Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks
Yen Ting Lin, Alexandros Papangelis, Seokhwan Kim, Dilek Hakkani-Tur
Abstract
While rich, open-domain textual data are generally available and may include interesting phenomena (humor, sarcasm, empathy, etc.) most are designed for language processing tasks, and are usually in a non-conversational format. In this work, we take a step towards automatically generating conversational data using Generative Conversational Networks, aiming to benefit from the breadth of available language and knowledge data, and train open domain social conversational agents. We evaluate our approach on conversations with and without knowledge on the Topical Chat dataset using automatic metrics and human evaluators. Our results show that for conversations without knowledge grounding, GCN can generalize from the seed data, producing novel conversations that are less relevant but more engaging and for knowledge-grounded conversations, it can produce more knowledge-focused, fluent, and engaging conversations. Specifically, we show that for open-domain conversations with 10% of seed data, our approach performs close to the baseline that uses 100% of the data, while for knowledge-grounded conversations, it achieves the same using only 1% of the data, on human ratings of engagingness, fluency, and relevance.- Anthology ID:
- 2022.sigdial-1.3
- Volume:
- Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue
- Month:
- September
- Year:
- 2022
- Address:
- Edinburgh, UK
- Editors:
- Oliver Lemon, Dilek Hakkani-Tur, Junyi Jessy Li, Arash Ashrafzadeh, Daniel Hernández Garcia, Malihe Alikhani, David Vandyke, Ondřej Dušek
- Venue:
- SIGDIAL
- SIG:
- SIGDIAL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 26–38
- Language:
- URL:
- https://aclanthology.org/2022.sigdial-1.3
- DOI:
- 10.18653/v1/2022.sigdial-1.3
- Cite (ACL):
- Yen Ting Lin, Alexandros Papangelis, Seokhwan Kim, and Dilek Hakkani-Tur. 2022. Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks. In Proceedings of the 23rd Annual Meeting of the Special Interest Group on Discourse and Dialogue, pages 26–38, Edinburgh, UK. Association for Computational Linguistics.
- Cite (Informal):
- Knowledge-Grounded Conversational Data Augmentation with Generative Conversational Networks (Lin et al., SIGDIAL 2022)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2022.sigdial-1.3.pdf
- Data
- Topical-Chat