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tBERT: Topic models and BERT joining forces for semantic similarity detection - 2020

Research Area:  Machine Learning

Abstract:

Semantic similarity detection is a fundamental task in natural language understanding. Adding topic information has been useful for previous feature-engineered semantic similarity models as well as neural models for other tasks. There is currently no standard way of combining topics with pretrained contextual representations such as BERT. We propose a novel topic-informed BERT-based architecture for pairwise semantic similarity detection and show that our model improves performance over strong neural baselines across a variety of English language datasets. We find that the addition of topics to BERT helps particularly with resolving domain-specific cases.

Author(s) Name:  Nicole Peinelt, Dong Nguyen, Maria Liakata

Journal name:  

Conferrence name:  Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Publisher name:  ACL

DOI:  10.18653/v1/2020.acl-main.630

Volume Information:  Pages: 7047–7055,2020