Abstract
We present the first work on automatically capturing alliance rupture in transcribed therapy sessions, trained on the text and self-reported rupture scores from both therapists and clients. Our NLP baseline outperforms a strong majority baseline by a large margin and captures client reported ruptures unidentified by therapists in 40% of such cases.
| Original language | English |
|---|---|
| Title of host publication | Computational Linguistics and Clinical Psychology |
| Subtitle of host publication | Improving Access, CLPsych 2021 - Proceedings of the 7th Workshop, in conjunction with NAACL 2021 |
| Editors | Nazli Goharian, Philip Resnik, Andrew Yates, Molly Ireland, Kate Niederhoffer, Rebecca Resnik |
| Publisher | Association for Computational Linguistics (ACL) |
| Pages | 122-128 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781954085411 |
| State | Published - 2021 |
| Event | 7th Workshop on Computational Linguistics and Clinical Psychology: Improving Access, CLPsych 2021 - Virtual, Online Duration: 11 Jun 2021 → … |
Publication series
| Name | Computational Linguistics and Clinical Psychology: Improving Access, CLPsych 2021 - Proceedings of the 7th Workshop, in conjunction with NAACL 2021 |
|---|
Conference
| Conference | 7th Workshop on Computational Linguistics and Clinical Psychology: Improving Access, CLPsych 2021 |
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| City | Virtual, Online |
| Period | 11/06/21 → … |
Bibliographical note
Publisher Copyright:©2021 Association for Computational Linguistics.
Funding
This work was supported by a UKRI/EPSRC Turing AI Fellowship to Maria Liakata (grant EP/V030302/1) and the The Alan Turing Institute (grant EP/N510129/1).
| Funders | Funder number |
|---|---|
| Alan Turing Institute | EP/N510129/1 |
| UK Research and Innovation | |
| Engineering and Physical Sciences Research Council | EP/V030302/1 |