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

This paper describes a new efficient speech act type tagging system. This system covers 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 labeled with SA tags. We then discuss the performance difference between the two languages. We also report on some translation experiments on positive response expressions using SA tags. 1 I n t r o d u c t i o n 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 objective is to assign tags to discourse units in texts or dialogues. Discourse tagging is studied mainly from two different viewpoints, i.e., linguistic and engineering 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 translation 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 attention because such segmentation provides the foundation of higher discourse structures (Grosz and Sidnet, 1986). Discourse segmentation has also received keen attention from the engineering side because the natural language processing systems that follow the speech recognition system are designed to accept linguistically meaningful units (Stolcke and Shriberg, 1996). There has been a lot of research following this line such as (Stolcke and Shriberg, 1996) (Cettolo and Falavigna, 1998), to only mention a few. We can take advantage of these studies as a preprocess for tagging. In this paper, however, we propose a statistical tagging system that optimally performs segmentation and tagging at the same time. Previous studies like (Litman and Passonneau, 1995) have pointed out that the use of a multiple information 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 problem on a word graph, which can be efficiently handled by an extended dynamic programming algorithm. Actually, we can efficiently find the optimal solution without limiting the search space at all. The results of our tagging experiments involving both Japanese and English corpora indicated a high performance for Japanese but a considerably lower performance for the English corpora. This work also reports on the use of speech act type tags for translating Japanese and English positive response expressions. Positive responses quite often appear in task-oriented dialogues like those in our tasks. They are often highly ambiguous and problematic in speech translation. We will show that these expressions can be effectively translated with the help of dialogue information, which we call speech act type tags. 2 T h e P r o b l e m s In this section, we briefly explain our speech act type tags and the tagged data and then formally define the tagging problem.