@inproceedings{lima-etal-2023-irit,
title = "{IRIT}{\_}{IRIS}{\_}{A} at {S}em{E}val-2023 Task 6: Legal Rhetorical Role Labeling Supported by Dynamic-Filled Contextualized Sentence Chunks",
author = "Lima, Alexandre Gomes de and
Moreno, Jose G. and
H. da S. Aranha, Eduardo",
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-1.125",
doi = "10.18653/v1/2023.semeval-1.125",
pages = "905--912",
abstract = "This work presents and evaluates an approach to efficiently leverage the context exploitation ability of pre-trained Transformer models as a way of boosting the performance of models tackling the Legal Rhetorical Role Labeling task. The core idea is to feed the model with sentence chunks that are assembled in a way that avoids the insertion of padding tokens and the truncation of sentences and, hence, obtain better sentence embeddings. The achieved results show that our proposal is efficient, despite its simplicity, since models based on it overcome strong baselines by 3.76{\%} in the worst case and by 8.71{\%} in the best case.",
}
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<abstract>This work presents and evaluates an approach to efficiently leverage the context exploitation ability of pre-trained Transformer models as a way of boosting the performance of models tackling the Legal Rhetorical Role Labeling task. The core idea is to feed the model with sentence chunks that are assembled in a way that avoids the insertion of padding tokens and the truncation of sentences and, hence, obtain better sentence embeddings. The achieved results show that our proposal is efficient, despite its simplicity, since models based on it overcome strong baselines by 3.76% in the worst case and by 8.71% in the best case.</abstract>
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%0 Conference Proceedings
%T IRIT_IRIS_A at SemEval-2023 Task 6: Legal Rhetorical Role Labeling Supported by Dynamic-Filled Contextualized Sentence Chunks
%A Lima, Alexandre Gomes de
%A Moreno, Jose G.
%A H. da S. Aranha, Eduardo
%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 lima-etal-2023-irit
%X This work presents and evaluates an approach to efficiently leverage the context exploitation ability of pre-trained Transformer models as a way of boosting the performance of models tackling the Legal Rhetorical Role Labeling task. The core idea is to feed the model with sentence chunks that are assembled in a way that avoids the insertion of padding tokens and the truncation of sentences and, hence, obtain better sentence embeddings. The achieved results show that our proposal is efficient, despite its simplicity, since models based on it overcome strong baselines by 3.76% in the worst case and by 8.71% in the best case.
%R 10.18653/v1/2023.semeval-1.125
%U https://aclanthology.org/2023.semeval-1.125
%U https://doi.org/10.18653/v1/2023.semeval-1.125
%P 905-912
Markdown (Informal)
[IRIT_IRIS_A at SemEval-2023 Task 6: Legal Rhetorical Role Labeling Supported by Dynamic-Filled Contextualized Sentence Chunks](https://aclanthology.org/2023.semeval-1.125) (Lima et al., SemEval 2023)
ACL