Anno-Mi: A dataset of expert- annotated counselling dialogues

Zixiu Wu* (Corresponding Author), Simone Balloccu, Vivek Kumar, Rim Helaoui, Ehud Reiter, Diego Reforgiato Recupero, Daniele Riboni

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

1 Citation (Scopus)

Abstract

Research on natural language processing for counselling dialogue analysis has seen substantial development in recent years, but access to this area remains extremely limited due to the lack of publicly available expert-annotated therapy
conversations. In this work, we introduce AnnoMI, the first publicly and freely accessible dataset of professionally transcribed and expert-annotated therapy dialogues. It consists of 133 conversations that demonstrate high- and low-quality motivational interviewing (MI), an effective counselling technique, and the annotations by domain experts cover key MI attributes. We detail the data collection process including dialogue selection, transcription and annotation. We also present
analyses of AnnoMI and discuss its potential applications
Original languageEnglish
Title of host publicationICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
PublisherIEEE Explore
Pages6177-6181
Number of pages5
ISBN (Electronic)978-1-6654-0540-9
ISBN (Print)978-1-6654-0541-6
DOIs
Publication statusPublished - 27 Apr 2022
EventIEEE ICASSP 2022: IEEIEEE International Conference on Acoustics, Speech and Signal Processing - In-person conference at Singapore or China, streamed for virtual participation, China
Duration: 22 Mar 202227 Mar 2022
https://2022.ieeeicassp.org/

Publication series

NameProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
PublisherIEEE
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

ConferenceIEEE ICASSP 2022
Country/TerritoryChina
Period22/03/2227/03/22
Internet address

Keywords

  • Counselling
  • Motivational Interviewing
  • Dialogue
  • Natural Language Processing
  • Dataset

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