Speech intention understanding based on decision tree learning
Yuki Irie, Shigeki Matsubara, Nobuo Kawaguchi, Yukiko Yamaguchi, Yasuyoshi Inagaki · 2004
Abstract This paper proposes a method of speech intention understand-ing based on a spoken dialogue corpus to which the intentiontags are given. The intention tag expresses the task-dependentintention of the speaker, and therefore, the proper understand-ing enables a spoken dialogue system to take appropriate ac-tions. We have tagged about 35000 utterances in the CIAIR in-car speech database. In our method, several decision trees forintention understanding are constructed. By constructing deci-sion trees and using them at the same time, the strong amount ofcharacteristic features related to intentions can be retrieved, andit can also be robustly coped with the diversity of the utterances.An experiment on inference of utterance intentions has shown73.1% accuracy. 1. Introduction In order to interact with a user naturally and smoothly, it is nec-essary for a spoken dialogue system to understand the intentionof the user exactly. As a method of speech intention under-standing, example-based approaches have been considered sofar [1, 3, 6]!%In general, these approaches involve comparinga spoken utterance with examples in a correctly-tagged corpus.The intention of the utterance is regarded as the intention tag ofthe most similar example in the corpus. However, it is difficultto infer the intention of the utterance to which any example inthe corpus is not similar.This paper proposes a method of speech intention under-standing based on a spoken dialogue corpus. The method con-structs several decision trees for intention understanding. Byconstructing several decision trees, the strong amount of char-acteristic features related to intentions can be retrieved, and itcan also be robustly coped with the diversity of the utterances.So far, we have designed an organization of the tags which iscalled Layered Intention Tag(LIT). These tags show more de-tailed utterance intention rather than the illocutionary act level,and have built the corpus[2, 3]. LIT is divided into several lay-ers considering the relevance between an intention and variousphenomena relevant to an utterance, such as a style, a keyword,a sentence structure. This method constructs several decisiontrees by using this corpus and infers the intention by combiningthem.In order to evaluate the effectiveness of our method, an ex-periment on inference of the utterance intentions was conductedusing the driver utterances about restaurant search recorded ona large-scale in-car spoken dialogue corpus of CIAIR[4, 5]. Asa result, the effectiveness of the method was confirmed.