Rule-based vs. Neural Net Approaches to Semantic Textual Similarity

Linrui Zhang, Dan Moldovan


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
Venues:
COLING | LR4NLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
12–17
Language:
URL:
https://www.aclweb.org/anthology/W18-3803
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/W18-3803.pdf