Learning dialog act processing

In this paper we describe a new approach for learning dialog act processing. In this approach we integrate a symbolic semantic segmentation parse,: with a learning dialog act network. In order to support the unforeseeable errors and variations of spoken language we have concentrated on robust data-driven learning. This approach already compares favorably with the statistical average plansibility method, produces a segmentation and dialog act assignment for all utteranccs in a robust manner, and redaces knowledge engineering since it can be bootstrapped from rather small corpora. Therefore, we consider this new approach as very promising for learning dialog act processing. 1 I n t r o d u c t i o n For several decades, the pragmatic interpretation at a dialog act level belongs to the most difficult and challenging tasks tbr natural language processing and computational linguistics (Austin, 1962; Searle, 1969; Wilks, 1985). Recently, we can see an important development in natural language processing and computational linguistics towards the use of empirical learning methods (for instance, (Charniak, 1993; Marcus et al., 1993; Wermter, 11995; Jones, 1995; Werml;er et al., 1996)). Primarily, new learning approaches have been successful for leo~'ically or syntactically tagged text corpora. In this paper we want to examine the potential of learning techniques at highcr pragmatic dialog levels of spoken language. Learning at least part of the dialog knowledge is desirable since it could reduce the knowledge engineering effort. Furthermore, inductive learning algorithms work in a data-driven mode and have the ability to extract gradual regularities in a robust manner. This robustness is particularly important for processing spoken language since spoken language can contain constructions including interjections, pauses, corrections, repetitions, false starts, semantically or syntactically incorrect constructions, etc. Tile use of learning is a new approach at the level of dialog acts and only recently, there have been some learning approaches for dialog knowledge (Mast et al., 1996; Alexanderson et al., 1995; Reithinger and Maier, 1995; Wang and Waibel, 1995). Different from these approaches, in this paper we examine the combination of learning techniques in simple recurrent networks with symbolic segmentation parsing at a dialog act level. Input to our dialog component are utterances h'om a corpus of business meeting arrangements like: "Tuesday at 10 is for me now again bad because I there still train I think we should [delay] the whole then really to the next week is this for you possible" 1. For a fiat level of dialog act processing, the incrementM output is (1) utterance boundaries within a dialog turn and (2) the specific dialog act within an utterance. The paper is structured as follows: First we will outline the domain and task and we will illustrate the dialog act categories. Then, we will describe the overall architecture of the dialog component in the SCREEN system (Symbolic Connectionist Robust Enterprise for Natural language), consisting of the segmentation parser and the dialog act network. We will describe the learning and generalization results for this dialog component and we will point out contributions and further work. l'Phis is ahnost a literal translation of the Germau utterance: "l)ienstags um zehn ist bei mir nun wiederum schlecht weft ich da noch trainieren bin ich denke wir sollten das Ganze dann doch auf die niichste Woche verschieben geht es bei ihnen da." We have chosen the literal word-by-word trauslation since our processing is incremental and knowledge about the order of the German words matter for processing.