English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

A legal textual entailment task is a task to recognize entailment between a law article and its statements. In the Competition on Legal Information Extraction/Entailment (COLIEE), this task is designed as a task to confirm the entailment of a yes/no answer from the given civil code article(s). Based on the development of deep-learning-based natural language processing tools such as bidirectional encoder representations from transformers (BERT), many participants in the task used such tools, and the best performance system of COLIEE 2020 was a BERT-based system. However, because of the limitation of the size of training data provided by the task organizer, training such tools to adapt to the variability of the questions is difficult. In this paper, we propose a data-augmentation method to make training data using civil code articles for understanding the syntactic structure of the questions and articles for entailment. Our BERT-based ensemble system, which uses this augmentation method, achieves the best performance (accuracy = 0.7037) in Task 4 of COLIEE 2021. We also introduce the results of additional experiments to discuss the characteristics of the proposed method.

Data-Augmentation Method for BERT-based Legal Textual Entailment Systems in COLIEE Statute Law Task