@inproceedings{joshi-etal-2024-il,
title = "{IL}-{TUR}: Benchmark for {I}ndian Legal Text Understanding and Reasoning",
author = "Joshi, Abhinav and
Paul, Shounak and
Sharma, Akshat and
Goyal, Pawan and
Ghosh, Saptarshi and
Modi, Ashutosh",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.618",
pages = "11460--11499",
abstract = "Legal systems worldwide are inundated with exponential growth in cases and documents. There is an imminent need to develop NLP and ML techniques for automatically processing and understanding legal documents to streamline the legal system. However, evaluating and comparing various NLP models designed specifically for the legal domain is challenging. This paper addresses this challenge by proposing : Benchmark for Indian Legal Text Understanding and Reasoning. contains monolingual (English, Hindi) and multi-lingual (9 Indian languages) domain-specific tasks that address different aspects of the legal system from the point of view of understanding and reasoning over Indian legal documents. We present baseline models (including LLM-based) for each task, outlining the gap between models and the ground truth. To foster further research in the legal domain, we create a leaderboard (available at: https://exploration-lab.github.io/IL-TUR/ ) where the research community can upload and compare legal text understanding systems.",
}
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%0 Conference Proceedings
%T IL-TUR: Benchmark for Indian Legal Text Understanding and Reasoning
%A Joshi, Abhinav
%A Paul, Shounak
%A Sharma, Akshat
%A Goyal, Pawan
%A Ghosh, Saptarshi
%A Modi, Ashutosh
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F joshi-etal-2024-il
%X Legal systems worldwide are inundated with exponential growth in cases and documents. There is an imminent need to develop NLP and ML techniques for automatically processing and understanding legal documents to streamline the legal system. However, evaluating and comparing various NLP models designed specifically for the legal domain is challenging. This paper addresses this challenge by proposing : Benchmark for Indian Legal Text Understanding and Reasoning. contains monolingual (English, Hindi) and multi-lingual (9 Indian languages) domain-specific tasks that address different aspects of the legal system from the point of view of understanding and reasoning over Indian legal documents. We present baseline models (including LLM-based) for each task, outlining the gap between models and the ground truth. To foster further research in the legal domain, we create a leaderboard (available at: https://exploration-lab.github.io/IL-TUR/ ) where the research community can upload and compare legal text understanding systems.
%U https://aclanthology.org/2024.acl-long.618
%P 11460-11499
Markdown (Informal)
[IL-TUR: Benchmark for Indian Legal Text Understanding and Reasoning](https://aclanthology.org/2024.acl-long.618) (Joshi et al., ACL 2024)
ACL
- Abhinav Joshi, Shounak Paul, Akshat Sharma, Pawan Goyal, Saptarshi Ghosh, and Ashutosh Modi. 2024. IL-TUR: Benchmark for Indian Legal Text Understanding and Reasoning. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11460–11499, Bangkok, Thailand. Association for Computational Linguistics.