Semantic Textual Similarity with Siamese Neural Networks

Tharindu Ranasinghe, Constantin Orasan, Ruslan Mitkov

Abstract
Calculating the Semantic Textual Similarity (STS) is an important research area in natural language processing which plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. This paper evaluates Siamese recurrent architectures, a special type of neural networks, which are used here to measure STS. Several variants of the architecture are compared with existing methods
Anthology ID:
R19-1116
Volume:
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)
Month:
September
Year:
2019
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
1004–1011
Language:
URL:
https://aclanthology.org/R19-1116
DOI:
10.26615/978-954-452-056-4_116
Bibkey:
Cite (ACL):
Tharindu Ranasinghe, Constantin Orasan, and Ruslan Mitkov. 2019. Semantic Textual Similarity with Siamese Neural Networks. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019), pages 1004–1011, Varna, Bulgaria. INCOMA Ltd..
Cite (Informal):
Semantic Textual Similarity with Siamese Neural Networks (Ranasinghe et al., RANLP 2019)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/R19-1116.pdf
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