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

By Karin Sevegnani, David M. Howcroft, Ioannis Konstas, Verena Rieser. 2021

49 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Anticipating Safety Issues in E2E Conversational AI: Framework and Tooling (2021, background): “Additionally, dialog systems that take initiative (Sevegnani et al., 2021) – as opposed to being purely reactive – could have a similar effect.”
You Don’t Know My Favorite Color: Preventing Dialogue Representations from Revealing Speakers’ Private Personas (2022, unknown): “…progress in large pretrained language models (Radford et al., 2019; Yang et al., 2019), some attempts (Wolf et al., 2019; Zhang et al., 2020; Ham et al., 2020; Shen et al., 2021; Sevegnani et al., 2021; Gu et al., 2021b) are made to build chatbots based on large generative language models (LMs).”