Evaluation of dialogue act recognition approaches

Král, Pavel; Pavelka, Tomáš; Cerisara, Christophe

doi:10.1109/mlsp.2008.4685529

Cited by 16 publications

(20 citation statements)

References 17 publications

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“…Note, that in this work, only texts are extracted, and the audio recordings are discarded for the time being. We have indeed shown in previous works (Král et al, 2006), (Král et al, 2007), (Král et al, 2008) that textual transcriptions are the most informative features for dialog act recognition, which justifies this choice as a first approximation.…”

Section: Introductionsupporting

confidence: 71%

Automatic Dialog Act Corpus Creation From Web Pages

Král

Cerisara

2010

Proceedings of the 12th International Conference on Enterprise Information Systems

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Abstract:This work presents two complementary tools dedicated to the task of textual corpus creation for linguistic researches. The chosen application domain is automatic dialog acts recognition, but the proposed tools might also be applied to any other research area that is concerned with dialogs processing. The first software captures relevant dialogs from freely available resources on the World Wide Web. Filtering and parsing of these web pages is realized thanks to a set of hand-crafted rules. A second set of rules is then applied to achieve automatic segmentation and dialog act tagging. The second software is finally used as a post-processing step to manually check and correct tagging errors when needed. In this paper, both softwares are presented, and the performances of automatic tagging are evaluated on a dialog corpus extracted from an online Czech journal. We show that reasonably good dialog act labeling accuracy may be achieved, hence greatly reducing the cost of building such corpora.

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Section: Introductionsupporting

confidence: 71%

Automatic Dialog Act Corpus Creation From Web Pages

Král

Cerisara

2010

Proceedings of the 12th International Conference on Enterprise Information Systems

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“…Other dialogue acts include: question, thank, introduce, suggest, feedback, confirm and motivate [30]. Based on detailed analysis of extensive annotated datasets, some dialogue act tag-sets have emerged as pseudo-standards in this area [31]. These large annotated datasets and tag sets are used to train classifiers that can distinguish between different dialogue acts.…”

Section: Automatic Detection Of Exploratory Dialoguementioning

confidence: 99%

An evaluation of learning analytics to identify exploratory dialogue in online discussions

Ferguson

Wei

et al. 2013

Proceedings of the Third International Conference on Learning Analytics and Knowledge

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Social learning analytics are concerned with the process of knowledge construction as learners build knowledge together in their social and cultural environments. One of the most important tools employed during this process is language. In this paper we take exploratory dialogue, a joint form of co-reasoning, to be an external indicator that learning is taking place. Using techniques developed within the field of computational linguistics, we build on previous work using cue phrases to identify exploratory dialogue within online discussion. Automatic detection of this type of dialogue is framed as a binary classification task that labels each contribution to an online discussion as exploratory or non-exploratory. We describe the development of a self-training framework that employs discourse features and topical features for classification by integrating both cue-phrase matching and k-nearest neighbour classification. Experiments with a corpus constructed from the archive of a two-day online conference show that our proposed framework outperforms other approaches. A classifier developed using the self-training framework is able to make useful distinctions between the learning dialogue taking place at different times within an online conference as well as between the contributions of individual participants.

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“…For example, adding a feature to indicate whether the speaker of an utterance is the same as the previous speaker or not, while coding at the whole transcript level. Methods to apply dialogue acts (Austin, 1975) as labels -labels indicating the type of "move" being made -(see for example, Erkens & Janssen, 2008;Král & Cerisara, 2012;Stolcke et al, 2000) -would also fall into this category. This is arguably not dissimilar to taking individual quotations from a transcript and providing some contextual information (although it is unusual for this to be formalized in the way feature selection is).…”

Section: Operationalizing Our Feature and Segmentation Level Represenmentioning

confidence: 99%

“…In those cases, though, where analysis is automated (in "Epistemic Games"), the dialogue is structured to facilitate identification of topically related talk in order to simplify the segmentation process. Other means through which segments may be identified include analysis of dialogue acts, (see, Erkens & Janssen, 2008;Král & Cerisara, 2012;Stolcke et al, 2000) through grammatical features indicating an exchange and shift to a new exchange, and more broadly than that analysis of "break points" in an exchange or topic indicative of new sections or types of dialogue (see for example, Chiu, 2008). In each case, the use of tokenizing for grammatical features (for example, through the use of the Part of Speech Taggers), and lexical tools for identification of topics, are important -both of these are feature selection tools, and again we see the interplay of features and segmentation -feature selection can be used to provide the means through which to segment, in order to identify regularities in features across segments.…”

Section: Operationalizing Our Feature and Segmentation Level Represenmentioning

confidence: 99%

Dialogue as Data in Learning Analytics for Productive Educational Dialogue

Knight

Littleton

2016

Learning Analytics

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This paper provides a novel, conceptually driven stance on the state of the contemporary analytic challenges faced in the treatment of dialogue as a form of data across on-and offline sites of learning. In prior research, preliminary steps have been taken to detect occurrences of such dialogue using automated analysis techniques. Such advances have the potential to foster effective dialogue using learning analytic techniques that scaffold, give feedback on, and provide pedagogic contexts promoting such dialogue. However, the translation of much prior learning science research to online contexts is complex, requiring the operationalization of constructs theorized in different contexts (often face-to-face), and based on different datasets and structures (often spoken dialogue). In this paper, we explore what could constitute the effective analysis of productive online dialogues, arguing that it requires consideration of three key facets of the dialogue: features indicative of productive dialogue; the unit of segmentation; and the interplay of features and segmentation with the temporal underpinning of learning contexts. The paper thus foregrounds key considerations regarding the analysis of dialogue data in emerging learning analytics environments, both for learning-science and for computationally oriented researchers.

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Evaluation of dialogue act recognition approaches

Cited by 16 publications

References 17 publications

Automatic Dialog Act Corpus Creation From Web Pages

Automatic Dialog Act Corpus Creation From Web Pages

An evaluation of learning analytics to identify exploratory dialogue in online discussions

Dialogue as Data in Learning Analytics for Productive Educational Dialogue

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