Dialogue Act Recognition (DAR) is a challenging problem in dialogue interpretation, which aims to attach semantic labels to utterances and characterize the speaker's intention. Currently, many existing approaches formulate the DAR problem ranging from multi-classification to structured prediction, which suffer from handcrafted feature extensions and attentive contextual structural dependencies. In this paper, we consider the problem of DAR from the viewpoint of extending richer Conditional Random Field (CRF) structural dependencies without abandoning end-to-end training. We incorporate hierarchical semantic inference with memory mechanism on the utterance modeling. We then extend structured attention network to the linear-chain conditional random field layer which takes into account both contextual utterances and corresponding dialogue acts. The extensive experiments on two major benchmark datasets Switchboard Dialogue Act (SWDA) and Meeting Recorder Dialogue Act (MRDA) datasets show that our method achieves better performance than other state-of-the-art solutions to the problem. It is a remarkable fact that our method is nearly close to the human annotator's performance on SWDA within 2 gap.
. @cite_36 present deal with the dialogue act classification using a statistically based language model. @cite_10 apply diverse intra-utterance features involving word n-gram cue phrases to understand the utterance and do the classification. @cite_40 propose a multidimensional approach to distinguish and annotate units in dialogue act segmentation and classification. @cite_18 focus on the dialogue act classification using a Bayesian approach. @cite_12 employ Latent Semantic Analysis (LSA) proper and augmented method to work for dialogue act classification. @cite_25 had an empirical investigation of sparse log-linear models for improved dialogue act classification. @cite_13 investigate a series of compositional distributional semantic models to dialogue act classification.
@cite_13 @cite_18 @cite_36 @cite_40 @cite_10 @cite_25 @cite_12
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We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as STATEMENT, QUESTION, BACKCHANNEL, AGREEMENT, DISAGREEMENT, and APOLOGY. Our model detects and predicts dialogue acts based on lexical, collocational, and prosodic cues, as well as on the discourse coherence of the dialogue act sequence. The dialogue model is based on treating the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. Constraints on the likely sequence of dialogue acts are modeled via a dialogue act n-gram. The statistical dialogue grammar is combined with word n-grams, decision trees, and neural networks modeling the idiosyncratic lexical and prosodic manifestations of each dialogue act. We develop a probabilistic integration of speech recognition with dialogue modeling, to improve both speech recognition and dialogue act classification accuracy. Models are trained and evaluated using a large hand-labeled database of 1,155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We achieved good dialogue act labeling accuracy (65 based on errorful, automatically recognized words and prosody, and 71 based on word transcripts, compared to a chance baseline accuracy of 35 and human accuracy of 84 ) and a small reduction in word recognition error. This paper presents our experiments in applying Latent Semantic Analysis (LSA) to dialogue act classification. We employ both LSA proper and LSA augmented in two ways. We report results on DIAG, our own corpus of tutoring dialogues, and on the CallHome Spanish corpus. Our work has the theoretical goal of assessing whether LSA, an approach based only on raw text, can be improved by using additional features of the text. In this paper we present a multidimensional approach to utterance segmentation and automatic dialogue act classification. We show that the use of multiple dimensions in distinguishing and annotating units not only supports a more accurate analysis of human communication, but can also help to solve some notorious problems concerning the segmentation of dialogue into functional units. We introduce the use of per-dimension segmentation for dialogue act taxonomies that feature multi-functionality and show that better classification results are obtained when using a separate segmentation for each dimension than when using one segmentation that fits all dimensions. Three machine learning techniques are applied and compared on the task of automatic classification of multiple communicative functions of utterances. The results are encouraging and indicate that communicative functions in important dimensions are easy machinelearnable. We present recent work in the area of Dialogue Act (DA) tagging. Identifying the dialogue acts of utterances is recognised as an important step towards understanding the content and nature of what speakers say. Our experiments investigate the use of a simple dialogue act classifier based on purely intra-utterance features — principally involving word n-gram cue phrases. Such a classifier performs surprisingly well, rivalling scores obtained using far more sophisticated language modelling techniques for the corpus we address. We also discuss the potential utility of classifiers that identify the n most likely dialogue acts for each utterance, leaving it to some later process to choose amongst these alternatives.