Neural Attention Learning for Legal Query Reformulation
Pages 272 - 273
Abstract
Query reformulation is the process of iteratively modifying a query to improve the quality of search engine results. In recent years, the task of reformulating natural language (NL) queries has received considerable diligence from both industry and academic communities. Since legal queries are diverse and multi-faceted, traditional approaches cannot effectively handle low frequency and out-of-vocabulary (OOV) words. Motivated by these issues, we rethink the task of legal query reformulation as a type of monolingual neural machine translation (NMT) problem, where the input (source) query is potentially erroneous and the output (target) query is its corrected form. We propose a unified and principled framework with multiple levels of granularity.
References
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I. Sutskever, O. Vinyals, and Q. V. Le, "Sequence to sequence learning with neural networks," in Advances in neural information processing systems, pp. 3104--3112. 2014.
[2]
S. Arunprasath and B. Venkata Nagaraju, "Deep ensemble learning for legal query understanding," in Proceedings of CIKM 2018 Workshop on Legal Data Analytics and Mining (LeDAM 2018), CEUR-WS.org, October 2018. To appear.
[3]
P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, "Enriching word vectors with subword information," TACL, vol. 5, pp. 135--146, 2017.
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P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, "Enriching word vectors with subword information," Transactions of the Association for Computational Linguistics, vol. 5, pp. 135--146, 2017.
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D. Bahdanau, K. Cho, and Y. Bengio, "Neural machine translation by jointly learning to align and translate," CoRR, vol. abs/1409.0473, 2014.
Index Terms
- Neural Attention Learning for Legal Query Reformulation
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Information & Contributors
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Published In
June 2019
312 pages
ISBN:9781450367547
DOI:10.1145/3322640
Copyright © 2019 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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In-Cooperation
- Univ. of Montreal: University of Montreal
- AAAI
- IAAIL: Intl Asso for Artifical Intel & Law
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 17 June 2019
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- Demonstration
- Research
- Refereed limited
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ICAIL '19
Sponsor:
ICAIL '19: Seventeenth International Conference on Artificial Intelligence and Law
June 17 - 21, 2019
QC, Montreal, Canada
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Overall Acceptance Rate 69 of 169 submissions, 41%
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