OTTers: One-turn Topic Transitions for Open-Domain Dialogue
Karin Sevegnani, David M. Howcroft, Ioannis Konstas, Verena Rieser
Abstract
Mixed initiative in open-domain dialogue requires a system to pro-actively introduce new topics. The one-turn topic transition task explores how a system connects two topics in a cooperative and coherent manner. The goal of the task is to generate a “bridging” utterance connecting the new topic to the topic of the previous conversation turn. We are especially interested in commonsense explanations of how a new topic relates to what has been mentioned before. We first collect a new dataset of human one-turn topic transitions, which we callOTTers. We then explore different strategies used by humans when asked to complete such a task, and notice that the use of a bridging utterance to connect the two topics is the approach used the most. We finally show how existing state-of-the-art text generation models can be adapted to this task and examine the performance of these baselines on different splits of the OTTers data.- Anthology ID:
- 2021.acl-long.194
- Volume:
- Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Venues:
- ACL | IJCNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 2492–2504
- Language:
- URL:
- https://aclanthology.org/2021.acl-long.194
- DOI:
- 10.18653/v1/2021.acl-long.194
- Cite (ACL):
- Karin Sevegnani, David M. Howcroft, Ioannis Konstas, and Verena Rieser. 2021. OTTers: One-turn Topic Transitions for Open-Domain Dialogue. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 2492–2504, Online. Association for Computational Linguistics.
- Cite (Informal):
- OTTers: One-turn Topic Transitions for Open-Domain Dialogue (Sevegnani et al., ACL-IJCNLP 2021)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2021.acl-long.194.pdf
- Code
- karinseve/OTTers
- Data
- OTTers, ConceptNet, Topical-Chat