Peter A. Heeman

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The Trains project is an eeort to build a conversationally proocient planning assistant. A key part of the project is the construction of the Trains system, which provides the research platform for a wide range of issues in natural language understanding, mixed-initiative planning systems, and representing and reasoning about time, actions and events. Four(More)
Interactive spoken dialogue provides many new challenges for natural language understanding systems. One of the most critical challenges is simply determining the speaker's intended utterances: both segmenting a speaker's turn into utterances and determining the intended words in each utterance. Even assuming perfect word recognition, the latter problem is(More)
Interactive spoken dialog provides many new challenges for natural language understanding systems. One of the most critical challenges is simply determining the speaker’s intended utterances: both segmenting a speaker’s turn into utterances and determining the intended words in each utterance. Even assuming perfect word recognition, the latter problem is(More)
Participants in a discourse sometimes fail to understand one another, but, when aware of the problem, collaborate upon or negotiate the meaning of a problematic utterance. To address nonunderstanding, we have developed two plan-based models of collaboration in identifying the correct referent of a description: one covers situations where both conversants(More)
Language models for speech recognition concentrate solely on recognizing the words that were spoken. In this paper, we advocate redefining the speech recognition problem so that its goal is to find both the best sequence of words and their POS tags, and thus incorporate POS tagging. The use of POS tags allows more sophisticated generalizations than are(More)
Interactive spoken dialogue provides many new challenges for natural language understanding systems. One of the most critical challenges is simply determining the speaker’s intended utterances: both segmenting a speaker’s turn into utterances and determining the intended words in each utterance. Even assuming perfect word recognition, the latter problem is(More)
This paper presents a computational model of how conversational participants collaborate in making referring expressions. The model is based on the planning paradigm. It employs plans for constructing and recognizing referring expressions and meta-plans for constructing and recognizing clarifications. This allows the model to account for the generation and(More)
This paper describes an application of reinforcement learning to determine a dialog policy for a complex collaborative task where policies for both the system and a proxy for a user of the system are learned simultaneously. With this approach a useful dialog policy is learned without the drawbacks of other approaches that require significant human(More)
We report on the results of a study in which pairs of subjects were involved in spoken dialogues and one of the subjects also operated a simulated vehicle. We estimated the driver's cognitive load based on pupil size measurements from a remote eye tracker. We compared the cognitive load estimates based on the physiological pupillometric data and driving(More)
Interactive spoken dialog provides many new challenges for spoken language systems. One of the most critical is the prevalence of speech repairs. This paper presents an algorithm that detects and corrects speech repairs based on finding the repair pattern. The repair pattern is built by finding word matches and word replacements, and identifying fragments(More)