Abstract
This paper presents a neural net approach to determine Semantic Textual Similarity (STS) using attention-based bidirectional Long Short-Term Memory Networks (Bi-LSTM). To this date, most of the traditional STS systems were rule-based that built on top of excessive use of linguistic features and resources. In this paper, we present an end-to-end attention-based Bi-LSTM neural network system that solely takes word-level features, without expensive feature engineering work or the usage of external resources. By comparing its performance with traditional rule-based systems against SemEval-2012 benchmark, we make an assessment on the limitations and strengths of neural net systems to rule-based systems on Semantic Textual Similarity.- Anthology ID:
- W18-3803
- Volume:
- Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing
- Month:
- August
- Year:
- 2018
- Address:
- Santa Fe, New Mexico, USA
- Editors:
- Peter Machonis, Anabela Barreiro, Kristina Kocijan, Max Silberztein
- Venue:
- LR4NLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 12–17
- Language:
- URL:
- https://aclanthology.org/W18-3803
- DOI:
- Cite (ACL):
- Linrui Zhang and Dan Moldovan. 2018. Rule-based vs. Neural Net Approaches to Semantic Textual Similarity. In Proceedings of the First Workshop on Linguistic Resources for Natural Language Processing, pages 12–17, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
- Cite (Informal):
- Rule-based vs. Neural Net Approaches to Semantic Textual Similarity (Zhang & Moldovan, LR4NLP 2018)
- PDF:
- https://preview.aclanthology.org/naacl24-info/W18-3803.pdf