Abstract
Task-oriented dialogue systems typically rely on large amounts of high-quality training data or require complex handcrafted rules. However, existing datasets are often limited in size con- sidering the complexity of the dialogues. Additionally, conventional training signal in- ference is not suitable for non-deterministic agent behavior, namely, considering multiple actions as valid in identical dialogue states. We propose the Conversation Graph (ConvGraph), a graph-based representation of dialogues that can be exploited for data augmentation, multi- reference training and evaluation of non- deterministic agents. ConvGraph generates novel dialogue paths to augment data volume and diversity. Intrinsic and extrinsic evaluation across three datasets shows that data augmentation and/or multi-reference training with ConvGraph can improve dialogue success rates by up to 6.4%.- Anthology ID:
- 2021.tacl-1.3
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 9
- Month:
- Year:
- 2021
- Address:
- Cambridge, MA
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 36–52
- Language:
- URL:
- https://aclanthology.org/2021.tacl-1.3
- DOI:
- 10.1162/tacl_a_00352
- Cite (ACL):
- Milan Gritta, Gerasimos Lampouras, and Ignacio Iacobacci. 2021. Conversation Graph: Data Augmentation, Training, and Evaluation for Non-Deterministic Dialogue Management. Transactions of the Association for Computational Linguistics, 9:36–52.
- Cite (Informal):
- Conversation Graph: Data Augmentation, Training, and Evaluation for Non-Deterministic Dialogue Management (Gritta et al., TACL 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.tacl-1.3.pdf