This repository contains the code for the paper "PoliToHFI at SemEval-2023 Task 6: Leveraging Entity-Aware and Hierarchical Transformers For Legal Entity Recognition and Court Judgement Prediction" accepted at SemEval-2023, Task 6.
The repository is organized as follows:
legal_ner: contains the code and the data for the Legal Named Entity Recognition (L-NER) task (Task 6.B)legal_cpje: contains the code and the data for the Court Judgment Prediction and Explanation (CJPE) tasks (Tasks 6.C.1 and 6.C.2)
Further details are available in the README files of the subfolders.
If you use this code, please cite the following paper:
@inproceedings{benedetto-etal-2023-politohfi,
title = "{P}oli{T}o{HFI} at {S}em{E}val-2023 Task 6: Leveraging Entity-Aware and Hierarchical Transformers For Legal Entity Recognition and Court Judgment Prediction",
author = "Benedetto, Irene and
Koudounas, Alkis and
Vaiani, Lorenzo and
Pastor, Eliana and
Baralis, Elena and
Cagliero, Luca and
Tarasconi, Francesco",
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-1.194",
doi = "10.18653/v1/2023.semeval-1.194",
pages = "1401--1411",
abstract = "The use of Natural Language Processing techniques in the legal domain has become established for supporting attorneys and domain experts in content retrieval and decision-making. However, understanding the legal text poses relevant challenges in the recognition of domain-specific entities and the adaptation and explanation of predictive models. This paper addresses the Legal Entity Name Recognition (L-NER) and Court judgment Prediction (CPJ) and Explanation (CJPE) tasks. The L-NER solution explores the use of various transformer-based models, including an entity-aware method attending domain-specific entities. The CJPE proposed method relies on hierarchical BERT-based classifiers combined with local input attribution explainers. We propose a broad comparison of eXplainable AI methodologies along with a novel approach based on NER. For the L-NER task, the experimental results remark on the importance of domain-specific pre-training. For CJP our lightweight solution shows performance in line with existing approaches, and our NER-boosted explanations show promising CJPE results in terms of the conciseness of the prediction explanations.",
}This code is released under the Apache 2.0 license. Please take a look at the LICENSE file for more details.
If you need help or issues using the code, please submit a GitHub issue.
For other communications related to this repository, please contact Alkis Koudounas or Irene Benedetto.