BERT-CNN based evidence retrieval and aggregation for Chinese legal multi-choice question answering

Abstract Legal question answering is an important natural language processing application in the legal domain. The Judicial Examination of Chinese Question Answering dataset is the most prominent and more challenging legal question answering dataset, which offers many multiple-choice legal questions...
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Autor*in:	Li, Yanling [verfasserIn] Wu, Jiaye Luo, Xudong

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Open-domain question answering Legal question answering Legal intelligence Deep learning

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: Neural computing & applications - Springer London, 1993, 36(2024), 11 vom: 16. Jan., Seite 5909-5925
Übergeordnetes Werk:	volume:36 ; year:2024 ; number:11 ; day:16 ; month:01 ; pages:5909-5925

Links:	Volltext

DOI / URN:	10.1007/s00521-023-09380-5

Katalog-ID:	SPR055178537

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520			\|a Abstract Legal question answering is an important natural language processing application in the legal domain. The Judicial Examination of Chinese Question Answering dataset is the most prominent and more challenging legal question answering dataset, which offers many multiple-choice legal questions and meta-information about the questions labelled by skilled humans. The current approaches to this task rely solely on pre-trained language models and do not find effective ways to utilise legal knowledge. We propose a retrieving-then-answering framework for the task. Its core is the Graph-Based Evidence Retrieval and Aggregation Network. The network enhances the model’s ability to answer a question by leveraging the legal knowledge relevant to the question and its answer options. The experimental results show that our model outperforms the existing state-of-the-art methods. The results also indicate that our proposed approach to using evidence is practical.
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allfields	10.1007/s00521-023-09380-5 doi (DE-627)SPR055178537 (SPR)s00521-023-09380-5-e DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 54.72 bkl Li, Yanling verfasserin aut BERT-CNN based evidence retrieval and aggregation for Chinese legal multi-choice question answering 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Legal question answering is an important natural language processing application in the legal domain. The Judicial Examination of Chinese Question Answering dataset is the most prominent and more challenging legal question answering dataset, which offers many multiple-choice legal questions and meta-information about the questions labelled by skilled humans. The current approaches to this task rely solely on pre-trained language models and do not find effective ways to utilise legal knowledge. We propose a retrieving-then-answering framework for the task. Its core is the Graph-Based Evidence Retrieval and Aggregation Network. The network enhances the model’s ability to answer a question by leveraging the legal knowledge relevant to the question and its answer options. The experimental results show that our model outperforms the existing state-of-the-art methods. The results also indicate that our proposed approach to using evidence is practical. Open-domain question answering (dpeaa)DE-He213 Legal question answering (dpeaa)DE-He213 Legal intelligence (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wu, Jiaye aut Luo, Xudong aut Enthalten in Neural computing & applications Springer London, 1993 36(2024), 11 vom: 16. Jan., Seite 5909-5925 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:36 year:2024 number:11 day:16 month:01 pages:5909-5925 https://dx.doi.org/10.1007/s00521-023-09380-5 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 VZ AR 36 2024 11 16 01 5909-5925
spelling	10.1007/s00521-023-09380-5 doi (DE-627)SPR055178537 (SPR)s00521-023-09380-5-e DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 54.72 bkl Li, Yanling verfasserin aut BERT-CNN based evidence retrieval and aggregation for Chinese legal multi-choice question answering 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Legal question answering is an important natural language processing application in the legal domain. The Judicial Examination of Chinese Question Answering dataset is the most prominent and more challenging legal question answering dataset, which offers many multiple-choice legal questions and meta-information about the questions labelled by skilled humans. The current approaches to this task rely solely on pre-trained language models and do not find effective ways to utilise legal knowledge. We propose a retrieving-then-answering framework for the task. Its core is the Graph-Based Evidence Retrieval and Aggregation Network. The network enhances the model’s ability to answer a question by leveraging the legal knowledge relevant to the question and its answer options. The experimental results show that our model outperforms the existing state-of-the-art methods. The results also indicate that our proposed approach to using evidence is practical. Open-domain question answering (dpeaa)DE-He213 Legal question answering (dpeaa)DE-He213 Legal intelligence (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wu, Jiaye aut Luo, Xudong aut Enthalten in Neural computing & applications Springer London, 1993 36(2024), 11 vom: 16. Jan., Seite 5909-5925 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:36 year:2024 number:11 day:16 month:01 pages:5909-5925 https://dx.doi.org/10.1007/s00521-023-09380-5 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 VZ AR 36 2024 11 16 01 5909-5925
allfields_unstemmed	10.1007/s00521-023-09380-5 doi (DE-627)SPR055178537 (SPR)s00521-023-09380-5-e DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 54.72 bkl Li, Yanling verfasserin aut BERT-CNN based evidence retrieval and aggregation for Chinese legal multi-choice question answering 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Legal question answering is an important natural language processing application in the legal domain. The Judicial Examination of Chinese Question Answering dataset is the most prominent and more challenging legal question answering dataset, which offers many multiple-choice legal questions and meta-information about the questions labelled by skilled humans. The current approaches to this task rely solely on pre-trained language models and do not find effective ways to utilise legal knowledge. We propose a retrieving-then-answering framework for the task. Its core is the Graph-Based Evidence Retrieval and Aggregation Network. The network enhances the model’s ability to answer a question by leveraging the legal knowledge relevant to the question and its answer options. The experimental results show that our model outperforms the existing state-of-the-art methods. The results also indicate that our proposed approach to using evidence is practical. Open-domain question answering (dpeaa)DE-He213 Legal question answering (dpeaa)DE-He213 Legal intelligence (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wu, Jiaye aut Luo, Xudong aut Enthalten in Neural computing & applications Springer London, 1993 36(2024), 11 vom: 16. Jan., Seite 5909-5925 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:36 year:2024 number:11 day:16 month:01 pages:5909-5925 https://dx.doi.org/10.1007/s00521-023-09380-5 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 VZ AR 36 2024 11 16 01 5909-5925
allfieldsGer	10.1007/s00521-023-09380-5 doi (DE-627)SPR055178537 (SPR)s00521-023-09380-5-e DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 54.72 bkl Li, Yanling verfasserin aut BERT-CNN based evidence retrieval and aggregation for Chinese legal multi-choice question answering 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Legal question answering is an important natural language processing application in the legal domain. The Judicial Examination of Chinese Question Answering dataset is the most prominent and more challenging legal question answering dataset, which offers many multiple-choice legal questions and meta-information about the questions labelled by skilled humans. The current approaches to this task rely solely on pre-trained language models and do not find effective ways to utilise legal knowledge. We propose a retrieving-then-answering framework for the task. Its core is the Graph-Based Evidence Retrieval and Aggregation Network. The network enhances the model’s ability to answer a question by leveraging the legal knowledge relevant to the question and its answer options. The experimental results show that our model outperforms the existing state-of-the-art methods. The results also indicate that our proposed approach to using evidence is practical. Open-domain question answering (dpeaa)DE-He213 Legal question answering (dpeaa)DE-He213 Legal intelligence (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wu, Jiaye aut Luo, Xudong aut Enthalten in Neural computing & applications Springer London, 1993 36(2024), 11 vom: 16. Jan., Seite 5909-5925 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:36 year:2024 number:11 day:16 month:01 pages:5909-5925 https://dx.doi.org/10.1007/s00521-023-09380-5 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 VZ AR 36 2024 11 16 01 5909-5925
allfieldsSound	10.1007/s00521-023-09380-5 doi (DE-627)SPR055178537 (SPR)s00521-023-09380-5-e DE-627 ger DE-627 rakwb eng 004 VZ 004 VZ 54.72 bkl Li, Yanling verfasserin aut BERT-CNN based evidence retrieval and aggregation for Chinese legal multi-choice question answering 2024 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Legal question answering is an important natural language processing application in the legal domain. The Judicial Examination of Chinese Question Answering dataset is the most prominent and more challenging legal question answering dataset, which offers many multiple-choice legal questions and meta-information about the questions labelled by skilled humans. The current approaches to this task rely solely on pre-trained language models and do not find effective ways to utilise legal knowledge. We propose a retrieving-then-answering framework for the task. Its core is the Graph-Based Evidence Retrieval and Aggregation Network. The network enhances the model’s ability to answer a question by leveraging the legal knowledge relevant to the question and its answer options. The experimental results show that our model outperforms the existing state-of-the-art methods. The results also indicate that our proposed approach to using evidence is practical. Open-domain question answering (dpeaa)DE-He213 Legal question answering (dpeaa)DE-He213 Legal intelligence (dpeaa)DE-He213 Deep learning (dpeaa)DE-He213 Wu, Jiaye aut Luo, Xudong aut Enthalten in Neural computing & applications Springer London, 1993 36(2024), 11 vom: 16. Jan., Seite 5909-5925 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:36 year:2024 number:11 day:16 month:01 pages:5909-5925 https://dx.doi.org/10.1007/s00521-023-09380-5 lizenzpflichtig Volltext SYSFLAG_0 GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 VZ AR 36 2024 11 16 01 5909-5925
language	English
source	Enthalten in Neural computing & applications 36(2024), 11 vom: 16. Jan., Seite 5909-5925 volume:36 year:2024 number:11 day:16 month:01 pages:5909-5925
sourceStr	Enthalten in Neural computing & applications 36(2024), 11 vom: 16. Jan., Seite 5909-5925 volume:36 year:2024 number:11 day:16 month:01 pages:5909-5925
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Open-domain question answering Legal question answering Legal intelligence Deep learning
dewey-raw	004
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author	Li, Yanling
spellingShingle	Li, Yanling ddc 004 bkl 54.72 misc Open-domain question answering misc Legal question answering misc Legal intelligence misc Deep learning BERT-CNN based evidence retrieval and aggregation for Chinese legal multi-choice question answering
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title	BERT-CNN based evidence retrieval and aggregation for Chinese legal multi-choice question answering
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title_sort	bert-cnn based evidence retrieval and aggregation for chinese legal multi-choice question answering
title_auth	BERT-CNN based evidence retrieval and aggregation for Chinese legal multi-choice question answering
abstract	Abstract Legal question answering is an important natural language processing application in the legal domain. The Judicial Examination of Chinese Question Answering dataset is the most prominent and more challenging legal question answering dataset, which offers many multiple-choice legal questions and meta-information about the questions labelled by skilled humans. The current approaches to this task rely solely on pre-trained language models and do not find effective ways to utilise legal knowledge. We propose a retrieving-then-answering framework for the task. Its core is the Graph-Based Evidence Retrieval and Aggregation Network. The network enhances the model’s ability to answer a question by leveraging the legal knowledge relevant to the question and its answer options. The experimental results show that our model outperforms the existing state-of-the-art methods. The results also indicate that our proposed approach to using evidence is practical. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract Legal question answering is an important natural language processing application in the legal domain. The Judicial Examination of Chinese Question Answering dataset is the most prominent and more challenging legal question answering dataset, which offers many multiple-choice legal questions and meta-information about the questions labelled by skilled humans. The current approaches to this task rely solely on pre-trained language models and do not find effective ways to utilise legal knowledge. We propose a retrieving-then-answering framework for the task. Its core is the Graph-Based Evidence Retrieval and Aggregation Network. The network enhances the model’s ability to answer a question by leveraging the legal knowledge relevant to the question and its answer options. The experimental results show that our model outperforms the existing state-of-the-art methods. The results also indicate that our proposed approach to using evidence is practical. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract Legal question answering is an important natural language processing application in the legal domain. The Judicial Examination of Chinese Question Answering dataset is the most prominent and more challenging legal question answering dataset, which offers many multiple-choice legal questions and meta-information about the questions labelled by skilled humans. The current approaches to this task rely solely on pre-trained language models and do not find effective ways to utilise legal knowledge. We propose a retrieving-then-answering framework for the task. Its core is the Graph-Based Evidence Retrieval and Aggregation Network. The network enhances the model’s ability to answer a question by leveraging the legal knowledge relevant to the question and its answer options. The experimental results show that our model outperforms the existing state-of-the-art methods. The results also indicate that our proposed approach to using evidence is practical. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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container_issue	11
title_short	BERT-CNN based evidence retrieval and aggregation for Chinese legal multi-choice question answering
url	https://dx.doi.org/10.1007/s00521-023-09380-5
remote_bool	true
author2	Wu, Jiaye Luo, Xudong
author2Str	Wu, Jiaye Luo, Xudong
ppnlink	271595574
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s00521-023-09380-5
up_date	2024-07-03T13:52:26.965Z
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score	7.398015

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BERT-CNN based evidence retrieval and aggregation for Chinese legal multi-choice question answering

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