OTTers: One-turn Topic Transitions for Open-Domain Dialogue
This work addresses the challenge of proactive topic management in dialogue systems, which is incremental as it builds on existing models with a new dataset and task.
The paper tackles the problem of generating coherent one-turn topic transitions in open-domain dialogue by introducing a new dataset, OTTers, and adapting state-of-the-art text generation models to this task, achieving performance benchmarks on different data splits.
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 call OTTers. 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.