Abstract
Calculating Semantic Textual Similarity (STS) plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. All modern state of the art STS methods rely on word embeddings one way or another. The recently introduced contextualised word embeddings have proved more effective than standard word embeddings in many natural language processing tasks. This paper evaluates the impact of several contextualised word embeddings on unsupervised STS methods and compares it with the existing supervised/unsupervised STS methods for different datasets in different languages and different domains- Anthology ID:
- R19-1115
- Volume:
- Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
- Month:
- September
- Year:
- 2019
- Address:
- Varna, Bulgaria
- Editors:
- Ruslan Mitkov, Galia Angelova
- Venue:
- RANLP
- SIG:
- Publisher:
- INCOMA Ltd.
- Note:
- Pages:
- 994–1003
- Language:
- URL:
- https://aclanthology.org/R19-1115
- DOI:
- 10.26615/978-954-452-056-4_115
- Cite (ACL):
- Tharindu Ranasinghe, Constantin Orasan, and Ruslan Mitkov. 2019. Enhancing Unsupervised Sentence Similarity Methods with Deep Contextualised Word Representations. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 994–1003, Varna, Bulgaria. INCOMA Ltd..
- Cite (Informal):
- Enhancing Unsupervised Sentence Similarity Methods with Deep Contextualised Word Representations (Ranasinghe et al., RANLP 2019)
- PDF:
- https://preview.aclanthology.org/naacl24-info/R19-1115.pdf