JNLP Team: Deep Learning for Legal Processing in COLIEE 2020
This work provides a useful approach for information retrieval and decision support in the legal domain, though it is incremental as it applies existing pre-training techniques to a specific domain.
The authors tackled legal retrieval and question-answering in the COLIEE 2020 competition by using deep learning methods pre-trained on large datasets and fine-tuned for specific tasks, achieving good performance to address data scarcity issues.
We propose deep learning based methods for automatic systems of legal retrieval and legal question-answering in COLIEE 2020. These systems are all characterized by being pre-trained on large amounts of data before being finetuned for the specified tasks. This approach helps to overcome the data scarcity and achieve good performance, thus can be useful for tackling related problems in information retrieval, and decision support in the legal domain. Besides, the approach can be explored to deal with other domain specific problems.