Abstract
We propose Differentiable Window, a new neural module and general purpose component for dynamic window selection. While universally applicable, we demonstrate a compelling use case of utilizing Differentiable Window to improve standard attention modules by enabling more focused attentions over the input regions. We propose two variants of Differentiable Window, and integrate them within the Transformer architecture in two novel ways. We evaluate our proposed approach on a myriad of NLP tasks, including machine translation, sentiment analysis, subject-verb agreement and language modeling. Our experimental results demonstrate consistent and sizable improvements across all tasks.- Anthology ID:
- 2020.acl-main.589
- Volume:
- Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Dan Jurafsky, Joyce Chai, Natalie Schluter, Joel Tetreault
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 6589–6599
- Language:
- URL:
- https://aclanthology.org/2020.acl-main.589
- DOI:
- 10.18653/v1/2020.acl-main.589
- Cite (ACL):
- Thanh-Tung Nguyen, Xuan-Phi Nguyen, Shafiq Joty, and Xiaoli Li. 2020. Differentiable Window for Dynamic Local Attention. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6589–6599, Online. Association for Computational Linguistics.
- Cite (Informal):
- Differentiable Window for Dynamic Local Attention (Nguyen et al., ACL 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2020.acl-main.589.pdf
- Data
- IMDb Movie Reviews, SST