Abstract
The encoder-decoder dialog model is one of the most prominent methods used to build dialog systems in complex domains. Yet it is limited because it cannot output interpretable actions as in traditional systems, which hinders humans from understanding its generation process. We present an unsupervised discrete sentence representation learning method that can integrate with any existing encoder-decoder dialog models for interpretable response generation. Building upon variational autoencoders (VAEs), we present two novel models, DI-VAE and DI-VST that improve VAEs and can discover interpretable semantics via either auto encoding or context predicting. Our methods have been validated on real-world dialog datasets to discover semantic representations and enhance encoder-decoder models with interpretable generation.- Anthology ID:
- P18-1101
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1098–1107
- Language:
- URL:
- https://aclanthology.org/P18-1101
- DOI:
- 10.18653/v1/P18-1101
- Cite (ACL):
- Tiancheng Zhao, Kyusong Lee, and Maxine Eskenazi. 2018. Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1098–1107, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation (Zhao et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/paclic-22-ingestion/P18-1101.pdf
- Code
- snakeztc/NeuralDialog-LAED + additional community code
- Data
- Penn Treebank