Dialogue Act Classification, Higher Order Dialogue Structure, and Instance-Based Learning
نویسندگان
چکیده
The main goal of this paper is to explore the predictive power of dialogue context on Dialogue Act classification, both as concerns the linear context provided by previous dialogue acts, and the hierarchical context specified by conversational games. As our learning approach, we extend Latent Semantic Analysis (LSA) as Feature LSA (FLSA), and combine FLSA with the k-Nearest Neighbor algorithm. FLSA adds richer linguistic features to LSA, which only uses words. The k-Nearest Neighbor algorithm obtains its best results when applied to the reduced semantic spaces generated by FLSA. Empirically, our results are better than previously published results on two different corpora, MapTask and CallHome Spanish. Linguistically, we confirm and extend previous observations that the hierarchical dialogue structure encoded via the notion of game is of primary importance for dialogue act recognition.
منابع مشابه
Machine Learning for Shallow Interpretation of User Utterances in Spoken Dialogue Systems
We investigate to what extent automatic learning techniques can be used for shallow interpretation of user utterances in spoken dialogue systems. This task involves dialogue act classification, shallow understanding and problem detection simultaneously. For this purpose we train both a rule-induction and a memory-based learning algorithm on a large set of surface features obtained by affordable...
متن کاملPreliminary Results On Dialogue Act and Subact Classification in Chat-based Online Tutorial Dialogues
We present in this paper preliminary results with dialogue act classification in human-to-human tutorial dialogues. Dialogue acts are ways to characterize the intentions and actions of the speakers in dialogues based on the language-as-action theory. This work serves our larger goal of identifying patterns of tutors’ actions, in the form of dialogue act and subact sequences, that relate to vari...
متن کاملUnderstanding Student Language: An Unsupervised Dialogue Act Classification Approach
Within the landscape of educational data, textual natural language is an increasingly vast source of learning-centered interactions. In natural language dialogue, student contributions hold important information about knowledge and goals. Automatically modeling the dialogue act of these student utterances is crucial for scaling natural language understanding of educational dialogues. Automatic ...
متن کاملAutomatic Utterance Segmentation in Instant Messaging Dialogue
Instant Messaging (IM) chat sessions are real-time, text-based conversations which can be analyzed using dialogue-act models. Dialogue acts represent the semantic information of an utterance, however, messages must be segmented into utterances before classification can take place. We describe and compare two statistical methods for automatic utterance segmentation and dialogue-act classificatio...
متن کاملA Tutorial Dialogue System for Real-Time Evaluation of Unsupervised Dialogue Act Classifiers: Exploring System Outcomes
Dialogue act classification is an important step in understanding students’ utterances within tutorial dialogue systems. Machinelearned models of dialogue act classification hold great promise, and among these, unsupervised dialogue act classifiers have the great benefit of eliminating the human dialogue act annotation effort required to label corpora. In contrast to traditional evaluation appr...
متن کامل