughout the article, and formalise the different variants of tasks corresponding to identification and association of bridging anaphors. We use the nomenclature used in .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Task Description",
"sec_num": "2"
},
{
"text": "Markables: A set of linguistic entities in a document of which anaphors and antecedents are both subsets.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Task Description",
"sec_num": "2"
},
{
"text": "Bridging Anaphor: A markable whose interpretation depends upon an antecedent, or more generally, which is implicitly linked to an antecedent with a non-identity relation. We refer to them as simply anaphors in the rest of this article.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Task Description",
"sec_num": "2"
},
{
"text": "Antecedent: The markable which is related to the anaphor with an implicit non-identity relation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Task Description",
"sec_num": "2"
},
{
"text": "There are two primary variants of the task, defined as follows:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Task Variants",
"sec_num": "2.1"
},
{
"text": "\u2022 Bridging Resolution: Given a document, and bridging anaphor markables, the task is that of finding the associated antecedent corresponding to each given bridging anaphors, from one of the markables preceding it.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Task Variants",
"sec_num": "2.1"
},
{
"text": "\u2022 Full Bridging: Given a document, identify the bridging anaphors and find their associ-ated antecedent from one of the markables preceding it.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Task Variants",
"sec_num": "2.1"
},
{
"text": "If the markables are provided alongwith the corpus annotations, we call them gold markables. Otherwise, the approaches solving the task are expected to predict these markables. We refer to the latter as predicted markables.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Task Variants",
"sec_num": "2.1"
},
{
"text": "The CODI-CRAC 2021 Shared-Task: Anaphora Resolution in Dialogues -Track BR (Gold) that we target is thus that of Full Bridging with Gold Markables 2 .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Task Variants",
"sec_num": "2.1"
},
{
"text": "3 Approach",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Task Variants",
"sec_num": "2.1"
},
{
"text": "Our full bridging system is based on the independent version (Joshi et al., 2019) of the higher-order coreference resolution model described in (Lee et al., 2018) . As their model is designed for the coreference resolution with predicted markables, we adapt it for the full bridging task with gold markables. In this sub-section, we provide a brief overview of our augmented model and the associated problem formulation. Following the footsteps of (Lee et al., 2018), we formulate the problem as selecting an antecedent y i , from the set \u03b3(i), for each markable m i in the document. The set includes a dummy antecedent and all the markables in the document before m i , that is \u03b3(i) = { , m 1 , ..., m i\u22121 }. A non-dummy assignment represents an anaphor-antecedent link between m i and y i , while a dummy assignment means that the markable has no antecedent in the document i.e., the markable is not a bridging anaphor.",
"cite_spans": [
{
"start": 61,
"end": 81,
"text": "(Joshi et al., 2019)",
"ref_id": "BIBREF10"
},
{
"start": 144,
"end": 162,
"text": "(Lee et al., 2018)",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Full Bridging with Higher-Order coreference model",
"sec_num": "3.1"
},
{
"text": "For each markable m i , the model learns a distribution P (y i ) over all the previous markable set \u03b3(i):",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Full Bridging with Higher-Order coreference model",
"sec_num": "3.1"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "P (y i ) = e s(m i ,y i ) y \u2208\u03b3(i) e s(m i ,y )",
"eq_num": "(1)"
}
],
"section": "Full Bridging with Higher-Order coreference model",
"sec_num": "3.1"
},
{
"text": "The s(x,y) is the scoring function consisting of three parts defined as:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Full Bridging with Higher-Order coreference model",
"sec_num": "3.1"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "s(x, y) = s m (x) + s m (y) + s p (x, y) (2) s m (x) = FFNN m (x) (3) s p (x, y) = FFNN p (x, y, \u03c6(x, y))",
"eq_num": "(4)"
}
],
"section": "Full Bridging with Higher-Order coreference model",
"sec_num": "3.1"
},
{
"text": "Here x and y are the encoded representation of the two markables. These encodings are obtained by concatenating the transformer's output at the start and end of the span along with the attention vector computed over the output representation of the tokens in the span. \u03c6(x, y) refers to the hand crafted features (the genre indicating the dataset to which this document belongs , speaker-ids, length of the two markables, and the number of tokens between them), while FFNN m and FFNN p represents feed forward network.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Full Bridging with Higher-Order coreference model",
"sec_num": "3.1"
},
{
"text": "Recall that the approach in (Lee et al., 2018 ) is factored into a two-staged beam search. The first stage is responsible for predicting markables: a beam of up to M potential markables is computed based on the spans with the highest markable scores s m (x) out of all possible text spans, up to a certain width, in a document. In the second stage, the pairwise scores s p (x, y) are then only computed between the top markables, in a coarse-to-fine manner. Since we have access to the gold markables, we repurpose the markable scorer s m (x) to score markables as possible antecedents or anaphors. The number of markables can be large in documents, thus the beam search is necessary to keep memory costs down, otherwise the pairwise scoring would not be feasible.",
"cite_spans": [
{
"start": 28,
"end": 45,
"text": "(Lee et al., 2018",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Full Bridging with Higher-Order coreference model",
"sec_num": "3.1"
},
{
"text": "We refer interested readers to (Lee et al., 2018) for a more detailed explanation of the model, including the coarse-to-fine pairwise scorer. The model is trained by the marginal log-likelihood of the possible correct antecedents. We further add a binary cross entropy based supervision over the outputs of the markable scorer s m (i), labeling markables as 0 if its neither an antecedent or an anaphor, and 1 if it is either of the two.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Full Bridging with Higher-Order coreference model",
"sec_num": "3.1"
},
{
"text": "We adapt the model explained above to also solve the Bridging Resolution task (See Sec. 2.1). That is, since the set of anaphor markables is given, we do not intend for the model to identity anaphors, as well as resolve their antecedents, but only do the latter. This allows simplifications to the model and training setting: first, we can pass each anaphor, together with the document (up to the end of the anaphor sentence), into the model at a time, predicting one antecedent for the input anaphor given the relevant part of the document, instead of passing the entire document into the model and predicting all the anaphors and their antecedents at the same time. This change in setting considerably alleviates memory constraints, since the pairwise scorer s p (x, y) is only computed between the one given anaphor and the possible antecedent markables, as opposed to an n by n pairwise scorer in the full bridging setting. This eliminates the need for both the mention scorer s m (x) and the coarse part of the coarse to fine pairwise scorer, leaving just the higher order, \"fine\" pairwise scorer described in (Lee et al., 2018) . Also, this allows the use of a cross entropy loss over all possible antecedents (excluding the dummy class, as we know each labeled anaphor has an antecedent) for each anaphor. Finally, we remove the auxiliary supervision over the markable scorer outputs, as the scorer is no longer used.",
"cite_spans": [
{
"start": 1115,
"end": 1133,
"text": "(Lee et al., 2018)",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Bridging Resolution with Higher-Order coreference model",
"sec_num": "3.2"
},
{
"text": "In this first experiment, we perform full bridging (with gold mentions) over the provided datasets (Sec. 4.1).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments",
"sec_num": "4"
},
{
"text": "The shared task is comprised of conversational documents from four domains, annotated with bridging anaphors using the Universal Anaphora format (Poesio et al., 1999) . The five domains are: Switchboard: A subset of the Switchboard Dialog Act Corpus (Godfrey et al., 1992) , this dataset consists of transcribed phone conversations between two participants about varied topics including child care, recycling and news media. We filter out transcribed speech disfluencies (such as \"emm\", \"ahh\", \"uh\", etc) based on a hand-crafted list of bi-grams as a pre-processing step. Light: Light is a collection of \"character driven, human-human crowdworker interaction involving action, emotes and dialogue\" (Urbanek et al., 2019) in the context of a fantasy text adventure game. Persuasion: A collection of crowdsourced online conversations where a persuader tries to convince the persuadee to donate to a charity were introduced in (Wang et al., 2019 ). An annotated subset of these conversations are a part of this shared task. AMI: Some of the transcripts of multi-speaker office meetings (Carletta, 2006) were annotated with bridging anaphors. Generally, these conversations are the longest of the four.",
"cite_spans": [
{
"start": 145,
"end": 166,
"text": "(Poesio et al., 1999)",
"ref_id": "BIBREF18"
},
{
"start": 250,
"end": 272,
"text": "(Godfrey et al., 1992)",
"ref_id": "BIBREF3"
},
{
"start": 698,
"end": 720,
"text": "(Urbanek et al., 2019)",
"ref_id": "BIBREF25"
},
{
"start": 924,
"end": 942,
"text": "(Wang et al., 2019",
"ref_id": "BIBREF27"
},
{
"start": 1083,
"end": 1099,
"text": "(Carletta, 2006)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets",
"sec_num": "4.1"
},
{
"text": "In all four cases, the test set is held out but Table 1 : Some statistics about the train sets of the datasets used in the shared task. From left to right, we report (i) the number of anaphors across all documents, (ii) number of documents, (iii) avg. length of each document, (iv) avg. number of markables in a document, and, (v) avg. number of tokens between an anaphor and its antecedent.",
"cite_spans": [],
"ref_spans": [
{
"start": 48,
"end": 55,
"text": "Table 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Datasets",
"sec_num": "4.1"
},
{
"text": "documents from the train set are annotated with markables, bridging anaphors and their antecedents. Apart from these, annotated instances from Trains-1993 (Allen, James and Heeman, Peter A., 1995 ), and Trains-1991 (Gross et al., 1993 Spoken Dialog Corpus, a subset of the ARRAU corpus (Uryupina et al., 2020) were used for training the models as well. Table 1 contains further statistics on the train set of these datasets.",
"cite_spans": [
{
"start": 143,
"end": 154,
"text": "Trains-1993",
"ref_id": null
},
{
"start": 155,
"end": 195,
"text": "(Allen, James and Heeman, Peter A., 1995",
"ref_id": "BIBREF0"
},
{
"start": 196,
"end": 214,
"text": "), and Trains-1991",
"ref_id": null
},
{
"start": 215,
"end": 234,
"text": "(Gross et al., 1993",
"ref_id": "BIBREF4"
},
{
"start": 286,
"end": 309,
"text": "(Uryupina et al., 2020)",
"ref_id": "BIBREF26"
}
],
"ref_spans": [
{
"start": 353,
"end": 360,
"text": "Table 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Datasets",
"sec_num": "4.1"
},
{
"text": "We use Entity-F1 (Pradhan et al., 2012) as our metric for this experiment. We initialize our model, as outlined in Sec. 3.1 with a transformer based encoder with bert-base-uncased weights (Devlin et al., 2019) provided on the HuggingFace Model Hub (Wolf et al., 2020) , and freeze it before subsequent fine-tuning. We use a two layer network with its hidden dimension and dropouts specified below, and a ReLU activation in the feed forward layers indicated in Eqn. 4. During training, we set the batch size as 1, vary the hidden dimension of feed forward subnetworks between {256, 512}, and their dropout between {0.0, 0.3, 0.5}. We also experiment with multiple class weights for auxiliary supervision over the mention scorer's outputs, to compensate for the imbalance between anaphoric and non-anaphoric markables. We vary the inclusion of hand-crafted features (see \u03c6(x, y) in Eq. 4 and the description of hand crafted features in Sec. 3.1) as a part of the grid search.",
"cite_spans": [
{
"start": 248,
"end": 267,
"text": "(Wolf et al., 2020)",
"ref_id": "BIBREF28"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Setup",
"sec_num": "4.2"
},
{
"text": "This experiment also represents our submission to the shared task. Corresponding to each of the aforementioned datasets, we submit a separate model. The hyperparameters for these models are found by running a 5-fold cross validation based grid search where, in each fold, the models are trained on 80% of instances of the correspond-ing dataset, and 100% of train instances of the remaining datasets 3 , and is evaluated on the held out instances from the corresponding dataset. Once the hyperparameters are fixed corresponding to a dataset, we retrain the model from scratch on all training instances of all datasets for up to 20 epochs. The performance of the approach can be found in Table 2 .",
"cite_spans": [],
"ref_spans": [
{
"start": 687,
"end": 694,
"text": "Table 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Experimental Setup",
"sec_num": "4.2"
},
{
"text": "We find that our end-to-end approach performs suboptimally on all four datasets. Upon closer inspection, this performance is indicative of the challenge arisen by the amount of markables in a document. For instance, a document in Persuasion (F1: 16.28) contains only 134 markables on average, whereas a document in AMI (F1: 6.00) contains 1381 markables. This alludes to an inverse correlation between the average number of markables in document of a dataset and the entity F1 score on it. Also, the \"Avg. words between\" column in Table 1 indicates that anaphors and antecedents lie closer to each other in Persuasion when compared to other datasets. We hypothesise that this correlation, modeled as a part of hand-crafted features is actively exploited by our approach to increase a higher relative performance on the task.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "4.3"
},
{
"text": "Moreover, full bridging can be thought of as a combination of anaphor identification and bridging resolution. In order to ascertain whether our approach falters disproportionately on either of the two tasks, we perform a subsequent bridging resolution experiment over the train sets of these datasets (as they contain identified anaphors).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "4.3"
},
{
"text": "We make the changes to the model and training procedure as outlined in Sec. 3.2. We keep the Table 2 : Results, and settings of our submission to the shared task as detailed in Sec. 4.2. As mentioned, we use the Entity-F1 metric to report the performance. All train refers to the collection of all instances from the training set of the datasets mentioned in Sec. 4.1, and 5CV dev refer to the development subsets, as they occur in each fold of the aforementioned 5-fold cross-validation based grid search in Sec. 4.2. same hyperparameters (and grid search based hyperparameter search) as above, and train one model to make the predictions. However, unlike before, this experiment is performed in a 5-fold cross validation setup. Specifically, in each fold, we treat 20% of instances from each dataset as the test set. Another 10% are reserved for hyperparameter optimisation, and the remaining 70% of the instances are used for training the model. The performance reported in Table 3 is averaged over the five folds.",
"cite_spans": [],
"ref_spans": [
{
"start": 93,
"end": 100,
"text": "Table 2",
"ref_id": null
},
{
"start": 977,
"end": 984,
"text": "Table 3",
"ref_id": "TABREF3"
}
],
"eq_spans": [],
"section": "Bridging Resolution",
"sec_num": "4.4"
},
{
"text": "We also report the performance of two baselines, outlined below. Random: We select one of the markables at random which appears either in the first sentence of the document, or up to two sentences behind the anaphor. This sentence based strategy seemed to perform better than the one used in Poesio et al., 2004) .",
"cite_spans": [
{
"start": 292,
"end": 312,
"text": "Poesio et al., 2004)",
"ref_id": "BIBREF20"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Baselines",
"sec_num": "4.4.1"
},
{
"text": "Skip-Gram: We take the mean of pretrained Skip-Gram (Mikolov et al., 2013) embeddings of every token in a markable to create its vector representation. Then, using the cosine distance as a measure of anaphora, we select the antecedent which lies closest to the anaphor.",
"cite_spans": [
{
"start": 52,
"end": 74,
"text": "(Mikolov et al., 2013)",
"ref_id": "BIBREF17"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Baselines",
"sec_num": "4.4.1"
},
{
"text": "We find that while the task is far from solved, our approach is significantly better at bridging resolution, compared to full bridging. That is, our model is unable to perform anaphor identification with a reasonable accuracy leading to a much worse per-formance on the full bridging task. Interestingly, the performance gap between the random and skipgram baseline suggests that anaphor and antecedent markables often lie close in the vector spaces in a non-trivial amount of cases.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "4.4.2"
},
{
"text": "Across different datasets, we observe a similar trend as in the previous experiment. Our approach (as well as the skip-gram baseline) achieve its highest score on Persuasion, followed by Switchboard, Light and AMI. The performance gap between AMI and the next best approach is not as stark here (-9.3% here; -23% in Exp. 1). This can be explained by observing the random baseline. Its performance suggests that the sentences around the anaphor in AMI conversations have the least amount of markables to choose from, thereby making the task slightly easier.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "4.4.2"
},
{
"text": "In this work, we experiment with a higher-order coreference model based end-to-end approach for the full bridging task over conversational documents. We find that it is unable to model the task's complexities, however, its performance on bridging resolution is significantly better. This suggests a different approach to anaphor detection is needed, whether it be a stand alone anaphor detection model or a more guided adaptation of the higher-order coreference model (Joshi et al., 2019) that better suits the bridging task. We leave investigations along this line for the future. We also aim to experiment with approaches that can prime a model towards conversational documents, such as including the SpanBERT (Joshi et al., 2020) pretraining objective as a pre-finetuning step.",
"cite_spans": [
{
"start": 468,
"end": 488,
"text": "(Joshi et al., 2019)",
"ref_id": "BIBREF10"
},
{
"start": 712,
"end": 732,
"text": "(Joshi et al., 2020)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "5"
},
{
"text": "Task home pagehttps://competitions. codalab.org/competitions/30312",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "For the purposes of further analysis, we also consider Bridging Resolution with Gold Markables over provided train sets.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "Including Trains-91 and Trains-93 datasets",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Trains spoken dialog corpus",
"authors": [
{
"first": "James",
"middle": [],
"last": "Allen",
"suffix": ""
},
{
"first": "Peter",
"middle": [
"A"
],
"last": "Heeman",
"suffix": ""
}
],
"year": 1995,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"DOI": [
"10.35111/VDZW-3271"
]
},
"num": null,
"urls": [],
"raw_text": "Allen, James and Heeman, Peter A. 1995. Trains spo- ken dialog corpus.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Announcing the ami meeting corpus",
"authors": [
{
"first": "Jean",
"middle": [],
"last": "Carletta",
"suffix": ""
}
],
"year": 2006,
"venue": "The ELRA Newsletter",
"volume": "11",
"issue": "1",
"pages": "3--5",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jean Carletta. 2006. Announcing the ami meeting cor- pus. The ELRA Newsletter, 11(1):3-5.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "BERT: pre-training of deep bidirectional transformers for language understanding",
"authors": [
{
"first": "Jacob",
"middle": [],
"last": "Devlin",
"suffix": ""
},
{
"first": "Ming-Wei",
"middle": [],
"last": "Chang",
"suffix": ""
},
{
"first": "Kenton",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Kristina",
"middle": [],
"last": "Toutanova",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019",
"volume": "1",
"issue": "",
"pages": "4171--4186",
"other_ids": {
"DOI": [
"10.18653/v1/n19-1423"
]
},
"num": null,
"urls": [],
"raw_text": "Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: pre-training of deep bidirectional transformers for language under- standing. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Pa- pers), pages 4171-4186. Association for Computa- tional Linguistics.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "SWITCHBOARD: telephone speech corpus for research and development",
"authors": [
{
"first": "John",
"middle": [
"J"
],
"last": "Godfrey",
"suffix": ""
},
{
"first": "Edward",
"middle": [],
"last": "Holliman",
"suffix": ""
},
{
"first": "Jane",
"middle": [],
"last": "Mcdaniel",
"suffix": ""
}
],
"year": 1992,
"venue": "IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '92",
"volume": "",
"issue": "",
"pages": "517--520",
"other_ids": {
"DOI": [
"10.1109/ICASSP.1992.225858"
]
},
"num": null,
"urls": [],
"raw_text": "John J. Godfrey, Edward Holliman, and Jane McDaniel. 1992. SWITCHBOARD: telephone speech corpus for research and development. In 1992 IEEE Inter- national Conference on Acoustics, Speech, and Sig- nal Processing, ICASSP '92, San Francisco, Califor- nia, USA, March 23-26, 1992, pages 517-520. IEEE Computer Society.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "The trains 91 dialogues",
"authors": [
{
"first": "Derek",
"middle": [],
"last": "Gross",
"suffix": ""
},
{
"first": "James",
"middle": [],
"last": "Allen",
"suffix": ""
},
{
"first": "David",
"middle": [],
"last": "Traum",
"suffix": ""
}
],
"year": 1993,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Derek Gross, James Allen, and David Traum. 1993. The trains 91 dialogues.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "A deterministic algorithm for bridging anaphora resolution",
"authors": [
{
"first": "Yufang",
"middle": [],
"last": "Hou",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1938--1948",
"other_ids": {
"DOI": [
"10.18653/v1/d18-1219"
]
},
"num": null,
"urls": [],
"raw_text": "Yufang Hou. 2018a. A deterministic algorithm for bridging anaphora resolution. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 -November 4, 2018, pages 1938-1948. Associ- ation for Computational Linguistics.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Enhanced word representations for bridging anaphora resolution",
"authors": [
{
"first": "Yufang",
"middle": [],
"last": "Hou",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT",
"volume": "2",
"issue": "",
"pages": "1--7",
"other_ids": {
"DOI": [
"10.18653/v1/n18-2001"
]
},
"num": null,
"urls": [],
"raw_text": "Yufang Hou. 2018b. Enhanced word representations for bridging anaphora resolution. In Proceedings of the 2018 Conference of the North American Chap- ter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 2 (Short Papers), pages 1-7. Association for Computa- tional Linguistics.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Bridging anaphora resolution as question answering",
"authors": [],
"year": 2020,
"venue": "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
"volume": "2020",
"issue": "",
"pages": "1428--1438",
"other_ids": {
"DOI": [
"10.18653/v1/2020.acl-main.132"
]
},
"num": null,
"urls": [],
"raw_text": "Yufang Hou. 2020. Bridging anaphora resolution as question answering. In Proceedings of the 58th An- nual Meeting of the Association for Computational Linguistics, ACL 2020, Online, July 5-10, 2020, pages 1428-1438. Association for Computational Linguistics.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Global inference for bridging anaphora resolution",
"authors": [
{
"first": "Yufang",
"middle": [],
"last": "Hou",
"suffix": ""
},
{
"first": "Katja",
"middle": [],
"last": "Markert",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Strube",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "907--917",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yufang Hou, Katja Markert, and Michael Strube. 2013. Global inference for bridging anaphora resolution. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computa- tional Linguistics: Human Language Technologies, pages 907-917.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Spanbert: Improving pre-training by representing and predicting spans",
"authors": [
{
"first": "Mandar",
"middle": [],
"last": "Joshi",
"suffix": ""
},
{
"first": "Danqi",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Yinhan",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Daniel",
"middle": [
"S"
],
"last": "Weld",
"suffix": ""
},
{
"first": "Luke",
"middle": [],
"last": "Zettlemoyer",
"suffix": ""
},
{
"first": "Omer",
"middle": [],
"last": "Levy",
"suffix": ""
}
],
"year": 2020,
"venue": "Trans. Assoc. Comput. Linguistics",
"volume": "8",
"issue": "",
"pages": "64--77",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S. Weld, Luke Zettlemoyer, and Omer Levy. 2020. Spanbert: Improving pre-training by representing and predicting spans. Trans. Assoc. Comput. Lin- guistics, 8:64-77.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "BERT for coreference resolution: Baselines and analysis",
"authors": [
{
"first": "Mandar",
"middle": [],
"last": "Joshi",
"suffix": ""
},
{
"first": "Omer",
"middle": [],
"last": "Levy",
"suffix": ""
},
{
"first": "Luke",
"middle": [],
"last": "Zettlemoyer",
"suffix": ""
},
{
"first": "Daniel",
"middle": [
"S"
],
"last": "Weld",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing",
"volume": "",
"issue": "",
"pages": "5802--5807",
"other_ids": {
"DOI": [
"10.18653/v1/D19-1588"
]
},
"num": null,
"urls": [],
"raw_text": "Mandar Joshi, Omer Levy, Luke Zettlemoyer, and Daniel S. Weld. 2019. BERT for coreference reso- lution: Baselines and analysis. In Proceedings of the 2019 Conference on Empirical Methods in Nat- ural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, Novem- ber 3-7, 2019, pages 5802-5807. Association for Computational Linguistics.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "The codi-crac 2021 shared task on anaphora, bridging, and discourse deixis in dialogue",
"authors": [
{
"first": "Sopan",
"middle": [],
"last": "Khosla",
"suffix": ""
},
{
"first": "Juntao",
"middle": [],
"last": "Yu",
"suffix": ""
},
{
"first": "Ramesh",
"middle": [],
"last": "Manuvinakurike",
"suffix": ""
},
{
"first": "Vincent",
"middle": [],
"last": "Ng",
"suffix": ""
},
{
"first": "Massimo",
"middle": [],
"last": "Poesio",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Strube",
"suffix": ""
},
{
"first": "Carolyn",
"middle": [],
"last": "Ros\u00e9",
"suffix": ""
}
],
"year": 2021,
"venue": "Proceedings of the CODI-CRAC 2021",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sopan Khosla, Juntao Yu, Ramesh Manuvinakurike, Vincent Ng, Massimo Poesio, Michael Strube, and Carolyn Ros\u00e9. 2021. The codi-crac 2021 shared task on anaphora, bridging, and discourse deixis in dialogue. In Proceedings of the CODI-CRAC 2021",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Shared Task on Anaphora, Bridging, and Discourse Deixis in Dialogue, Association for Computational Linguistics.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Bridging resolution: A survey of the state of the art",
"authors": [
{
"first": "Hideo",
"middle": [],
"last": "Kobayashi",
"suffix": ""
},
{
"first": "Vincent",
"middle": [],
"last": "Ng",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 28th International Conference on Computational Linguistics",
"volume": "2020",
"issue": "",
"pages": "3708--3721",
"other_ids": {
"DOI": [
"10.18653/v1/2020.coling-main.331"
]
},
"num": null,
"urls": [],
"raw_text": "Hideo Kobayashi and Vincent Ng. 2020. Bridging res- olution: A survey of the state of the art. In Proceed- ings of the 28th International Conference on Com- putational Linguistics, COLING 2020, Barcelona, Spain (Online), December 8-13, 2020, pages 3708- 3721. International Committee on Computational Linguistics.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Leveraging different meronym discovery methods for bridging resolution in french",
"authors": [
{
"first": "Emmanuel",
"middle": [],
"last": "Lassalle",
"suffix": ""
},
{
"first": "Pascal",
"middle": [],
"last": "Denis",
"suffix": ""
}
],
"year": 2011,
"venue": "Anaphora Processing and Applications -8th Discourse Anaphora and Anaphor Resolution Colloquium",
"volume": "7099",
"issue": "",
"pages": "35--46",
"other_ids": {
"DOI": [
"10.1007/978-3-642-25917-3_4"
]
},
"num": null,
"urls": [],
"raw_text": "Emmanuel Lassalle and Pascal Denis. 2011. Leverag- ing different meronym discovery methods for bridg- ing resolution in french. In Anaphora Process- ing and Applications -8th Discourse Anaphora and Anaphor Resolution Colloquium, DAARC 2011, Faro, Portugal, October 6-7, 2011. Revised Selected Papers, volume 7099 of Lecture Notes in Computer Science, pages 35-46. Springer.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Higher-order coreference resolution with coarse-tofine inference",
"authors": [
{
"first": "Kenton",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Luheng",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Luke",
"middle": [],
"last": "Zettlemoyer",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "2",
"issue": "",
"pages": "687--692",
"other_ids": {
"DOI": [
"10.18653/v1/N18-2108"
]
},
"num": null,
"urls": [],
"raw_text": "Kenton Lee, Luheng He, and Luke Zettlemoyer. 2018. Higher-order coreference resolution with coarse-to- fine inference. In Proceedings of the 2018 Confer- ence of the North American Chapter of the Associ- ation for Computational Linguistics: Human Lan- guage Technologies, Volume 2 (Short Papers), pages 687-692, New Orleans, Louisiana. Association for Computational Linguistics.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Collective classification for fine-grained information status",
"authors": [
{
"first": "Katja",
"middle": [],
"last": "Markert",
"suffix": ""
},
{
"first": "Yufang",
"middle": [],
"last": "Hou",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Strube",
"suffix": ""
}
],
"year": 2012,
"venue": "The 50th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",
"volume": "1",
"issue": "",
"pages": "795--804",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Katja Markert, Yufang Hou, and Michael Strube. 2012. Collective classification for fine-grained information status. In The 50th Annual Meeting of the Associa- tion for Computational Linguistics, Proceedings of the Conference, July 8-14, 2012, Jeju Island, Korea -Volume 1: Long Papers, pages 795-804. The Asso- ciation for Computer Linguistics.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Efficient estimation of word representations in vector space",
"authors": [
{
"first": "Tom\u00e1s",
"middle": [],
"last": "Mikolov",
"suffix": ""
},
{
"first": "Kai",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Greg",
"middle": [],
"last": "Corrado",
"suffix": ""
},
{
"first": "Jeffrey",
"middle": [],
"last": "Dean",
"suffix": ""
}
],
"year": 2013,
"venue": "1st International Conference on Learning Representations",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Tom\u00e1s Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word represen- tations in vector space. In 1st International Con- ference on Learning Representations, ICLR 2013, Scottsdale, Arizona, USA, May 2-4, 2013, Workshop Track Proceedings.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "The mate meta-scheme for coreference in dialogues in multiple languages",
"authors": [
{
"first": "Massimo",
"middle": [],
"last": "Poesio",
"suffix": ""
},
{
"first": "Florence",
"middle": [],
"last": "Bruneseaux",
"suffix": ""
},
{
"first": "Laurent",
"middle": [],
"last": "Romary",
"suffix": ""
}
],
"year": 1999,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Massimo Poesio, Florence Bruneseaux, and Laurent Romary. 1999. The mate meta-scheme for corefer- ence in dialogues in multiple languages. In ACL'99",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Workshop Towards Standards and Tools for Discourse Tagging",
"authors": [],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "65--74",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Workshop Towards Standards and Tools for Dis- course Tagging, pages 65-74.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Learning to resolve bridging references",
"authors": [
{
"first": "Massimo",
"middle": [],
"last": "Poesio",
"suffix": ""
},
{
"first": "Rahul",
"middle": [],
"last": "Mehta",
"suffix": ""
},
{
"first": "Axel",
"middle": [],
"last": "Maroudas",
"suffix": ""
},
{
"first": "Janet",
"middle": [],
"last": "Hitzeman",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "143--150",
"other_ids": {
"DOI": [
"10.3115/1218955.1218974"
]
},
"num": null,
"urls": [],
"raw_text": "Massimo Poesio, Rahul Mehta, Axel Maroudas, and Janet Hitzeman. 2004. Learning to resolve bridging references. In Proceedings of the 42nd Annual Meet- ing of the Association for Computational Linguistics, 21-26 July, 2004, Barcelona, Spain, pages 143-150. ACL.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning -Proceedings of the Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes",
"authors": [
{
"first": "Alessandro",
"middle": [],
"last": "Sameer Pradhan",
"suffix": ""
},
{
"first": "Nianwen",
"middle": [],
"last": "Moschitti",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Xue",
"suffix": ""
}
],
"year": 2012,
"venue": "EMNLP-CoNLL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sameer Pradhan, Alessandro Moschitti, and Nianwen Xue, editors. 2012. Joint Conference on Empiri- cal Methods in Natural Language Processing and Computational Natural Language Learning -Pro- ceedings of the Shared Task: Modeling Multilingual Unrestricted Coreference in OntoNotes, EMNLP- CoNLL 2012, July 13, 2012, Jeju Island, Korea. ACL.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "BASHI: A corpus of wall street journal articles annotated with bridging links",
"authors": [
{
"first": "Ina",
"middle": [],
"last": "R\u00f6siger",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the Eleventh International Conference on Language Resources and Evaluation, LREC 2018",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ina R\u00f6siger. 2018. BASHI: A corpus of wall street journal articles annotated with bridging links. In Proceedings of the Eleventh International Confer- ence on Language Resources and Evaluation, LREC 2018, Miyazaki, Japan, May 7-12, 2018. European Language Resources Association (ELRA).",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Bridging resolution: Task definition, corpus resources and rule-based experiments",
"authors": [
{
"first": "Ina",
"middle": [],
"last": "R\u00f6siger",
"suffix": ""
},
{
"first": "Arndt",
"middle": [],
"last": "Riester",
"suffix": ""
},
{
"first": "Jonas",
"middle": [],
"last": "Kuhn",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 27th International Conference on Computational Linguistics",
"volume": "",
"issue": "",
"pages": "3516--3528",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ina R\u00f6siger, Arndt Riester, and Jonas Kuhn. 2018. Bridging resolution: Task definition, corpus re- sources and rule-based experiments. In Proceedings of the 27th International Conference on Computa- tional Linguistics, COLING 2018, Santa Fe, New Mexico, USA, August 20-26, 2018, pages 3516- 3528. Association for Computational Linguistics.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Corpus linguistics at work",
"authors": [
{
"first": "Elena",
"middle": [],
"last": "Tognini-Bonelli",
"suffix": ""
}
],
"year": 2001,
"venue": "",
"volume": "6",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Elena Tognini-Bonelli. 2001. Corpus linguistics at work, volume 6. John Benjamins Publishing.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Learning to speak and act in a fantasy text adventure game",
"authors": [
{
"first": "Jack",
"middle": [],
"last": "Urbanek",
"suffix": ""
},
{
"first": "Angela",
"middle": [],
"last": "Fan",
"suffix": ""
},
{
"first": "Siddharth",
"middle": [],
"last": "Karamcheti",
"suffix": ""
},
{
"first": "Saachi",
"middle": [],
"last": "Jain",
"suffix": ""
},
{
"first": "Samuel",
"middle": [],
"last": "Humeau",
"suffix": ""
},
{
"first": "Emily",
"middle": [],
"last": "Dinan",
"suffix": ""
},
{
"first": "Tim",
"middle": [],
"last": "Rockt\u00e4schel",
"suffix": ""
},
{
"first": "Douwe",
"middle": [],
"last": "Kiela",
"suffix": ""
},
{
"first": "Arthur",
"middle": [],
"last": "Szlam",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing",
"volume": "",
"issue": "",
"pages": "673--683",
"other_ids": {
"DOI": [
"10.18653/v1/D19-1062"
]
},
"num": null,
"urls": [],
"raw_text": "Jack Urbanek, Angela Fan, Siddharth Karamcheti, Saachi Jain, Samuel Humeau, Emily Dinan, Tim Rockt\u00e4schel, Douwe Kiela, Arthur Szlam, and Ja- son Weston. 2019. Learning to speak and act in a fantasy text adventure game. In Proceedings of the 2019 Conference on Empirical Methods in Nat- ural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, Novem- ber 3-7, 2019, pages 673-683. Association for Com- putational Linguistics.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "Annotating a broad range of anaphoric phenomena, in a variety of genres: the ARRAU corpus",
"authors": [
{
"first": "Olga",
"middle": [],
"last": "Uryupina",
"suffix": ""
},
{
"first": "Ron",
"middle": [],
"last": "Artstein",
"suffix": ""
},
{
"first": "Antonella",
"middle": [],
"last": "Bristot",
"suffix": ""
},
{
"first": "Federica",
"middle": [],
"last": "Cavicchio",
"suffix": ""
},
{
"first": "Francesca",
"middle": [],
"last": "Delogu",
"suffix": ""
},
{
"first": "Kepa Joseba",
"middle": [],
"last": "Rodr\u00edguez",
"suffix": ""
},
{
"first": "Massimo",
"middle": [],
"last": "Poesio",
"suffix": ""
}
],
"year": 2020,
"venue": "Nat. Lang. Eng",
"volume": "26",
"issue": "1",
"pages": "95--128",
"other_ids": {
"DOI": [
"10.1017/S1351324919000056"
]
},
"num": null,
"urls": [],
"raw_text": "Olga Uryupina, Ron Artstein, Antonella Bristot, Feder- ica Cavicchio, Francesca Delogu, Kepa Joseba Ro- dr\u00edguez, and Massimo Poesio. 2020. Annotating a broad range of anaphoric phenomena, in a variety of genres: the ARRAU corpus. Nat. Lang. Eng., 26(1):95-128.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Persuasion for good: Towards a personalized persuasive dialogue system for social good",
"authors": [
{
"first": "Xuewei",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Weiyan",
"middle": [],
"last": "Shi",
"suffix": ""
},
{
"first": "Richard",
"middle": [],
"last": "Kim",
"suffix": ""
},
{
"first": "Yoojung",
"middle": [],
"last": "Oh",
"suffix": ""
},
{
"first": "Sijia",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Jingwen",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Zhou",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019",
"volume": "1",
"issue": "",
"pages": "5635--5649",
"other_ids": {
"DOI": [
"10.18653/v1/p19-1566"
]
},
"num": null,
"urls": [],
"raw_text": "Xuewei Wang, Weiyan Shi, Richard Kim, Yoojung Oh, Sijia Yang, Jingwen Zhang, and Zhou Yu. 2019. Per- suasion for good: Towards a personalized persuasive dialogue system for social good. In Proceedings of the 57th Conference of the Association for Compu- tational Linguistics, ACL 2019, Florence, Italy, July 28-August 2, 2019, Volume 1: Long Papers, pages 5635-5649. Association for Computational Linguis- tics.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "Transformers: State-of-the-art natural language processing",
"authors": [
{
"first": "Thomas",
"middle": [],
"last": "Wolf",
"suffix": ""
},
{
"first": "Lysandre",
"middle": [],
"last": "Debut",
"suffix": ""
},
{
"first": "Victor",
"middle": [],
"last": "Sanh",
"suffix": ""
},
{
"first": "Julien",
"middle": [],
"last": "Chaumond",
"suffix": ""
},
{
"first": "Clement",
"middle": [],
"last": "Delangue",
"suffix": ""
},
{
"first": "Anthony",
"middle": [],
"last": "Moi",
"suffix": ""
},
{
"first": "Pierric",
"middle": [],
"last": "Cistac",
"suffix": ""
},
{
"first": "Tim",
"middle": [],
"last": "Rault",
"suffix": ""
},
{
"first": "R\u00e9mi",
"middle": [],
"last": "Louf",
"suffix": ""
},
{
"first": "Morgan",
"middle": [],
"last": "Funtowicz",
"suffix": ""
},
{
"first": "Joe",
"middle": [],
"last": "Davison",
"suffix": ""
},
{
"first": "Sam",
"middle": [],
"last": "Shleifer",
"suffix": ""
},
{
"first": "Clara",
"middle": [],
"last": "Patrick Von Platen",
"suffix": ""
},
{
"first": "Yacine",
"middle": [],
"last": "Ma",
"suffix": ""
},
{
"first": "Julien",
"middle": [],
"last": "Jernite",
"suffix": ""
},
{
"first": "Canwen",
"middle": [],
"last": "Plu",
"suffix": ""
},
{
"first": "Teven",
"middle": [
"Le"
],
"last": "Xu",
"suffix": ""
},
{
"first": "Sylvain",
"middle": [],
"last": "Scao",
"suffix": ""
},
{
"first": "Mariama",
"middle": [],
"last": "Gugger",
"suffix": ""
},
{
"first": "Quentin",
"middle": [],
"last": "Drame",
"suffix": ""
},
{
"first": "Alexander",
"middle": [
"M"
],
"last": "Lhoest",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Rush",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
"volume": "",
"issue": "",
"pages": "38--45",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pier- ric Cistac, Tim Rault, R\u00e9mi Louf, Morgan Funtow- icz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush. 2020. Transformers: State-of-the-art natural language pro- cessing. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 38-45, Online. Asso- ciation for Computational Linguistics.",
"links": null
}
},
"ref_entries": {
"TABREF3": {
"html": null,
"num": null,
"content": "<table/>",
"type_str": "table",
"text": "Results of the experiment outlined in Sec. 4.4."
}
}
}
} |