@inproceedings{prasad-etal-2023-irit,
title = "{IRIT}{\_}{IRIS}{\_}{C} at {S}em{E}val-2023 Task 6: A Multi-level Encoder-based Architecture for Judgement Prediction of Legal Cases and their Explanation",
author = "Prasad, Nishchal and
Boughanem, Mohand and
Dkaki, Taoufiq",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Da San Martino, Giovanni and
Tayyar Madabushi, Harish and
Kumar, Ritesh and
Sartori, Elisa},
booktitle = "Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.semeval-1.94",
doi = "10.18653/v1/2023.semeval-1.94",
pages = "686--692",
abstract = "This paper describes our system used for sub-task C (1 {\&} 2) in Task 6: LegalEval: Understanding Legal Texts. We propose a three-level encoder-based classification architecture that works by fine-tuning a BERT-based pre-trained encoder, and post-processing the embeddings extracted from its last layers, using transformer encoder layers and RNNs. We run ablation studies on the same and analyze itsperformance. To extract the explanations for the predicted class we develop an explanation extraction algorithm, exploiting the idea of a model{'}s occlusion sensitivity. We explored some training strategies with a detailed analysis of the dataset. Our system ranks 2nd (macro-F1 metric) for its sub-task C-1 and 7th (ROUGE-2 metric) for sub-task C-2.",
}
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<abstract>This paper describes our system used for sub-task C (1 & 2) in Task 6: LegalEval: Understanding Legal Texts. We propose a three-level encoder-based classification architecture that works by fine-tuning a BERT-based pre-trained encoder, and post-processing the embeddings extracted from its last layers, using transformer encoder layers and RNNs. We run ablation studies on the same and analyze itsperformance. To extract the explanations for the predicted class we develop an explanation extraction algorithm, exploiting the idea of a model’s occlusion sensitivity. We explored some training strategies with a detailed analysis of the dataset. Our system ranks 2nd (macro-F1 metric) for its sub-task C-1 and 7th (ROUGE-2 metric) for sub-task C-2.</abstract>
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%0 Conference Proceedings
%T IRIT_IRIS_C at SemEval-2023 Task 6: A Multi-level Encoder-based Architecture for Judgement Prediction of Legal Cases and their Explanation
%A Prasad, Nishchal
%A Boughanem, Mohand
%A Dkaki, Taoufiq
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Da San Martino, Giovanni
%Y Tayyar Madabushi, Harish
%Y Kumar, Ritesh
%Y Sartori, Elisa
%S Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F prasad-etal-2023-irit
%X This paper describes our system used for sub-task C (1 & 2) in Task 6: LegalEval: Understanding Legal Texts. We propose a three-level encoder-based classification architecture that works by fine-tuning a BERT-based pre-trained encoder, and post-processing the embeddings extracted from its last layers, using transformer encoder layers and RNNs. We run ablation studies on the same and analyze itsperformance. To extract the explanations for the predicted class we develop an explanation extraction algorithm, exploiting the idea of a model’s occlusion sensitivity. We explored some training strategies with a detailed analysis of the dataset. Our system ranks 2nd (macro-F1 metric) for its sub-task C-1 and 7th (ROUGE-2 metric) for sub-task C-2.
%R 10.18653/v1/2023.semeval-1.94
%U https://aclanthology.org/2023.semeval-1.94
%U https://doi.org/10.18653/v1/2023.semeval-1.94
%P 686-692
Markdown (Informal)
[IRIT_IRIS_C at SemEval-2023 Task 6: A Multi-level Encoder-based Architecture for Judgement Prediction of Legal Cases and their Explanation](https://aclanthology.org/2023.semeval-1.94) (Prasad et al., SemEval 2023)
ACL