Dialog act tagging with support vector machines and hidden Markov models

Surendran, D.; Levow, Gina‐Anne

doi:10.21437/interspeech.2006-535

Cited by 57 publications

(11 citation statements)

References 17 publications

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“…Reference [9] mentioned that the sequence of dialogue acts should be constrained via a dialog-act based n-gram. [10] used a combination of SVM and HMM for dialog-act tagging, and obtained better result than those previously reported.…”

Section: Related Workmentioning

confidence: 78%

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Predicting and Tagging Dialog-act Using MDP and SVM

Zhou,

Zong,

et al. 2008

Int. Symp. On Chinese Spoken Language Processing

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Dialog-act tagging is one of the hot topics in processing human-human conversation. In this paper, we introduce a novel model to predict and tag the dialog-act, in which Markov Decision Process (MDP) is utilized to predict the dialog-act sequence instead of using traditional dialog-act based n-gram, and Support Vector Machine (SVM) is employed to classify the dialog-act for each utterance. The predicting result of MDP and the classifying result of SVM are integrated as the final tagging. The experimental results have shown that our approach outperforms the traditional method.

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Section: Related Workmentioning

confidence: 78%

“…In the recently years, new techniques on classifier have been used successfully. For example, SVM is used in [10], and graphical model is used in [4]. However, most of them can not incorporate contextual information.…”

Section: Related Workmentioning

confidence: 99%

Predicting and Tagging Dialog-act Using MDP and SVM

Zhou,

Zong,

et al. 2008

Int. Symp. On Chinese Spoken Language Processing

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“…But both frequency-based and machine learning approaches tend to rely more on an instance-based than a sequence-based approach. Frequency-based and machine learning models that make use of a sequence-based approach make use of Hidden Markov Models and dynamic programming algorithms to optimize the sequence of predicted dialog acts (Ji & Bilmes, 2005, 2006Stolcke et al, 2000;Surendran & Levow, 2006). Sequence-based deep learning approaches typically encode context by connecting the utterances in a neural network structure (e.g., Colombo et al, 2020;Chen et al, 2018;Kumar et al, 2018;Raheja & Tetreault, 2019).…”

Section: Instance-based Versus Sequence-basedmentioning

confidence: 99%

Surface and Contextual Linguistic Cues in Dialog Act Classification: A Cognitive Science View

Linders,

Louwerse

2023

Cognitive Science

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What role do linguistic cues on a surface and contextual level have in identifying the intention behind an utterance? Drawing on the wealth of studies and corpora from the computational task of dialog act classification, we studied this question from a cognitive science perspective. We first reviewed the role of linguistic cues in dialog act classification studies that evaluated model performance on three of the most commonly used English dialog act corpora. Findings show that frequency‐based, machine learning, and deep learning methods all yield similar performance. Classification accuracies, moreover, generally do not explain which specific cues yield high performance. Using a cognitive science approach, in two analyses, we systematically investigated the role of cues in the surface structure of the utterance and cues of the surrounding context individually and combined. By comparing the explained variance, rather than the prediction accuracy of these cues in a logistic regression model, we found that (1) while surface and contextual linguistic cues can complement each other, surface linguistic cues form the backbone in human dialog act identification, (2) with word frequency statistics being particularly important for the dialog act, and (3) the similar trends across corpora, despite differences in the type of dialog, corpus setup, and dialog act tagset. The importance of surface linguistic cues in dialog act classification sheds light on how both computers and humans take advantage of these cues in speech act recognition.

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“…Previous studies discuss several methods for automated DA classification. Early DA classification models were traditional machine learning (ML) methods such as support vector machines [42,43], sometimes combined with hidden Markov models [44], Bayesian network-based classifiers [45,46], decision trees [47], and more [48]. Recently, solutions for DA classification involve methods in deep learning.…”

Section: Related Workmentioning

confidence: 99%

Dialogue Act Classification via Transfer Learning for Automated Labeling of Interviewee Responses in Virtual Reality Job Interview Training Platforms for Autistic Individuals

Adiani

Colopietro

Wade

et al. 2023

Signals

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Computer-based job interview training, including virtual reality (VR) simulations, have gained popularity in recent years to support and aid autistic individuals, who face significant challenges and barriers in finding and maintaining employment. Although popular, these training systems often fail to resemble the complexity and dynamism of the employment interview, as the dialogue management for the virtual conversation agent either relies on choosing from a menu of prespecified answers, or dialogue processing is based on keyword extraction from the transcribed speech of the interviewee, which depends on the interview script. We address this limitation through automated dialogue act classification via transfer learning. This allows for recognizing intent from user speech, independent of the domain of the interview. We also redress the lack of training data for a domain general job interview dialogue act classifier by providing an original dataset with responses to interview questions within a virtual job interview platform from 22 autistic participants. Participants’ responses to a customized interview script were transcribed to text and annotated according to a custom 13-class dialogue act scheme. The best classifier was a fine-tuned bidirectional encoder representations from transformers (BERT) model, with an f1-score of 87%.

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Dialog act tagging with support vector machines and hidden Markov models

Cited by 57 publications

References 17 publications

Predicting and Tagging Dialog-act Using MDP and SVM

Predicting and Tagging Dialog-act Using MDP and SVM

Surface and Contextual Linguistic Cues in Dialog Act Classification: A Cognitive Science View

Dialogue Act Classification via Transfer Learning for Automated Labeling of Interviewee Responses in Virtual Reality Job Interview Training Platforms for Autistic Individuals

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