OTTers: One-turn Topic Transitions for Open-Domain Dialogue

Sevegnani, Karin; Howcroft, David M.; Konstas, Ioannis; Rieser, Verena

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.

Citation

Sevegnani, K., Howcroft, D. M., Konstas, I., & Rieser, V. (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) (2492-2504). https://doi.org/10.18653/v1/2021.acl-long.194

• Conference Name: 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing
• Conference Location: Online
• Start Date: Aug 1, 2021
• End Date: Aug 6, 2021
• Acceptance Date: May 6, 2021
• Publication Date: 2021
• Deposit Date: Aug 24, 2021
• Publicly Available Date: Aug 24, 2021
• Publisher: Association for Computational Linguistics (ACL)
• Pages: 2492-2504
• Book Title: 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)
• DOI: https://doi.org/10.18653/v1/2021.acl-long.194
• Public URL: http://researchrepository.napier.ac.uk/Output/2794497
• Publisher URL: https://aclanthology.org/2021.acl-long.194/