Concept Type Prediction and Responsive Adaptation in a Dialogue System

Responsive adaptation in spoken dialog systems involves a change in dialog system behavior in response to a user or a dialog situation. In this paper we address responsive adaptation in the automatic speech recognition (ASR) module of a spoken dialog system. We hypothesize that information about the content of a user utterance may help improve speech recognition for the utterance. We use a two-step process to test this hypothesis: first, we automatically predict the task-relevant concept types likely to be present in a user utterance using features from the dialog context and from the output of first-pass ASR of the utterance; and then, we adapt the ASR's language model to the predicted content of the user's utterance and run a second pass of ASR. We show that: (1) it is possible to achieve high accuracy in determining presence or absence of particular concept types in a post-confirmation utterance; and (2) 2-pass speech recognition with concept type classification and language model adaptation can lead to improved speech recognition performance for post-confirmation utterances

Content Planning and Generation in ContinuousSpeech Spoken Dialog Systems

Researchers interested in constructing conversational agents that can interact naturally in relatively complex domains face a unique set of constraints. Generation must take place in real, or near-real, time. The language coverage must be extensive, and language use must be varied. A grammar-based approach can be both slow and awkward. On the other hand, it is difficult t

Aspects of Natural Language Generation

Natural language generation is a knowledge-intensive, goal-directed process involving many interacting choices. Some questions that a generation system must answer include: (1) What information needs to be included in the output to satisfy the speaker's or writer's communicative goals? (2) How should a discourse contribution be structured to ensure its coherence? (3) Which modalities should be used to maximize the information exchange? (4) How can output be tailored to specific users? In this paper, we examine some aspects of natural language generation that constrain the planning process, including theories of discourse structure, models of discourse context and of users, and multimodal generation

Content Planning for Multi-Modal, Mixed-Initiative Task-Oriented Dialog

Previous research in content planning for generation has tended to assume that content planning can be performed: ffl by one monolithic planning process ffl using intentional and rhetorical structures only ffl by performing top-down planning to produce complete plans and then executing them These are significant simplifying assumptions that may work for text and monologic discourse, but fail to account for significant aspects of dialog. We would like to examine these assumptions in a critical light, and to do that we propose the construction of a theory of content planning for task-oriented dialog that can handle global and local dialog behaviors (initiative, conversational conventions, turn-taking and grounding) as well as intentional and rhetorical structures, and that takes into account the different levels of dialog (domain, task and conversation). The University of Rochester Computer Science Department supported this work. 1 Introduction According to Merriam-Webster [44], di..

Aspects of Natural Language Generation

Natural language generation is a knowledge-intensive, goal-directed process involving many interacting choices. Some questions that a generation system must answer include: ffl what information needs to be included in the output to satisfy the speaker's or writer's communicative goals? ffl how should a discourse contribution be structured to ensure its coherence? ffl which modalities should be used to maximize the information exchange? ffl how can output be tailored to specific users? In this paper, we examine some aspects of natural language generation that constrain the planning process, including theories of discourse structure, models of discourse context and of users, and multimodal generation. This work was supported by ONR research grant N00014-95-1-1088, U.S. Air Force/Rome Labs research contract no. F30602-95-1-0025, NSF research grant no. IRI-9623665 and Columbia University/NSF research grant no. OPG: Contents 1 Introduction 4 1.1 What is Natural Language Generation? ..

A Preliminary Model of Centering in Dialog

The centering framework explains local discourse coherence by relating a speaker's focus of attention and the forms of referring expressions. Although this framework has proven useful in single-speaker discourse, its utility for multi-party discourse has not been shown. It is unclear how to adapt it to handle discourse phenomena such as turn-taking, acknowledgments, first and second person pronouns, and disfluencies. This paper reports our experiments applying three naive models of centering theory for dialog. These results will be used as a baseline for future, more sophisticated models

TRAINS-96 System Evaluation

In this report we describe an experiment designed to: evaluate the performance of the TRAINS-96 system as a whole; examine the utility of a new robust post-parser module, recently added to the TRAINS system; and explore the benefit to the user of receiving system feedback on speech input. The evaluation uses the same task-based methodology as was used for the TRAINS-95 evaluation [Sikorski and Allen 96], in which the user and computer cooperatively solve a given problem. Success is measured in terms of task performance measures such as time to completion of a task, and the quality of the final plan produced

Annotating Argumentation Acts in Spoken Dialog

This manual describes a method for annotating rhetorical relations, adjacency pairs, and other argumentation acts found in task-oriented spoken dialog [Traum, 1993; Traum and Hinkelman, 1992]. It is largely aimed at the novice annotator rather than the computational linguist, and therefore in choosing terminology we have valued intuitiveness over precision. This work came out of an exploration of how to mark structure above the speech act in the Monroe corpus [Stent, 2000 (TN 99-2)]. For more information about the development of this manual, see [Stent, 2000 (INLG)]. This tool is designed for use with ArgumentationTool, a tool for marking argumentation acts in dialog that is available from http://www.cs.rochester.edu/research/cisd/resources/aad/