Automatic analysis of multiparty meetings

Renals, Steve

doi:10.1007/s12046-011-0051-3

Cited by 3 publications

(3 citation statements)

References 53 publications

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“…Before using automatically calculated scores of discussions as a proxy for other reading ability tests, in the future researchers should (a) determine how quantitative discourse features compare to other validated reading tests beyond AIMSweb R‐CBM scores, (b) broaden the sample to include more participants across multiple grade levels, (c) analyze quantitative features in the context of literacy discussions beyond Quality Talk, and (d) develop a method for handling dependency of talk within a group, including exploring discourse features applicable to multiparty interactions (Renals, ; Song & McNary, ).…”

Section: Resultsmentioning

confidence: 99%

“…Moving beyond manual annotations, other scholars have used automated methods to analyze oral meetings. Renals () investigated automatic analysis of meetings with multiple parties, including methods of automatically summarizing meeting content. Germesin and Wilson () worked to automatically identify cases when an individual agreed with a statement made by another person in the group by applying machine learning to various features of meetings, such as the types of words used, the timing and duration of speech, and the structure of combinations of features across an entire meeting.…”

Section: Theoretical Foundations Relating Classroom Discussion To Reamentioning

confidence: 99%

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Automated Scoring of Students’ Small‐Group Discussions to Assess Reading Ability

Kosh

Greene

Murphy

et al. 2017

Educational Measurement

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We explored the feasibility of using automated scoring to assess upper‐elementary students’ reading ability through analysis of transcripts of students’ small‐group discussions about texts. Participants included 35 fourth‐grade students across two classrooms that engaged in a literacy intervention called Quality Talk. During the course of one school year, data were collected at 10 time points for a total of 327 student‐text encounters, with a different text discussed at each time point. To explore the possibility of automated scoring, we considered which quantitative discourse variables (e.g., variables to measure language sophistication and latent semantic analysis variables) were the strongest predictors of scores on a multiple‐choice and constructed‐response reading comprehension test. Convergent validity evidence was collected by comparing automatically calculated quantitative discourse features to scores on a reading fluency test. After examining a variety of discourse features using multilevel modeling, results showed that measures of word rareness and word diversity were the most promising variables to use in automated scoring of students’ discussions.

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Section: Resultsmentioning

confidence: 99%

Section: Theoretical Foundations Relating Classroom Discussion To Reamentioning

confidence: 99%

Automated Scoring of Students’ Small‐Group Discussions to Assess Reading Ability

Kosh

Greene

Murphy

et al. 2017

Educational Measurement

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“…Some of the tasks that have been addressed include speech recognition in meetings, addressee identification, dialogue segmentation, meeting summarization, and automatic detection of agreements and disagreements. Renals (2011) gives an overview of research carried out using the AMI Meeting Corpus and provides many references to other studies of multi-party meetings.…”

Section: Multi-party Dialoguementioning

confidence: 99%

Dialogue

Fernández¹

2014

The Oxford Handbook of Computational Linguistics 2nd Edition

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Research on dialogue deals with the study of language as it is used in spontaneous conversation. Dialogue is a multi-agent activity that takes place in real time, with speakers interacting with each other in an online fashion. This makes conversational language markedly different from the kind of language found in texts and brings in new challenges for computational linguistics. This chapter introduces the main phenomena that characterize language in dialogue interaction—including disfluencies, dialogue acts, alignment, grounding, and turn taking—and discusses some of the key approaches to modelling dialogue that are fundamental in computational research, such as dialogue act taxonomies and dynamic semantic theories of dialogue.

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Evidential Reasoning and Learning: a Survey

Feng

Qin

2022

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

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Dialogue summarization aims to condense the original dialogue into a shorter version covering salient information, which is a crucial way to reduce dialogue data overload. Recently, the promising achievements in both dialogue systems and natural language generation techniques drastically lead this task to a new landscape, which results in significant research attentions. However, there still remains a lack of a comprehensive survey for this task. To this end, we take the first step and present a thorough review of this research field carefully and widely. In detail, we systematically organize the current works according to the characteristics of each domain, covering meeting, chat, email thread, customer service and medical dialogue. Additionally, we provide an overview of publicly available research datasets as well as organize two leaderboards under unified metrics. Furthermore, we discuss some future directions, including faithfulness, multi-modal, multi-domain and multi-lingual dialogue summarization, and give our thoughts respectively. We hope that this first survey of dialogue summarization can provide the community with a quick access and a general picture to this task and motivate future researches.

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Automatic analysis of multiparty meetings

Cited by 3 publications

References 53 publications

Automated Scoring of Students’ Small‐Group Discussions to Assess Reading Ability

Automated Scoring of Students’ Small‐Group Discussions to Assess Reading Ability

Dialogue

Evidential Reasoning and Learning: a Survey

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