David DeVault

Can I Finish? Learning When to Respond to Incremental Interpretation Results in Interactive Dialogue

We investigate novel approaches to responsive overlap behaviors in dialogue systems, opening possibilities for systems to interrupt, acknowledge or complete a users utterance while it is still in progress. Our specific contributions are a method for determining when a system has reached a point of maximal understanding of an ongoing user utterance, and a prototype implementation that shows how systems can use this ability to strategically initiate system completions of user utterances. More broadly, this framework facilitates the implementation of a range of overlap behaviors that are common in human dialogue, but have been largely absent in dialogue systems.

Towards Natural Language Understanding of Partial Speech Recognition Results in Dialogue Systems

We investigate natural language understanding of partial speech recognition results to equip a dialogue system with incremental language processing capabilities for more realistic human-computer conversations. We show that relatively high accuracy can be achieved in understanding of spontaneous utterances before utterances are completed.

Incremental dialogue understanding and feedback for multiparty, multimodal conversation

In order to provide comprehensive listening behavior, virtual humans engaged in dialogue need to incrementally listen, interpret, understand, and react to what someone is saying, in real time, as they are saying it. In this paper, we describe an implemented system for engaging in multiparty dialogue, including incremental understanding and a range of feedback. We present an FML message extension for feedback in multipary dialogue that can be connected to a feedback realizer. We also describe how the important aspects of that message are calculated by different modules involved in partial input processing as a speaker is talking in a multiparty dialogue.

An Information-State Approach to Collaborative Reference

We describe a dialogue system that works with its interlocutor to identify objects. Our contributions include a concise, modular architecture with reversible processes of understanding and generation, an information-state model of reference, and flexible links between semantics and collaborative problem solving.

Learning to Interpret Utterances Using Dialogue History

We describe a methodology for learning a disambiguation model for deep pragmatic interpretations in the context of situated task-oriented dialogue. The system accumulates training examples for ambiguity resolution by tracking the fates of alternative interpretations across dialogue, including subsequent clarificatory episodes initiated by the system itself. We illustrate with a case study building maximum entropy models over abductive interpretations in a referential communication task. The resulting model correctly resolves 81% of ambiguities left unresolved by an initial handcrafted baseline. A key innovation is that our method draws exclusively on a systems own skills and experience and requires no human annotation.

Making Grammar-Based Generation Easier to Deploy in Dialogue Systems

We present a development pipeline and associated algorithms designed to make grammarbased generation easier to deploy in implemented dialogue systems. Our approach realizes a practical trade-off between the capabilities of a systems generation component and the authoring and maintenance burdens imposed on the generation content author for a deployed system. To evaluate our approach, we performed a human rating study with system builders who work on a common largescale spoken dialogue system. Our results demonstrate the viability of our approach and illustrate authoring/performance trade-offs between hand-authored text, our grammar-based approach, and a competing shallow statistical NLG technique.

Practical grammar-based NLG from examples

We present a technique that opens up grammar-based generation to a wider range of practical applications by dramatically reducing the development costs and linguistic expertise that are required. Our method infers the grammatical resources needed for generation from a set of declarative examples that link surface expressions directly to the applications available semantic representations. The same examples further serve to optimize a run-time search strategy that generates the best output that can be found within an application-specific time frame. Our method offers substantially lower development costs than hand-crafted grammars for application-specific NLG, while maintaining high output quality and diversity.

Interpreting vague utterances in context

We use the interpretation of vague scalar predicates like small as an illustration of how systematic semantic models of dialogue context enable the derivation of useful, fine-grained utterance interpretations from radically underspecified semantic forms. Because dialogue context suffices to determine salient alternative scales and relevant distinctions along these scales, we can infer implicit standards of comparison for vague scalar predicates through completely general pragmatics, yet closely constrain the intended meaning to within a natural range.

FLoReS: A Forward Looking, Reward Seeking, Dialogue Manager

We present FLoReS, a new information-state-based dialogue manager, making use of forward inference, local dialogue structure, and plan operators representing subdialogue structure. The aim is to support both advanced, flexible, mixed initiative interaction and efficient policy creation by domain experts. The dialogue manager has been used for two characters in the SimCoach project and is currently being used in several related projects. We present the design of the dialogue manager and preliminary comparative evaluation with a previous system that uses a more conventional state chart dialogue manager.

Negotiation as a Challenge Problem for Virtual Humans

We argue for the importance of negotiation as a challenge problem for virtual human research, and introduce a virtual conversational agent that allows people to practice a wide range of negotiation skills. We describe the multi-issue bargaining task, which has become a de facto standard for teaching and research on negotiation in both the social and computer sciences. This task is popular as it allows scientists or instructors to create a variety of distinct situations that arise in real-life negotiations, simply by manipulating a small number of mathematical parameters. We describe the development of a virtual human that will allow students to practice the interpersonal skills they need to recognize and navigate these situations. An evaluation of an early wizard-controlled version of the system demonstrates the promise of this technology for teaching negotiation and supporting scientific research on social intelligence.