Is the User Enjoying the Conversation? A Case Study on the Impact on the Reward Function

2021/01/13 by Lina M. Rojas-Barahona, Rojas-Barahona, Lina M.
Computer Science · #AI in Service Interactions #Advanced Text Analysis Techniques #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Speech and dialogue systems

paper · pdf · doi:10.48550/arxiv.2101.05004

openalex publication_date 2021/01/13 · openalex created_date 2021/01/18 · openalex updated_date 2026/07/28

Abstract

The impact of user satisfaction in policy learning task-oriented dialogue systems has long been a subject of research interest. Most current models for estimating the user satisfaction either (i) treat out-of-context short-texts, such as product reviews, or (ii) rely on turn features instead of on distributed semantic representations. In this work we adopt deep neural networks that use distributed semantic representation learning for estimating the user satisfaction in conversations. We evaluate the impact of modelling context length in these networks. Moreover, we show that the proposed hierarchical network outperforms state-of-the-art quality estimators. Furthermore, we show that applying these networks to infer the reward function in a Partial Observable Markov Decision Process (POMDP) yields to a great improvement in the task success rate.

Citations

• Improving neural networks by preventing co-adaptation of feature detectors
• Deep Learning for Aspect-Based Sentiment Analysis: A Comparative Review
• LLM-Supported Manufacturing Mapping Generation

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