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VERVE: Template-based ReflectiVE Rewriting for MotiVational IntErviewing

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Reflective listening is a fundamental skill that counselors must acquire to achieve proficiency in motivational interviewing (MI). It involves responding in a manner that acknowledges and explores the meaning of what the client has expressed in the conversation. In this work, we introduce the task of counseling response rewriting, which transforms non-reflective statements into reflective responses. We introduce VERVE, a template-based rewriting system with paraphrase-augmented training and adaptive template updating. VERVE first creates a template by identifying and filtering out tokens that are not relevant to reflections and constructs a reflective response using the template. Paraphrase-augmented training allows the model to learn less-strict fillings of masked spans, and adaptive template updating helps discover effective templates for rewriting without significantly removing the original content. Using both automatic and human evaluations, we compare our method against text rewriting baselines and show that our framework is effective in turning non-reflective statements into more reflective responses while achieving a good content preservation-reflection style trade-off.

Original languageEnglish (US)
Title of host publicationFindings of the Association for Computational Linguistics
Subtitle of host publicationEMNLP 2023
PublisherAssociation for Computational Linguistics (ACL)
Pages10289-10302
Number of pages14
ISBN (Electronic)9798891760615
DOIs
StatePublished - 2023
Externally publishedYes
Event2023 Findings of the Association for Computational Linguistics: EMNLP 2023 - Hybrid, Singapore
Duration: Dec 6 2023Dec 10 2023

Publication series

NameFindings of the Association for Computational Linguistics: EMNLP 2023

Conference

Conference2023 Findings of the Association for Computational Linguistics: EMNLP 2023
Country/TerritorySingapore
CityHybrid
Period12/6/2312/10/23

Bibliographical note

Publisher Copyright:
© 2023 Association for Computational Linguistics.

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