| { | |
| "paper_id": "2019", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T14:55:07.913701Z" | |
| }, | |
| "title": "A Feature-granularity Training Strategy for Chinese Spoken Question Answering", | |
| "authors": [ | |
| { | |
| "first": "Shang-Bao", | |
| "middle": [], | |
| "last": "\u7f85\u4e0a\u5821", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Taiwan University of Science and Technology", | |
| "location": {} | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Luo", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "National Taiwan University of Science and Technology", | |
| "location": {} | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Kuan-Yu", | |
| "middle": [], | |
| "last": "\u9673\u51a0\u5b87", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "\u570b\u7acb\u81fa\u7063\u79d1\u6280\u5927\u5b78\u7cfb\u8cc7\u8a0a\u5de5\u7a0b\u7cfb Department of computer science and information engineering National Taiwan University of Science and Technology", | |
| "institution": "", | |
| "location": {} | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "", | |
| "middle": [], | |
| "last": "Chen", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "\u570b\u7acb\u81fa\u7063\u79d1\u6280\u5927\u5b78\u7cfb\u8cc7\u8a0a\u5de5\u7a0b\u7cfb Department of computer science and information engineering National Taiwan University of Science and Technology", | |
| "institution": "", | |
| "location": {} | |
| }, | |
| "email": "[email protected]" | |
| } | |
| ], | |
| "year": "", | |
| "venue": null, | |
| "identifiers": {}, | |
| "abstract": "In a spoken question answering (SQA) system, a straightforward strategy is to transcribe given speech utterances into text using an ASR system. After that, classic methods can be readily used to the auto-transcribe text. However, such a strategy usually can not achieve a good performance due to the recognition errors. In order to mitigate the problem, in this paper, we", | |
| "pdf_parse": { | |
| "paper_id": "2019", | |
| "_pdf_hash": "", | |
| "abstract": [ | |
| { | |
| "text": "In a spoken question answering (SQA) system, a straightforward strategy is to transcribe given speech utterances into text using an ASR system. After that, classic methods can be readily used to the auto-transcribe text. However, such a strategy usually can not achieve a good performance due to the recognition errors. In order to mitigate the problem, in this paper, we", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
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| "section": "Abstract", | |
| "sec_num": null | |
| } | |
| ], | |
| "body_text": [], | |
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| } | |
| } |