Dialogue-Oriented Review Summary Generation for Spoken Dialogue Recommendation Systems

In this paper we present an opinion summarization technique in spoken dialogue systems. Opinion mining has been well studied for years, but very few have considered its application in spoken dialogue systems. Review summarization, when applied to real dialogue systems, is much more complicated than pure text-based summarization. We conduct a systematic study on dialogue-system-oriented review analysis and propose a three-level framework for a recommendation dialogue system. In previous work we have explored a linguistic parsing approach to phrase extraction from reviews. In this paper we will describe an approach using statistical models such as decision trees and SVMs to select the most representative phrases from the extracted phrase set. We will also explain how to generate informative yet concise review summaries for dialogue purposes. Experimental results in the restaurant domain show that the proposed approach using decision tree algorithms achieves an outperformance of 13% compared to SVM models and an improvement of 36% over a heuristic rule baseline. Experiments also show that the decision-tree-based phrase selection model can achieve rather reliable predictions on the phrase label, comparable to human judgment. The proposed statistical approach is based on domain-independent learning features and can be extended to other domains effectively

Eighty Challenges Facing Speech Input/Output Technologies

ABSTRACT During the past three decades, we have witnessed remarkable progress in the development of speech input/output technologies. Despite these successes, we are far from reaching human capabilities of recognizing nearly perfectly the speech spoken by many speakers, under varying acoustic environments, with essentially unrestricted vocabulary. Synthetic speech still sounds stilted and robot-like, lacking in real personality and emotion. There are many challenges that will remain unmet unless we can advance our fundamental understanding of human communication -how speech is produced and perceived, utilizing our innate linguistic competence. This paper outlines some of these challenges, ranging from signal presentation and lexical access to language understanding and multimodal integration, and speculates on how these challenges could be met

Utilizing Review Summarization in a Spoken Recommendation System

In this paper we present a framework for spoken recommendation systems. To provide reliable recommendations to users, we incorporate a review summarization technique which extracts informative opinion summaries from grass-roots users‘ reviews. The dialogue system then utilizes these review summaries to support both quality-based opinion inquiry and feature- specific entity search. We propose a probabilistic language generation approach to automatically creating recommendations in spoken natural language from the text-based opinion summaries. A user study in the restaurant domain shows that the proposed approaches can effectively generate reliable and helpful recommendations in human-computer conversations.T-Party ProjectQuanta Computer (Firm

Speech Communication

Contains reports on three research projects.U.S. Air Force Cambridge Research Laboratories under Contract F19628-72-C-0181National Institutes of Health (Grant 5 RO1 NS04332-09)Joint Services Electronics Programs (U.S. Army, U. S. Navy, and U. S. Air Force) under Contract DAAB07-71-C-0300M. I. T. Lincoln Laboratory Purchase Order CC-57

The MIT Summit Speech Recognition System: A Progress Report

Recently, we initiated a project to develop a phonetically-based spoken language understanding system called SUMMIT. In contrast to many of the past efforts that make use of heuristic rules whose development requires intense knowledge engineering, our approach attempts to express the speech knowledge within a formal framework using well-defined mathematical tools. In our system, features and decision strategies are discovered and trained automatically, using a large body of speech data. This paper describes the system, and documents its current performance

N.b.: A graphical user interface for annotating spoken dialogue

Abstract Corpora of transcribed and annotated dialogues are very useful for developing and evaluating the coverage of algorithms for discourse generation and interpretation and dialogue modelling. On the other hand, there is no agreement on the choice of units and conventions for annotating discourse constituents, and the annotation process can be difficult and prone to inconsistencies. This paper presents N.b., a graphical user interface for annotating the discourse structure of spoken dialogue. Different annotation instructions and different theories about discourse interpretation and generation can be easily incorporated in the annotation process without the need of changing the graphical user interface. The instructions and the annotated text are displayed in a clear-cut way, and typing is reduced to a minimum. We describe how to use N.b. for annotating embedded discourse segments and the system's end-to-end performance in a transcribed dialogue

Speech Communication

Contains reports on four research projects.U. S. Air Force Cambridge Research Laboratories under Contract F19628-69-C-0044National Institutes of Health (Grant 5 RO1 NS 04332-08

Speech Communication

Contains reports on five research projects.C.J. LeBel FellowshipKurzweil Applied IntelligenceNational Institutes of Health (Grant 5 T32 NS07040)National Institutes of Health (Grant 5 R01 NS04332)National Science Foundation (Grant 1ST 80-17599)Systems Development FoundationU.S. Navy - Office of Naval Research (Contract N00014-82-K-0727

Speech Communication

Contains reports on two research projects.U.S. Navy Office of Naval Research (Contract N00014-67-A-0204-0064)National Institutes of Health (Grant 5 ROl NS04332-09)National Science Foundation (Grant GK-31353