Abstract
This paper describes our submission to the sentiment analysis sub-task of “Build It, Break It: The Language Edition (BIBI)”, on both the builder and breaker sides. As a builder, we use convolutional neural nets, trained on both phrase and sentence data. As a breaker, we use Q-learning to learn minimal change pairs, and apply a token substitution method automatically. We analyse the results to gauge the robustness of NLP systems.- Anthology ID:
- W17-5404
- Volume:
- Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Editors:
- Emily Bender, Hal Daumé III, Allyson Ettinger, Sudha Rao
- Venue:
- WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 27–32
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/W17-5404/
- DOI:
- 10.18653/v1/W17-5404
- Cite (ACL):
- Yitong Li, Trevor Cohn, and Timothy Baldwin. 2017. BIBI System Description: Building with CNNs and Breaking with Deep Reinforcement Learning. In Proceedings of the First Workshop on Building Linguistically Generalizable NLP Systems, pages 27–32, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- BIBI System Description: Building with CNNs and Breaking with Deep Reinforcement Learning (Li et al., 2017)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/W17-5404.pdf