| { | |
| "paper_id": "2021", | |
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| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T13:27:39.538654Z" | |
| }, | |
| "title": "Tracing variation in discourse connectives in translation and interpreting through neural semantic spaces", | |
| "authors": [ | |
| { | |
| "first": "Ekaterina", | |
| "middle": [], | |
| "last": "Lapshinova-Koltunski", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Saarland University", | |
| "location": { | |
| "addrLine": "University Campus A.2.2", | |
| "postCode": "DE-66123", | |
| "settlement": "Saarbr\u00fccken" | |
| } | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Heike", | |
| "middle": [], | |
| "last": "Przybyl", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "Saarland University", | |
| "location": { | |
| "addrLine": "University Campus A.2.2", | |
| "postCode": "DE-66123", | |
| "settlement": "Saarbr\u00fccken" | |
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| }, | |
| "email": "[email protected]" | |
| }, | |
| { | |
| "first": "Yuri", | |
| "middle": [], | |
| "last": "Bizzoni", | |
| "suffix": "", | |
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| "institution": "Saarland University", | |
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| "settlement": "Saarbr\u00fccken" | |
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| "abstract": "In the present paper, we explore lexical contexts of discourse markers in translation and interpreting on the basis of word embeddings. Our special interest is on contextual variation of the same discourse markers in (written) translation vs. (simultaneous) interpreting. To explore this variation at the lexical level, we use a data-driven approach: we compare bilingual neural word embeddings trained on source-to-translation and source-tointerpreting aligned corpora. Our results show more variation of semantically related items in translation spaces vs. interpreting ones and a more consistent use of fewer connectives in interpreting. We also observe different trends with regard to the discourse relation types.", | |
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| "abstract": [ | |
| { | |
| "text": "In the present paper, we explore lexical contexts of discourse markers in translation and interpreting on the basis of word embeddings. Our special interest is on contextual variation of the same discourse markers in (written) translation vs. (simultaneous) interpreting. To explore this variation at the lexical level, we use a data-driven approach: we compare bilingual neural word embeddings trained on source-to-translation and source-tointerpreting aligned corpora. Our results show more variation of semantically related items in translation spaces vs. interpreting ones and a more consistent use of fewer connectives in interpreting. We also observe different trends with regard to the discourse relation types.", | |
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| "section": "Abstract", | |
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| "text": "This paper presents an explorative study of discourse connectives in cross-linguistically mediated communication. We compare written translation with spoken simultaneous interpreting in the domain of European Parliament discourse. We start from the perspective of translationese, an observation that translations share specific linguistic features distinguishing them from non-translated language. Although interpreting has recently received increased attention in computational approaches, there are not so many studies of interpretese.", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "In this work, we are aiming to understand the differences between translated and interpreted texts in terms of discourse connectives. Przybyl et al. (forthcoming) show that translations and interpreting transcripts differ significantly in the use of these linguistic units. For instance, there is a difference in the preference for the choice of connectives triggering the same relation. Specifically, the relation of contrast/concession is preferably expressed with the connective however in translation, whereas the use of but is characteristic of interpreting. This indicates that in marking logical relations, interpreters tend to prefer more general items over more specific ones. This is in line with the existing observations about differences between speech and writing (Crible and Cuenca, 2017) : there are fewer but more polyfunctional discourse markers in speech than in writing.", | |
| "cite_spans": [ | |
| { | |
| "start": 134, | |
| "end": 162, | |
| "text": "Przybyl et al. (forthcoming)", | |
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| "start": 778, | |
| "end": 803, | |
| "text": "(Crible and Cuenca, 2017)", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "To explore the differences between translated and interpreted texts, we follow the data-driven approach as in and use neural word embeddings (Word2Vec) to compare the bilingual semantic spaces from bilingual word embeddings built on aligned corpora. The resulting semantic spaces model the lexical choices of a specific translation. We train two bilingual distributional models on two comparable, aligned corpora (translation and interpreting) and compare the resulting semantic spaces to detect differences in the lexical patterns of discourse connectives impacted by translation mode (written vs. spoken). As stated by the existing constraints of interpreting (high cognitive load, time pressure) have impact on lexical choices, which is reflected in interpreting if compared to translation.", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "Translated texts share linguistic characteristics which distinguish them from non-translated textsthe phenomenon of translationese (see Gellerstam, 1986; Baker, 1993; Toury, 1995) . These differences can be traced in the distribution of various language patterns, i.e. linguistic features mostly organised in terms of more abstract categories (sometimes called translation universals or translation features) such as normalisation and shining-through (Teich, 2003) , simplification (Toury, 1995) , convergence (Laviosa, 2002) and explicitation (Olohan and Baker, 2000) . The latter is often related to discourse connectives. For instance, Gumul (2006, 184) stated that explicitation in interpreting is related to adding discourse markers among other means of cohesive explicitness. At the same time, Shlesinger (1995) observed a reduction of cohe-sive ties in interpreting if compared to the source language input (implicitation). Kajzer-Wietrzny (2012) showed that there are differences between translation and interpreting in the usage of linking adverbials (with translation being more explicit).", | |
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| "text": "The phenomena of explicitation and implicitation may also depend on the type of relations discourse connectives trigger: cognitively simple relations are more often left implicit than relations that are cognitively more complex (see Hoek et al., 2017) . This is also confirmed in a recent study by Blumenthal-Dram\u00e9 (2021) who showed that causal links are more expected than concessive ones and the processing of concessive sentences benefits more from the explicit marking than the processing of causal sentences (also pointing to cross-lingual differences between English and German).", | |
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| "text": "Although we are not pursuing creation of a multilingual lexicon, our work is related to those dealing with mapping of discourse connectives (Stede et al., 2019; Bourgonje et al., 2018 Bourgonje et al., , 2017 Kosseim, 2017, 2014) . Numerous studies analysed discourse connectives from a cross-lingual point of view using aligned texts (see Hoek and Zufferey, 2015; Zufferey and Cartoni, 2014; Meyer and Webber, 2013; . In our exploratory study of the differences between translation and interpreting, we do not focus on the direct transfer of specific discourse connectives, but look into their bilingual lexical context. In this sense, our work relates to the study by Roth and Upadhyay (2019) , who used cross-lingual embeddings and discourse connectives to analyse semantically related words in several languages.", | |
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| "text": "We rely on the methodology proposed by Bizzoni and Teich (2019), which is related to other studies that attempt to use word embeddings for linguistic analysis, such as Dubossarsky et al. 2017; Fankhauser and Kupietz (2017); . Whereas analyse general variation in lexical choices in interpreting and translation, our focus is on discourse-related phenomena. Besides that, our work is related to those with a focus on domain-specific word embeddings (Zhang et al., 2019; Wang et al., 2018) , those dealing with multilingual word embeddings for lexicon induction and mapping (Shi et al., 2019; Zhang et al., 2019; Artetxe et al., 2017 Artetxe et al., , 2018 and those focusing on creating consistent spaces (Huang et al., 2018) in cross-lingual word analogy tasks (Ul\u010dar et al., 2020; Brychc\u00edn et al., 2019) .", | |
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| "text": "Our data includes officially published original speeches, as well as transcripts of the speeches delivered at the European Parliament (EP) aligned with their translations or interpretations, correspondingly. Both parallel subsets are parts of the Europarl-UdS (Karakanta et al., 2018) and EPIC-UdS (Przybyl et al., forthcoming) corpora, with English as source and German as target. Both corpora are strictly comparable in terms of register, as they contain European Parliament speeches. The spoken part (EPIC-UdS) are true transcripts of the spoken utterances by members of the EP and interpeters, including spoken language features such as false starts, filled pauses and unfinished sentences. The basis of written dataset (Europarl-UdS) is also a spoken event in the European Parliament, however modified to respect written language characteristics. Overall, we have 130,000 sentence pairs in the translated data and 3,397 sentence pairs for the interpreted data. Refer to Table 1 This paper uses the approach described by Bizzoni and Teich (2019): neural word embeddings (Word2Vec) are used to compare sourceto-translation and source-to-interpreting lexical choices. The sentence aligned corpus data is used to create reshuffled bilingual pseudo-sentences. Subsequently, a standard skipgram Word2Vec model is trained on these sentences to create translation and interpreting spaces. The main idea of this method is that words with a consistent translation in an aligned corpus will share similar contexts with their translation, and will result in close proximity in distributional spaces. This is a mechanism similar to that used in standard distributional semantics, where words having similar contexts are closer in space, but it is applied to bilingual aligned contexts instead of standard monolingual texts.", | |
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| "section": "Data and Methods", | |
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| "text": "For example, if a source word A is always translated with a target word B, and B is only used to translate A, such words will appear only together and their bilingual context will be identical. As an effect, their distributional vectors will be extremely similar and the two words will be very close neighbours in a semantic space. The more a translation deviates from this type of systematicity, the less two words will result close in the semantic space.", | |
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| "text": "For the words without consistent translation, different configurations in translation space exist. If a word is ambiguous, it can be close to the variants of its translations in the space, but the similarity would be lower (reflected in the cosine similarity score). If a word is very hard or even impossible to translate with one term, there will be no translations close to the word, which does not show a high similarity to its neighbours. While this method, being based on Word2Vec, is sensitive to frequency effects, show that it is robust enough to obtain meaningful results from corpora few thousands sentences long (see , for more details and examples).", | |
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| "text": "Our list of connectives is restricted to 14 items that appear to be most frequent in both written and spoken data at hand. 1 The connectives can be grouped according to their senses defined in PDTB-3 (Webber et al., 2019, p. 17) as temporal (finally, first, firstly, secondly), contingency (as, because, if, so, why), comparison (but, however, yet) and expansion (also, that). Some of them are ambiguous and may trigger different relations, e.g. the connective as is ambiguous and may express all the four meaning relations, although preference is given for temporal and contingency (Webber et al., 2019, p. 56) . The connective finally is ambiguous between temporal meaning and the meaning of expansion, whereas the connective if is conditional, but may also express comparison.", | |
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| "text": "(Webber et al., 2019, p. 17)", | |
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| "text": "The resulting semantic spaces of connectives are analyse in the following way: 10 nearest neighbours of the 14 discourse connectives are analysed for semantic relation to the connective -if an item expresses the same discourse function, i.e. source or target language synonym (e.g. however -butzwar), can be used for paraphrasing the function (e.g. why -for this reason, finally -to sum up) or items that express other logical relations via discourse connective (e.g. that -ob), we consider them as semantically related.", | |
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| "text": "We have a number of assumptions about the resulting semantic spaces. First of all, following the findings by , we expect that the connectives translated consistently with the same target language equivalent will be very close to each other in the resulting semantic space. Besides that, we know that interpreters tend to use fewer connectives than translators. So we expect less variation in interpreting than translation, and consequently, fewer but closer semantic neighbours in interpreting than in translation. At the same time, as there should be more implicitation in interpreting than in translation, we expect more target language equivalents in the semantic spaces of translation than interpreting. Furthermore, we expect variation in the resulting semantic spaces with regard to the discourse relation a connective expresses.", | |
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| "section": "Data and Methods", | |
| "sec_num": "3" | |
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| "text": "We qualitatively analyse the differences between resulting translation and interpreting spaces (TR and SI spaces in the following). For the sake of space, we will report on the most remarkable cases only. The whole list of spaces for both translation and interpreting is given in the Appendix.", | |
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| "section": "Analysis and Observations", | |
| "sec_num": "4" | |
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| "text": "Overall observations The TR spaces display far greater semantic proximity with synonyms or other semantically related items than the SI spaces. The interpreting space for 5 out of 14 discourse connectives under analysis (also, but, however, so, yet) shows no related item within the 10 nearest neighbours. The maximum amount of semantically related items in the list of 10 nearest neighbours in the interpreting space is 3, whereas up to 10 out of 10 nearest neighbours in the translation space are semantically linked to the connective studied.", | |
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| "section": "Analysis and Observations", | |
| "sec_num": "4" | |
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| { | |
| "text": "Content of TR and SI spaces Translation spaces of some connectives (but, however, yet, finally) display many near synonyms in English or equivalents in German, however linked with lower cosine similarity score. By contrast, their interpreting spaces contain either few or no semantically related items. One of such examples is the discourse connective but, that we illustrate in Table 2 .", | |
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| "text": "Table 2", | |
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| "sec_num": "4" | |
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| "text": "Notably, this discourse connective is frequent in both translated and interpreted texts of our data, being even more frequent in interpreting than in translation if compared to the other two discourse connectives of comparison under analysis (however and yet). However, the SI space does not contain any semantically related English words or German equivalents, while the TR space does so (e.g. the synonym however or the translation equivalent zwar). We assume that no matching equivalents within the nearest neighbours in interpreting confirms the general implicitation trend as discussed in the literature about interpreting: due to the time pressure and high cognitive load, interpreters tend to omit discourse connectives used in the source.", | |
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| "section": "Analysis and Observations", | |
| "sec_num": "4" | |
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| "text": "Scores in TR and SI spaces In general, very few semantically related items occur in the interpreting space. However, if there is a synonym or translation equivalent in the SI cluster, cosine similarity is generally higher in the interpreting space than for equivalent items in the translation space, as seen in Table 3 This can be explained by the general tendency of interpreting to frequently use a smaller repository of discourse connectives. This reduced variation in interpreting leads to stronger clusters within the semantic space. For instance, if is consistently interpreted with the target language equivalent wenn and is frequently used in proximity to dann. Cosine similarity scores for wenn in the SI space with .87 is higher than the highest nearest neighbour cosine similarity score in the TR space (see Table 3 ). If interpreters add connectives in the German target and the English sources do not contain their triggers (see Defrancq, Bart, 2016) , the systematicity of the translation is reduced and the connectives' similarity to their triggers is lower; thus, they might not appear at all in the semantic clusters we analyse.", | |
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| "text": "Bart, 2016)", | |
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| "text": "Table 3", | |
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| "text": "Table 3", | |
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| "text": "Connective ambiguity Connectives so and that seem not to cluster well with semantically related words in either translation or interpreting space. This can be explained by their multiple functions: they not only serve as discourse connective, but also as other discourse elements, e.g. that can be used as demonstrative reference, whereas so can express clausal substitution or also be used as a modifying adverb or an intensifier. With the word embedding approach applied on the raw data (not annotated for true discourse connectives), we cannot distinguish between these functions.", | |
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| { | |
| "text": "Observations on discourse senses We also observe variation in the patterns for various types of discourse relations. For instance, connectives triggering the relation of comparison (but, however and yet), temporal connectives (finally, first, firstly and secondly) and the conditional if have almost always equivalents in the translation spaces. This confirms the dependency of the implicitation/explicitation process on the type of relation, as shown by Hoek et al. (2017) and Blumenthal-Dram\u00e9 (2021), see Section 2 above. Cognitively more complex relations (concession, which is grouped within comparison and condition) cannot be easily left out, and have therefore almost always equivalents in translation spaces. At the same time, cognitively simple relations (expansion and contingency) do not necessarily do so. However, this observation is true for translation only and does not apply for the interpreting data. An exception is the conditional if. It clusters with its equivalents in both translation and interpreting space, however showing semantically related connectives in English in the translation space only (when, unless), see Table 3 and the whole spaces in the Appendix.", | |
| "cite_spans": [ | |
| { | |
| "start": 455, | |
| "end": 473, | |
| "text": "Hoek et al. (2017)", | |
| "ref_id": null | |
| } | |
| ], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Analysis and Observations", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "We used neural semantic spaces to observe differences between discourse connectives in translation and interpreting. Generally, we observe similar trends reported by for the lexical differences between interpreting and translation. Our results are in line with implication and explicitation trends in translation. They confirm the assumption that interpreting shows more implicitation than translation. It was also interesting to see that cognitive complexity of relations also has impact on the resulting semantic spaces in translation, but has a different effect on the interpreting spaces. In future, we plan to further investigate these differences using a wider range of connectives expressing different discourse relations. We would also like to systematically compare our results with the original aligned corpus, to provide a convincing qualitative test of the trends we have observed through our semantic spaces.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion and Future Work", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "Also, Word2Vec represents one of the most efficient methods to produce a word's compact distributional profile, but it is not the only one. It could be interesting to compare the results of Word2Vec with other state of the art, non-contextualized 2 word embeddings when applied to the same corpus.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion and Future Work", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "Moreover, our results also confirm the tendency of interpreting to show less variation in terms of the range of discourse connectives. Here, we would like to extend our work and include more discourse connectives. Finally, we plan to experiment with disambiguated data (include the cases of discourse relations only). ", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion and Future Work", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "We excluded some of the frequent connectives due to their high ambiguity(and, or). We also considered two discourse particles well and now, but excluded them from the current analysis, as they are more typical of spoken language, i.e. interpreting.", | |
| "cite_spans": [], | |
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| "section": "", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [ | |
| { | |
| "text": "This work is based on research funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -SFB 1102 / Project-ID 232722074, project B7 -Modelling Human Translation with a Noisy Channel.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Acknowledgement", | |
| "sec_num": null | |
| } | |
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| "content": "<table><tr><td>written</td><td colspan=\"2\">token spoken token</td></tr><tr><td>WR</td><td>9,654,581 SP</td><td>66,226</td></tr><tr><td>TR</td><td>8,954,825 SI</td><td>57,622</td></tr></table>", | |
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| "text": "Translation and interpreting spaces for but and 10 nearest neighbours with cosine similarity; semantically unrelated items in brackets.", | |
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| "text": "for the connectives if, as and secondly.", | |
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| "text": "Defrancq, Bart. 2016. Well, interpreters. . . a corpusbased study of a pragmatic particle used by simultaneous interpreters. In Corpas Pastor, Gloria and Seghiri Dominguez, Miriam, editor, Corpus-based approaches to translation and interpreting : from theory to applications, volume 106 of Studien zur romanischen Sprachwissenschaft und interkulturellen Kommunikation, pages 105-128. Peter Lang. ; nevertheless .73; yet .71; whilst .68; while .67; though .55; nonetheless .54; zwar .50; although .49; (coin .45) (zweitausendzw\u00f6lf .77); (informationssystem .77); (spiele .75); (achtunddrei\u00dfig .74), (uk .73); (abtreibungsrecht", | |
| "content": "<table><tr><td colspan=\"2\">A Appendix continued from previous page</td><td/></tr><tr><td colspan=\"3\">Haim Dubossarsky, Daphna Weinshall, and Eitan Grossman. 2017. Outta control: Laws of semantic change and inherent biases in word representation models. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1136-1145, Copenhagen, Denmark. Associa-tion for Computational Linguistics. Peter Fankhauser and Marc Kupietz. 2017. Visual-izinglanguage change in a corpus of contemporary German. In Proceedings of the 9th International Corpus Linguistics Conference, University of Birm-ingham. Martin Gellerstam. 1986. Translationese in Swedish novels translated from English. In L. Wollin and H. Lindquist, editors, Translation Studies in Scan-dinavia, pages 88-95. CWK Gleerup, Lund. Ewa Gumul. 2006. Explicitation in simultaneous in-TR space TR space temporal finally abschlie\u00dfend .67; conclusion .59; lastly .55; (reiterate .53); schluss .5; (be-merken .49); abschluss .45; schlie\u00dflich .42; conclude .42; (hervorheben .41) however but .86.73); (paragraph .73); (characters .73); SI space SI space sum .71; (unterbrechen .7); (lehrt .69); (gollnisch .69); (karte .68); (teach .68); (fish .66); (gollnish .66); (subject .65); (zauberstab .72); (lewis .72) (heben .65) first zweiten .61; second .58; firstly .57; er-sten .51; zweite .49; mal .48; erstmals .46; zun\u00e4chst .45; erste .44; (reading .43) zun\u00e4chst .76; yet however .71; but .66; nevertheless (destroy .64); (wussten .64); (pro-(verbesserte .74); .6; while .55; whilst .51; though .45; duzierte .63); (pork .63); (kontrol-(franz\u00f6sichen .74); (november .73); zwar .44; although .44, nonetheless .41; lierte .62); (zwang .61); (vernichten ersten .73; (mandats .72); erste .72; (stretches .39) .61); (industrial .61); (throw .61); (wis-(wahl .71); (ehre .71); (vortragen .7) firstly erstens .75; secondly .72; zweitens .66; thirdly .6; first .57; drittens .56; (pfeiler .51); zun\u00e4chst 0,50; (pillar .50); second .48 senschaftler .60) (achtzehn .87); endlich .83; (eigh-expansion teen .83); (ausreichende .82); (gefallen also furthermore .72; addition .66; similarly (sicherheit .54); (ma\u00dfnahmen .51); .82); (umweltausschusses .81); (gordon .64; equally .6; (indeed .57); moreover (denk .51); (therapeuten .49); (gesund-.8); (mitkollaborateure .8); (erfasst .8); .57; sowohl .56; including .53; likewise heitswesen .49); (repeal .49); (secu-(mcavan 0,8) secondly zweitens .76; erstens .75; firstly .72; drittens .68; thirdly .67; fourthly .55; second .55; zweite .45; (pillar .44); viertens .44 .48; too .46 rity .48); (fidschi .48); (interessen .48); zweitens .82; (procurement .77); (smes (hum .48) .74); (calculations .72); (roughly .72); that which .37; what .32; (tatsache .29); (verordnung .55); (daran .54); (fair .52); (berechnungen .7); (fernen .7); (billio-(assertion .29); (firmly .28); whatever (ver\u00e4nderung .52); (study .52); (trans-nen .7); (f\u00fcnfhundertachtzig .7); (edf .28; (richtig .27); (this .27); (klar .26); parent .51); (incidentally .51); (offen-.7) contingency (sicherzustellen .26) heit .51); (sp\u00fcren .51); (ob .5)</td></tr><tr><td colspan=\"2\">terpreting: A strategy or a by-product of language as (angesehen .53); (erstes .52); (bezeich-</td><td>wie .57; (beginnt .57); (begins .56);</td></tr><tr><td colspan=\"2\">mediation? Across Languages and Cultures. A Mul-nen .52); (bezeichnet .51); (betrachtet tidisciplinary Journal for Translation and Interpret-.48); (than .48); (how .47); (erweisen ing Studies, 7:171-190. .45); (gut .45); (insofern .45)</td><td>(wirklichen .56); (arbeitsdokument .55); european .53); presently .53; (getreten .52); (far .52); (worse .52)</td></tr><tr><td colspan=\"2\">Jet Hoek and Sandrine Zufferey. 2015. Factors influ-because since .38; (geschweige .37); (owing encing the implicitation of discourse relations across .28); as .24; (moreira .24); (louth .24); languages. In Proceedings of the 11th Joint ACL-ISO Workshop on Interoperable Semantic Annota-(kleinlich .23); (timed .23); (improper</td><td>(dumping .62); (r\u00fcckst\u00e4nde .6); (hoher .6); (g\u00fcte .59); (ninety .57); denn .56; at-tributed .54; weil .53; (awful .53); (food</td></tr><tr><td colspan=\"2\">tion (ISA-11), London, UK. Association for Compu-.23); (vilify .23)</td><td>.52)</td></tr><tr><td colspan=\"2\">tational Linguistics. if when .73; unless .66; though .51;</td><td>wenn .87; dann .72; (\u00fcberhaupt .7);</td></tr><tr><td colspan=\"2\">Jet Hoek, Sandrine Zufferey, Jacqueline Evers-whenever .49, albeit .48; (ansieht .47);</td><td>(nachzukommen .65); (cannot .64);</td></tr><tr><td colspan=\"2\">Vermeul, and Ted J.M. Sanders. 2017. Cognitive (durchkommt .47); (bedenkt .46); dann</td><td>(ohne .64); (priorit\u00e4ten .64); (f\u00e4hrt .64);</td></tr><tr><td colspan=\"2\">complexity and the linguistic marking of coherence .44; whether .42 relations: A parallel corpus study. Journal of Prag-so (genannten .4); (genannte .4); (m\u00f6glich matics, 121:113-131. .39); (called .38); (schnell .37); there-</td><td>(without .62); (properly .62) (oben .57); (schuhe .56); (represent .56); (unfortunate .56); (fight .55);</td></tr><tr><td/><td>fore .36; why .35; (weitermachen .32);</td><td>(outer .55); (gut .55); (darzustellen .55);</td></tr><tr><td/><td>(getan .28); (quickly .28)</td><td>(vertrete .54); (sought .53)</td></tr><tr><td>why</td><td>reason .61; warum .59; weshalb .59;</td><td>(mainstream .56); (legislativpaket .56);</td></tr><tr><td/><td>grund .54; explain .53; gr\u00fcnde .53;</td><td>(umfasst .56); (backed .55); (letztes</td></tr><tr><td/><td>wieso .52; reasons .40; deshalb .39;</td><td>.54); deshalb .53; (started .52); (weaken</td></tr><tr><td/><td>therefore .38</td><td>.51); (doubled .51); (gebeten .5)</td></tr><tr><td>comparison</td><td/><td/></tr><tr><td>but</td><td>however .86; whilst .7; while .7; yet .66;</td><td>(ausschluss .57); (vorrrecht .56); (kein</td></tr><tr><td/><td>nevertheless .62; though .57; although</td><td>.54); (forum .5); (exercising .5); (ak-</td></tr><tr><td/><td>.53; nonetheless .48; zwar .48; (bilden</td><td>tualisiert .5); (abtreibungsrecht .49);</td></tr><tr><td/><td>.41)</td><td>(wahrheitsfindung .49); (scheint .48);</td></tr><tr><td/><td/><td>(institutionelle .47)</td></tr><tr><td/><td/><td>continued on next page</td></tr></table>", | |
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