An Efficient Statistical Speech Act Type Tagging System for Speech Translation Systems

An Efficient Statistical Speech Act Type Tagging System for Speech Translation Systems Hideki Tanaka and Akio Yokoo ATR Interpreting Telecommunications Research Laboratories 2-2, Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan {t anakah I ayokoo}©itl, atr. co. jp Abstract This paper describes a new efficient speech act type tagging system. This system cov- ers the tasks of (1) segmenting a turn into the optimal number of speech act units (SA units), and (2) assigning a speech act type tag (SA tag) to each SA unit. Our method is based on a theoretically clear statistical model that integrates linguistic, acoustic and situational information. We report tagging experiments on Japanese and English dialogue corpora manually la- beled with SA tags. We then discuss the performance difference between the two languages. We also report on some trans- lation experiments on positive response expressions using SA tags. 1 Introduction This paper describes a statistical speech act type tagging system that utilizes linguistic, acoustic and situational features. This work can be viewed as a study on automatic "Discourse Tagging" whose ob- jective is to assign tags to discourse units in texts or dialogues. Discourse tagging is studied mainly from two different viewpoints, i.e., linguistic and engineer- ing viewpoints. The work described here belongs to the latter group. More specifically, we are interested in automatically recognizing the speech act types of utterances and in applying them to speech transla- tion systems. Several studies on discourse tagging to date have been motivated by engineering applications. The early studies by Nagata and Morimoto (1994) and Reithinger and Maier (1995) showed the possibility of predicting dialogue act tags for next utterances with statistical methods. These studies, however, presupposed properly segmented utterances, which is not a realistic assumption. In contrast to this assumption, automatic utterance segmentation (or discourse segmentation) is desired here. Discourse segmentation in linguistics, whether manual or automatic, has also received keen atten- tion because such segmentation provides the founda- tion of higher discourse structures (Grosz and Sid- net, 1986). Discourse segmentation has also received keen at- tention from the engineering side because the nat- ural language processing systems that follow the speech recognition system are designed to accept lin- guistically meaningful units (Stolcke and Shriberg, 1996). There has been a lot of research following this line such as (Stolcke and Shriberg, 1996) (Cet- tolo and Falavigna, 1998), to only mention a few. We can take advantage of these studies as a pre- process for tagging. In this paper, however, we pro- pose a statistical tagging system that optimally per- forms segmentation and tagging at the same time. Previous studies like (Litman and Passonneau, 1995) have pointed out that the use of a multiple informa- tion source can contribute to better segmentation and tagging, and so our statistical model integrates linguistic, acoustic and situational information. The problem can be formalized as a search prob- lem on a word graph, which can be efficiently han- dled by an extended dynamic programming algo- rithm. Actually, we can efficiently find the optimal solution …

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