Analysis and classification of cooperative and competitive dialogs (original) (raw)

A statistical signature for automatic dialogue classification

2008

In the last few years, there has been a certain attention to the problem of human-human communication, trying to devise artificial systems able to mediate a conversational setting between two or more people. In this paper, we designed an automatic system based on a generative structure able to classify hard dialog acts. The generative model is composed by integrating a hierarchical Gaussian mixture model and the Influence Model, originating a brand new method able to deal with such difficult scenarios. The method has been tested on a set of conversational settings involving dialogues between adults and children and adults, in flat and arguing discussions, proving very accurate classification results.

Robust Classification of Dialog Acts from the Transcription of Utterances

International Conference on Semantic Computing (ICSC 2007), 2007

This paper presents a robust classification of dialog acts from text utterances. Two different types, namely, bag-of-words and syntactic relationship among words, were used to extract the discourse level features from the transcript of utterances. Subsequently a number of feature mining methods have been used to identify the most relevant features and their roles in classifying dialog acts. The selected features are used to learn the underlying models of dialog acts using a number of existing machine learning algorithms from the WEKA toolbox. Empirical analyses using the HCRC Map Task Corpus dialog data was conducted to evaluate the performance of the proposed approach.

Generative modeling and classification of dialogs by a low-level turn-taking feature

Pattern Recognition, 2011

In the last few years, a growing attention has been paid to the problem of human–human communication, trying to devise artificial systems able to mediate a conversational setting between two or more people. In this paper, we propose an automatic system based on a generative structure able to classify dialog scenarios. The generative model is composed by integrating a Gaussian mixture model and a (observed) Markovian influence model, and it is fed with a novel low-level acoustic feature termed steady conversational period (SCP). SCPs are built on duration of continuous slots of silence or speech, taking also into account conversational turn-taking. The interactional dynamics built upon the transitions among SCPs provides a behavioral blueprint of conversational settings without relying on segmental or continuous phonetic features, and may be important for predicting the evolution of typical conversational situations in different dialog scenarios. The model has been tested on an extensive set of real, dyadic and multi-person conversational settings, including a recent dyadic dataset and the AMI meeting corpus. Comparative tests are made using conventional acoustic features and classification methods, showing that the proposed scheme provides superior classification performances for all conversational settings in our datasets. Moreover, we prove that our approach is able to characterize the nature of multi-person conversation (namely, the role of the participants) in a very accurate way, thus demonstrating great versatility.► Generative models based on Markov models can explain the turn-taking of a dialog. ► Novel features are proposed, named steady conversational periods (SCPs). ► SCPs enrich the capability of a Markov framework in explaining the turn-taking. ► Different classes of dialogs can be analyzed exploiting SCPs.

Text Based Dialog Act Classification for Multiparty Meetings

Lecture Notes in Computer Science, 2006

This paper compares the performance of various machine learning approaches and their combination for dialog act (DA) classification of meetings data. For this task, boosting and three other text based approaches previously described in the literature are used. To further improve the classification performance, various combination schemes take into account the results of the individual classifiers. All classification methods are evaluated on the ICSI Meeting Corpus based on both reference transcripts and the output of a speech-to-text (STT) system. The results indicate that both the proposed boosting based approach and a method relying on maximum entropy substantially outperform the use of mini language models and a simple scheme relying on cue phrases. The best performance was achieved by combining methods with a multilayer perceptron.

Toward an Automatic Classification of Negotiation Styles Using Natural Language Processing

Lecture Notes in Computer Science, 2017

We present a natural language processing model that allows automatic classification and prediction of the user's negotiation style during the interaction with virtual humans in a 3D game. We collected the sentences used in the interactions of the users with virtual artificial agents and their associated negotiation style as measured by ROCI-II test. We analyzed the documents containing the sentences for each style applying text mining techniques and found statistical differences among the styles in agreement with their theoretical definitions. Finally, we trained two machine learning classifiers on the two datasets using pre-trained Word2Vec embeddings.

Classification of modal meaning in negotiation dialogues

2017

This paper addresses modality classification for multi-issue bargaining dialogues in order to model human-like negotiation behaviour and to efficiently compute negotiation strategies. We propose a modality annotation and classification scheme comprising semantically distinguishable categories applied reliably by humans and machines. Our classification of modality varieties is based on both the traditional dichotomy of epistemic and root modalities, and on the analysis of the available corpus data. Our modality scheme has been used for annotating human-human dialogues and training SVM-based classifiers. We built predictive models that show accuracies in the range between 73.3 and 82.6%.

Transition-Relevance Places Machine Learning-Based Detection in Dialogue Interactions

Elektronika Ir Elektrotechnika, 2023

A transition-relevance place (TRP) represents a place in a conversation where a change of speaker can occur. The appearance and use of these points in the dialogue ensures a correct and smooth alternation between the speakers. In the presented article, we focused on the study of prosodic speech parameters in the Slovak language, and we tried to experimentally verify the potential of these parameters to detect TRP. To study turn-taking issues in dyadic conversations, the Slovak dialogue corpus was collected and annotated. TRP places were identified by the human annotator in the manual labelling process. The data were then divided into chunks that reflect the length of the interpausal dialogue units and the prosodic features were computed. In the Matlab environment, we compared different types of classifiers based on machine learning in the role of an automatic TRP detector based on pitch and intensity parameters. The achieved results indicate that prosodic parameters can be useful in detecting TRP after splitting the dialogue into interpausal units. The designed approach can serve as a tool for automatic conversational analysis or can be used to label large databases for training predictive models, which can help machines to enhance human-machine spoken dialogue applications.

Automatic Detection of Dialog Acts Based on Multi-level Information Ý

Recently there has been growing interest in using dialog acts to characterize human-human and human-machine dialogs. This paper reports on our experience in the annotation and the automatic detection of dialog acts in human-human spoken dialog corpora. Our work is based on two hypotheses: first, word position is more important than the exact word in identifying the dialog act; and second, there is a strong grammar constraining the sequence of dialog acts. A memory based learning approach has been used to detect dialog acts. In a first set of experiments the number of utterances per turn is known, and in a second set, the number of utterances is hypothesized using a language model for utterance boundary detection. In order to verify our first hypothesis, the model trained on a French corpus was tested on a corpus for a similar task in English and for a second French corpus from a different domain. A correct dialog act detection rate of about 84% is obtained for the same domain and language condition and about 75% for the cross-language and cross-domain conditions.

Annotations for dynamic diagnosis of the dialog state

2002

This paper describes recent work aimed at relating multi-level dialog annotations with meta-data annotations for a corpus of real humanhuman dialogs. This work is carried out in the context of the AMITIES project in which spoken dialog systems for call center services are being developed. A corpus of 100 agent-client dialogs have been annotated with three types of annotations. The first are utterance-level DAMSL-style dialogic labels. The second set of annotations applies to exchanges and takes into account of the dynamic aspect of dialog progress. Finally, 5 emotions types are annotated at the utterance level. Some of these multi-style annotations were used in a multiple linear regression analysis to predict dialog quality. The predictive factors are able to explain about 80% of the dialog accidents.

Modeling and evaluating dialog success in the LAST MINUTE corpus

2014

The LAST MINUTE corpus comprises records and transcripts of naturalistic problem solving dialogs between N = 130 subjects and a companion system simulated in a Wizard of Oz experiment. Our goal is to detect dialog situations where subjects might break up the dialog with the system which might happen when the subject is unsuccessful. We present a dialog act based representation of the dialog courses in the problem solving phase of the experiment and propose and evaluate measures for dialog success or failure derived from this representation. This dialog act representation refines our previous coarse measure as it enables the correct classification of many dialog sequences that were ambiguous before. The dialog act representation is useful for the identification of different subject groups and the exploration of interesting dialog courses in the corpus. We find young females to be most successful in the challenging last part of the problem solving phase and young subjects to have the ...