Abstract
With the recent success of Recurrent Neural Networks (RNNs) in Machine Translation (MT), attention mechanisms have become increasingly popular. The purpose of this paper is two-fold; firstly, we propose a novel attention model on Tree Long Short-Term Memory Networks (Tree-LSTMs), a tree-structured generalization of standard LSTM. Secondly, we study the interaction between attention and syntactic structures, by experimenting with three LSTM variants: bidirectional-LSTMs, Constituency Tree-LSTMs, and Dependency Tree-LSTMs. Our models are evaluated on two semantic relatedness tasks: semantic relatedness scoring for sentence pairs (SemEval 2012, Task 6 and SemEval 2014, Task 1) and paraphrase detection for question pairs (Quora, 2017).- Anthology ID:
- P18-1197
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2116–2125
- Language:
- URL:
- https://aclanthology.org/P18-1197
- DOI:
- 10.18653/v1/P18-1197
- Cite (ACL):
- Amulya Gupta and Zhu Zhang. 2018. To Attend or not to Attend: A Case Study on Syntactic Structures for Semantic Relatedness. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 2116–2125, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- To Attend or not to Attend: A Case Study on Syntactic Structures for Semantic Relatedness (Gupta & Zhang, ACL 2018)
- PDF:
- https://preview.aclanthology.org/teach-a-man-to-fish/P18-1197.pdf
- Data
- SICK