Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model

WenLi Zhuang, Ernie Chang


Abstract
This paper describes a neural-network model which performed competitively (top 6) at the SemEval 2017 cross-lingual Semantic Textual Similarity (STS) task. Our system employs an attention-based recurrent neural network model that optimizes the sentence similarity. In this paper, we describe our participation in the multilingual STS task which measures similarity across English, Spanish, and Arabic.
Anthology ID:
S17-2023
Volume:
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)
Month:
August
Year:
2017
Address:
Vancouver, Canada
Venue:
*SEMEVAL
SIGs:
SIGLEX | SIGSEM
Publisher:
Association for Computational Linguistics
Note:
Pages:
164–169
Language:
URL:
https://www.aclweb.org/anthology/S17-2023
DOI:
10.18653/v1/S17-2023
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/S17-2023.pdf