Open AccessProceedings Article
Lightly Supervised Learning of Procedural Dialog Systems
Svitlana Volkova,Pallavi Choudhury,Chris Quirk,Bill Dolan,Luke Zettlemoyer +4 more
- pp 1669-1679
TLDR
A novel approach is described that first automatically extracts task knowledge from instructions, then learns a dialog manager over this task knowledge to provide assistance, and can be integrated into a dialog system that provides effective help to users.Abstract:
Procedural dialog systems can help users achieve a wide range of goals. However, such systems are challenging to build, currently requiring manual engineering of substantial domain-specific task knowledge and dialog management strategies. In this paper, we demonstrate that it is possible to learn procedural dialog systems given only light supervision, of the type that can be provided by non-experts. We consider domains where the required task knowledge exists in textual form (e.g., instructional web pages) and where system builders have access to statements of user intent (e.g., search query logs or dialog interactions). To learn from such textual resources, we describe a novel approach that first automatically extracts task knowledge from instructions, then learns a dialog manager over this task knowledge to provide assistance. Evaluation in a Microsoft Office domain shows that the individual components are highly accurate and can be integrated into a dialog system that provides effective help to users.read more
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Procedure dialogs using reinforcement learning
TL;DR: In this paper, a machine learning model is used to improve procedure dialogs through knowledge mining within a reinforcement learning framework, and user interactions with the model are monitored and used to update the model.
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Adaptive dialogue management using intent clustering and fuzzy rules
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References
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Proceedings Article
Learning to interpret natural language navigation instructions from observations
David L. Chen,Raymond J. Mooney +1 more
TL;DR: A system that learns to transform natural-language navigation instructions into executable formal plans by using a learned lexicon to refine inferred plans and a supervised learner to induce a semantic parser.
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Weakly Supervised Learning of Semantic Parsers for Mapping Instructions to Actions
Yoav Artzi,Luke Zettlemoyer +1 more
TL;DR: This paper shows semantic parsing can be used within a grounded CCG semantic parsing approach that learns a joint model of meaning and context for interpreting and executing natural language instructions, using various types of weak supervision.
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A survey of statistical user simulation techniques for reinforcement-learning of dialogue management strategies
TL;DR: The role of the dialogue manager in a spoken dialogue system is summarized, a short introduction to reinforcement-learning of dialogue management strategies is given, the literature on user modelling for simulation-based strategy learning is reviewed and recent work on user model evaluation is described.
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Learning Semantic Correspondences with Less Supervision
TL;DR: A generative model is presented that simultaneously segments the text into utterances and maps each utterance to a meaning representation grounded in the world state and generalizes across three domains of increasing difficulty.