Aiming to Know You Better Perhaps Makes Me a More Engaging Dialogue Partner

By Yury Zemlyanskiy, Fei Sha. 2018

20 citations recorded by the source corpus.

There have been several attempts to define a plausible motivation for a chit-chat dialogue agent that can lead to engaging conversations. In this work, we explore a new direction where the agent specifically focuses on discovering information about its interlocutor. We formalize this approach by defining a quantitative metric. We propose an algorithm for the agent to maximize it. We validate the idea with human evaluation where our system outperforms various baselines. We demonstrate that the metric indeed correlates with the human judgments of engagingness.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Survey of Hallucination in Natural Language Generation (2022, background): “PersonaChat datasets [30, 211] accelerate the research on persona consistency [68, 88, 118, 192, 202, 207, 214].”
Importance of Search and Evaluation Strategies in Neural Dialogue Modeling (2018, background): “Some are more generous while others are quite harsh, as recently reported in Zhang et al. (2018); Zemlyanskiy and Sha (2018).”
You Impress Me: Dialogue Generation via Mutual Persona Perception (2020, background): “More recently, under the topic of dialogue per-sonalizing, Zemlyanskiy and Sha (2018) proposed a post-processing method to re-rank candidates generated by beam search, while Olabiyi et al. (2019) employed adversarial approaches to solve the consistency problem on interlocutors’ names.”
Importance of a Search Strategy in Neural Dialogue Modelling (2018, background): “Some annotators are more generous while others are quite harsh, leading the naive average score to have very high variance; for example, as recently reported in (Zhang et al., 2018; Zemlyanskiy and Sha, 2018).”
Personalizing Dialogue Agents via Meta-Learning (2019, background): “Several works improved on the initial baselines with various methodologies (Kulikov et al., 2018; Yavuz et al.; Hancock et al., 2019; Lucas et al., 2009; Joshi et al., 2017; Zemlyanskiy and Sha, 2018; Gao et al., 2018).”