# Threshold-Based Retrieval and Textual Entailment Detection on Legal Bar Exam Questions

arXiv: https://arxiv.org/abs/1905.13350 · HTML: https://mlsanity.com/paper/1905.13350/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 May 2019.

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

- [A Survey on Legal Question Answering Systems](https://mlsanity.com/paper/2110.07333/) (2021)
- [JNLP Team: Deep Learning Approaches for Legal Processing Tasks in COLIEE 2021](https://mlsanity.com/paper/2106.13405/) (2021)
- [Transformer-based Approaches for Legal Text Processing](https://mlsanity.com/paper/2202.06397/) (2022)
- [Know When to Fuse: Investigating Non-English Hybrid Retrieval in the Legal Domain](https://mlsanity.com/paper/2409.01357/) (2024)

## Abstract (arXiv)

Getting an overview over the legal domain has become challenging, especially in a broad, international context. Legal question answering systems have the potential to alleviate this task by automatically retrieving relevant legal texts for a specific statement and checking whether the meaning of the statement can be inferred from the found documents. We investigate a combination of the BM25 scoring method of Elasticsearch with word embeddings trained on English translations of the German and Japanese civil law. For this, we define criteria which select a dynamic number of relevant documents according to threshold scores. Exploiting two deep learning classifiers and their respective prediction bias with a threshold-based answer inclusion criterion has shown to be beneficial for the textual entailment task, when compared to the baseline.