Related papers: Investigating Reinforcement Learning for Communication Strategies in a Task-Initiative Setting

Investigating Reinforcement Learning for Communication Strategies in a Task-Initiative Setting

URL: http://arxiv.org/abs/2308.01479v1
Date: Thu, 3 Aug 2023 00:10:23 GMT
Title: Investigating Reinforcement Learning for Communication Strategies in a Task-Initiative Setting
Authors: Baber Khalid and Matthew Stone
Abstract summary: We analyze the trade-offs between initial presentation and subsequent followup as a function of user clarification strategy. We find surprising advantages to coherence-based representations of dialogue strategy, which bring minimal data requirements, explainable choices, and strong audit capabilities.
Score: 8.680676599607123
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Many conversational domains require the system to present nuanced information to users. Such systems must follow up what they say to address clarification questions and repair misunderstandings. In this work, we explore this interactive strategy in a referential communication task. Using simulation, we analyze the communication trade-offs between initial presentation and subsequent followup as a function of user clarification strategy, and compare the performance of several baseline strategies to policies derived by reinforcement learning. We find surprising advantages to coherence-based representations of dialogue strategy, which bring minimal data requirements, explainable choices, and strong audit capabilities, but incur little loss in predicted outcomes across a wide range of user models.

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