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_26 treat the discourse structure of a conversation as a hidden Markov model and the individual dialogue acts as observations emanating from the model states. @cite_3 study the effectiveness of supervised learning algorithms SVM-HMM for DA modeling across a comprehensive set of conversations. Similar to the SVM-HMM, @cite_27 also use a combination of linear support vector machines and hidden markov models for dialog act tagging in the HCRC MapTask corpus. @cite_35 explore two sequence learners with a memory-based tagger and conditional random fields into turn-internal DA chunks. @cite_19 also applied HMM to discover internal dialogue strategies inherent in the structure of the sequenced dialogue acts. @cite_17 use skip-chain conditional random field to model non-local pragmatic dependencies between paired utterances. @cite_28 investigate the use of conditional random fields for joint segmentation and classification of dialog acts exploiting both word and prosodic features.
@cite_35 @cite_26 @cite_28 @cite_3 @cite_19 @cite_27 @cite_17
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We use a combination of linear support vector machines and hidden markov models for dialog act tagging in the HCRC MapTask corpus, and obtain better results than those previously reported. Support vector machines allow easy integration of sparse highdimensional text features and dense low-dimensional acoustic features, and produce posterior probabilities usable by sequence labelling algorithms. The relative contribution of text and acoustic features for each class of dialog act is analyzed. Index Terms: dialog acts, discourse, support vector machines, classification probabilities, Viterbi algorithm. Probabilistic graphical models, such as partially observable Markov decision processes (POMDPs), have been used in stochastic spoken dialog systems to handle the inherent uncertainty in speech recognition and language understanding. Such dialog systems suffer from the fact that only a relatively small number of domain variables are allowed in the model, so as to ensure the generation of good-quality dialog policies. At the same time, the non-language perception modalities on robots, such as vision-based facial expression recognition and Lidar-based distance detection, can hardly be integrated into this process. In this paper, we use a probabilistic commonsense reasoner to “guide” our POMDP-based dialog manager, and present a principled, multimodal dialog management (MDM) framework that allows the robot's dialog belief state to be seamlessly updated by both observations of human spoken language, and exogenous events such as the change of human facial expressions. The MDM approach has been implemented and evaluated both in simulation and on a real mobile robot using guidance tasks. 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 proposes a novel approach for predicting user satisfaction transitions during a dialogue only from the ratings given to entire dialogues, with the aim of reducing the cost of creating reference ratings for utterances dialogue-acts that have been necessary in conventional approaches. In our approach, we first train hidden Markov models (HMMs) of dialogue-act sequences associated with each overall rating. Then, we combine such rating-related HMMs into a single HMM to decode a sequence of dialogue-acts into state sequences representing to which overall rating each dialogue-act is most related, which leads to our rating predictions. Experimental results in two dialogue domains show that our approach can make reasonable predictions; it significantly outperforms a baseline and nears the upper bound of a supervised approach in some evaluation criteria. We also show that introducing states that represent dialogue-act sequences that occur commonly in all ratings into an HMM significantly improves prediction accuracy.