Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/309934 
Year of Publication: 
2022
Citation: 
[Journal:] The Review of Socionetwork Strategies [ISSN:] 1867-3236 [Volume:] 16 [Issue:] 1 [Publisher:] Springer Nature Singapore [Place:] Singapore [Year:] 2022 [Pages:] 197-219
Publisher: 
Springer Nature Singapore, Singapore
Abstract: 
Textual entailment classification is one of the hardest tasks for the Natural Language Processing community. In particular, working on entailment with legal statutes comes with an increased difficulty, for example in terms of different abstraction levels, terminology and required domain knowledge to solve this task. In course of the COLIEE competition, we develop three approaches to classify entailment. The first approach combines Sentence-BERT embeddings with a graph neural network, while the second approach uses the domain-specific model LEGAL-BERT, further trained on the competition's retrieval task and fine-tuned for entailment classification. The third approach involves embedding syntactic parse trees with the KERMIT encoder and using them with a BERT model. In this work, we discuss the potential of the latter technique and why of all our submissions, the LEGAL-BERT runs may have outperformed the graph-based approach.
Subjects: 
Contextual embeddings
Graph embeddings
Transformers
Textual entailment
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.