AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models
Jonáš Kulhánek, Vojtěch Hudeček, Tomáš Nekvinda, Ondřej Dušek
Abstract
Attention-based pre-trained language models such as GPT-2 brought considerable progress to end-to-end dialogue modelling. However, they also present considerable risks for task-oriented dialogue, such as lack of knowledge grounding or diversity. To address these issues, we introduce modified training objectives for language model finetuning, and we employ massive data augmentation via back-translation to increase the diversity of the training data. We further examine the possibilities of combining data from multiples sources to improve performance on the target dataset. We carefully evaluate our contributions with both human and automatic methods. Our model substantially outperforms the baseline on the MultiWOZ data and shows competitive performance with state of the art in both automatic and human evaluation.- Anthology ID:
- 2021.nlp4convai-1.19
- Volume:
- Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI
- Month:
- November
- Year:
- 2021
- Address:
- Online
- Editors:
- Alexandros Papangelis, Paweł Budzianowski, Bing Liu, Elnaz Nouri, Abhinav Rastogi, Yun-Nung Chen
- Venue:
- NLP4ConvAI
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 198–210
- Language:
- URL:
- https://aclanthology.org/2021.nlp4convai-1.19
- DOI:
- 10.18653/v1/2021.nlp4convai-1.19
- Cite (ACL):
- Jonáš Kulhánek, Vojtěch Hudeček, Tomáš Nekvinda, and Ondřej Dušek. 2021. AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models. In Proceedings of the 3rd Workshop on Natural Language Processing for Conversational AI, pages 198–210, Online. Association for Computational Linguistics.
- Cite (Informal):
- AuGPT: Auxiliary Tasks and Data Augmentation for End-To-End Dialogue with Pre-Trained Language Models (Kulhánek et al., NLP4ConvAI 2021)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2021.nlp4convai-1.19.pdf
- Code
- ufal/augpt
- Data
- MultiWOZ, Taskmaster-1