Abstract
We investigate an end-to-end method for automatically inducing task-based dialogue systems from small amounts of unannotated dialogue data. It combines an incremental semantic grammar - Dynamic Syntax and Type Theory with Records (DS-TTR) - with Reinforcement Learning (RL), where language generation and dialogue management are a joint decision problem. The systems thus produced are incremental: dialogues are processed word-by-word, shown previously to be essential in supporting natural, spontaneous dialogue. We hypothesised that the rich linguistic knowledge within the grammar should enable a combinatorially large number of dialogue variations to be processed, even when trained on very few dialogues. Our experiments show that our model can process 74% of the Facebook AI bAbI dataset even when trained on only 0.13% of the data (5 dialogues). It can in addition process 65% of bAbI+, a corpus we created by systematically adding incremental dialogue phenomena such as restarts and self-corrections to bAbI. We compare our model with a state-of-the-art retrieval model, MEMN2N. We find that, in terms of semantic accuracy, the MEMN2N model shows very poor robustness to the bAbI+ transformations even when trained on the full bAbI dataset.- Anthology ID:
- D17-1236
- Volume:
- Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
- Month:
- September
- Year:
- 2017
- Address:
- Copenhagen, Denmark
- Venue:
- EMNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2220–2230
- Language:
- URL:
- https://aclanthology.org/D17-1236
- DOI:
- 10.18653/v1/D17-1236
- Cite (ACL):
- Arash Eshghi, Igor Shalyminov, and Oliver Lemon. 2017. Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2220–2230, Copenhagen, Denmark. Association for Computational Linguistics.
- Cite (Informal):
- Bootstrapping incremental dialogue systems from minimal data: the generalisation power of dialogue grammars (Eshghi et al., EMNLP 2017)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/D17-1236.pdf