Abstract
While neural machine translation (NMT) provides high-quality translation, it is still hard to interpret and analyze its behavior. We present an interactive interface for visualizing and intervening behavior of NMT, specifically concentrating on the behavior of beam search mechanism and attention component. The tool (1) visualizes search tree and attention and (2) provides interface to adjust search tree and attention weight (manually or automatically) at real-time. We show the tool gives various methods to understand NMT.- Anthology ID:
- D17-2021
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 121–126
- Language:
- URL:
- https://aclanthology.org/D17-2021
- DOI:
- 10.18653/v1/D17-2021
- Cite (ACL):
- Jaesong Lee, Joong-Hwi Shin, and Jun-Seok Kim. 2017. Interactive Visualization and Manipulation of Attention-based Neural Machine Translation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 121–126, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Interactive Visualization and Manipulation of Attention-based Neural Machine Translation (Lee et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D17-2021.pdf