Stochastic Representation of Conceptual Structure in the ATIS Task

STOCHASTIC REPRESENTATION OF CONCEPTUAL STRUCTURE IN THE ATIS TASK Roberto Pieraccini, Esther Levin, Chin-Hui Lee AT&T Bell Laboratories 600 Mountain Avenue Murray Hill, N J, 07974 ABSTRACT We propose a model for a statistical representation of the conceptual structure in a restricted subset of spoken natural language. The model is used for segmenting a sentence into phrases and labeling them with concept relations (or cases). The model is trained using a corpus of annotated transcribed sentences. The performance of the model was assessed on two tasks, including DARPA ATIS class A sentences. INTRODUCTION The goal of a speech understanding system is generally that of translating a sequence of acoustic measurements of the speech sig- nal into some form that represents the meaning conveyed by the sentence. One of the knowledge representation paradigms, known as semantic networks [2] establishes relations between conceptual entities using a graph structure. These concept relations, or lin- guistic cases, can be used to label different parts of a sentence in order to obtain its interpretation. The task itself defines the set of relevant cases. For instance, for the task of assigning the origin, the destination and the departure time of a flight, a con- venient representation is in terms of the following set of cases: C = {01,C2, C3, C4}, where Cl = ORIGIN, C2 = DESTINATION, 03 = DEPARTURE_TIME, and C4 = DUMMY. The introduction of a DUMMY case is useful for covering all the parts of the sen- tence that are not relevant to the task. A sentence like ! would like to fly from Boston to Chicago next Saturday night can be analyzed as: • DUMMY: I would like to fly • ORIGIN: from Boston • DESTINATION: to Chicago • DEPARTURE_TIME: next Saturday night. Note that although the first phrase (I would like to fly) conveys important information, it is considered irrelevant to this partic- ular task, and therefore assigned to the DUMMY case. The segmentation of a sentence into cases (conceptual segmentation) can be described by labeling each word in the sentence with the index of the case it expresses. In the example above, the con- ceptuM segmentation is represented by the following sequence of labels: C = (cI,c2, c3...c12) (1) where: el = c2 ..... e5 = 04 (2) e 6 = c? = C1 c 8 ~ c 9 z C 2 In this paper we tackle the problem of decoding the words con- stituting the spoken sentence and the corresponding sequence of case labels, from the speech signal. MAP DECODING OF CASES Let us denote by A = a,,a2...aN, (3) the sequence of acoustic observations extracted from a spoken sentence, by W = ~,,w2...WM, (4) the sequence of words constituting the sentence, and by C = cl,c2...CM, (5) the sequence of case labels, where ci takes its values from a pre- defined set of conceptual relations C = {C1,C2,...CK}. The problem of finding W and C given A can be approached using the maximum a posteriori decoding (MAP). Following …

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