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
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 164–169
- Language:
- URL:
- https://aclanthology.org/S17-2023
- DOI:
- 10.18653/v1/S17-2023
- Cite (ACL):
- WenLi Zhuang and Ernie Chang. 2017. Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 164–169, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Neobility at SemEval-2017 Task 1: An Attention-based Sentence Similarity Model (Zhuang & Chang, SemEval 2017)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/S17-2023.pdf
- Data
- SICK