# CAPTAIN at COLIEE 2023: Efficient Methods for Legal Information Retrieval and Entailment Tasks

arXiv: https://arxiv.org/abs/2401.03551 · HTML: https://mlsanity.com/paper/2401.03551/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 7 Jan 2024.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [MS MARCO: A Human Generated MAchine Reading COmprehension Dataset](https://mlsanity.com/paper/1611.09268/) (2016)
- [JNLP Team: Deep Learning for Legal Processing in COLIEE 2020](https://mlsanity.com/paper/2011.08071/) (2020)
- [Repurposing Entailment for Multi-Hop Question Answering Tasks](https://mlsanity.com/paper/1904.09380/) (2019)
- [JNLP Team: Deep Learning Approaches for Legal Processing Tasks in COLIEE 2021](https://mlsanity.com/paper/2106.13405/) (2021)
- [THUIR@COLIEE-2020: Leveraging Semantic Understanding and Exact Matching for Legal Case Retrieval and Entailment](https://mlsanity.com/paper/2012.13102/) (2020)
- [ParaLaw Nets -- Cross-lingual Sentence-level Pretraining for Legal Text Processing](https://mlsanity.com/paper/2106.13403/) (2021)

## Built on by (most cited first, 4 of 4)

- [Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP](https://mlsanity.com/paper/2407.00402/) (2024)
- [Enhancing Legal Document Retrieval: A Multi-Phase Approach with Large Language Models](https://mlsanity.com/paper/2403.18093/) (2024)
- [Exploiting LLMs' Reasoning Capability to Infer Implicit Concepts in Legal Information Retrieval](https://mlsanity.com/paper/2410.12154/) (2024)
- [LentEx: Generalizable Latent Entity Extraction via Synthetic Data and Instruction-Tuned LLMs](https://mlsanity.com/paper/2609.04511/) (2026)

## Abstract (arXiv)

The Competition on Legal Information Extraction/Entailment (COLIEE) is held annually to encourage advancements in the automatic processing of legal texts. Processing legal documents is challenging due to the intricate structure and meaning of legal language. In this paper, we outline our strategies for tackling Task 2, Task 3, and Task 4 in the COLIEE 2023 competition. Our approach involved utilizing appropriate state-of-the-art deep learning methods, designing methods based on domain characteristics observation, and applying meticulous engineering practices and methodologies to the competition. As a result, our performance in these tasks has been outstanding, with first places in Task 2 and Task 3, and promising results in Task 4. Our source code is available at https://github.com/Nguyen2015/CAPTAIN-COLIEE2023/tree/coliee2023.