An Application of Reinforcement Learning to Dialogue Strategy Selection in a Spoken Dialogue System for Email (1106.0241v1)

Abstract: This paper describes a novel method by which a spoken dialogue system can learn to choose an optimal dialogue strategy from its experience interacting with human users. The method is based on a combination of reinforcement learning and performance modeling of spoken dialogue systems. The reinforcement learning component applies Q-learning (Watkins, 1989), while the performance modeling component applies the PARADISE evaluation framework (Walker et al., 1997) to learn the performance function (reward) used in reinforcement learning. We illustrate the method with a spoken dialogue system named ELVIS (EmaiL Voice Interactive System), that supports access to email over the phone. We conduct a set of experiments for training an optimal dialogue strategy on a corpus of 219 dialogues in which human users interact with ELVIS over the phone. We then test that strategy on a corpus of 18 dialogues. We show that ELVIS can learn to optimize its strategy selection for agent initiative, for reading messages, and for summarizing email folders.

An Application of Reinforcement Learning to Dialogue Strategy Selection in a Spoken Dialogue System for Email

The paper presents a methodical approach to designing spoken dialogue systems by integrating reinforcement learning (RL) and performance modeling. It explores the capacity of these systems to autonomously learn and deploy optimal dialogue strategies through interactions with human users. By focusing on a specific application, the research develops an interactive voice system, ELVIS, to access email via phone, thereby exploring how RL can enhance decision-making in dialogue management.

The framework is structured around two principal components: Q-learning for reinforcement and the PARADISE evaluation mechanism for modeling dialogue performance and deriving rewards. This dual approach enables the system to adaptively refine its strategy choices across diverse dialogue states. The exploration of strategy optimization is contextualized within ELVIS, investigating agent initiative, message reading, and email folder summarization strategies.

Methodology and Implementation

The research implements a detailed state machine representation for ELVIS, anchoring strategy decisions to distinct variables capturing dialogue state. Diverse strategies were trialed, including system and mixed initiative approaches, different summarization strategies, and varied reading strategies.

The training phase involved gathering data through user interactions. Key metrics were defined, encompassing dialogue efficiency (elapsed time, system turns, etc.), quality (recognition scores, rejections, timeouts), task success (as perceived by users), and user satisfaction (quantified through a detailed survey). Data collected informed the system's state-action reward frameworks, essential to evaluating different strategies.

Experimental Design and Results

Elvis's training involved human subjects performing predefined tasks to interact with the state-machine-driven dialogue system. Ultimately, different strategies were scrutinized, leading to conclusions favoring specific dialogue managerial styles. The experiments demonstrated that ELVIS, through RL-enabled optimization, could determine operational strategies for agent initiatives and reading patterns, resulting in elevated user satisfaction metrics compared to unoptimized configurations.

Implications and Future Directions

The findings emphasize how RL, coupled with robust modeling of dialogue performance, can substantially impact the practical deployment of voice-interactive systems. The methodology demonstrates potential testability even with variabilities in user input and provides insight into creating models that are adaptive without explicit human supervision. Key system parameters can be considered when expanding system capabilities or applying similar principles in multifaceted domains.

The research opens several avenues for enhanced exploration, including refining state space representations to better capture user interaction dynamics, which could further refine strategy optimization. Variances in user adaptation and potential long-term learning aspects remain prospective areas for paper. Additionally, considering larger datasets with richer user interaction histories may facilitate more granular insight into dialogue strategy effectiveness.

Practically, these findings can guide the development of systems aimed at managing increasingly complex dialogue-driven interactions, moving towards more intuitive and efficient human-machine communication. Reinforcement learning methods, as demonstrated, hold definitive promise in refining spoken dialogue systems, laying groundwork for future developments and complexification of interactive systems.