@inproceedings{mousavi-etal-2021-like,
title = "Would you like to tell me more? Generating a corpus of psychotherapy dialogues",
author = "Mousavi, Seyed Mahed and
Cervone, Alessandra and
Danieli, Morena and
Riccardi, Giuseppe",
editor = "Shivade, Chaitanya and
Gangadharaiah, Rashmi and
Gella, Spandana and
Konam, Sandeep and
Yuan, Shaoqing and
Zhang, Yi and
Bhatia, Parminder and
Wallace, Byron",
booktitle = "Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.nlpmc-1.1",
doi = "10.18653/v1/2021.nlpmc-1.1",
pages = "1--9",
abstract = "The acquisition of a dialogue corpus is a key step in the process of training a dialogue model. In this context, corpora acquisitions have been designed either for open-domain information retrieval or slot-filling (e.g. restaurant booking) tasks. However, there has been scarce research in the problem of collecting personal conversations with users over a long period of time. In this paper we focus on the types of dialogues that are required for mental health applications. One of these types is the follow-up dialogue that a psychotherapist would initiate in reviewing the progress of a Cognitive Behavioral Therapy (CBT) intervention. The elicitation of the dialogues is achieved through textual stimuli presented to dialogue writers. We propose an automatic algorithm that generates textual stimuli from personal narratives collected during psychotherapy interventions. The automatically generated stimuli are presented as a seed to dialogue writers following principled guidelines. We analyze the linguistic quality of the collected corpus and compare the performances of psychotherapists and non-expert dialogue writers. Moreover, we report the human evaluation of a corpus-based response-selection model.",
}
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<abstract>The acquisition of a dialogue corpus is a key step in the process of training a dialogue model. In this context, corpora acquisitions have been designed either for open-domain information retrieval or slot-filling (e.g. restaurant booking) tasks. However, there has been scarce research in the problem of collecting personal conversations with users over a long period of time. In this paper we focus on the types of dialogues that are required for mental health applications. One of these types is the follow-up dialogue that a psychotherapist would initiate in reviewing the progress of a Cognitive Behavioral Therapy (CBT) intervention. The elicitation of the dialogues is achieved through textual stimuli presented to dialogue writers. We propose an automatic algorithm that generates textual stimuli from personal narratives collected during psychotherapy interventions. The automatically generated stimuli are presented as a seed to dialogue writers following principled guidelines. We analyze the linguistic quality of the collected corpus and compare the performances of psychotherapists and non-expert dialogue writers. Moreover, we report the human evaluation of a corpus-based response-selection model.</abstract>
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%0 Conference Proceedings
%T Would you like to tell me more? Generating a corpus of psychotherapy dialogues
%A Mousavi, Seyed Mahed
%A Cervone, Alessandra
%A Danieli, Morena
%A Riccardi, Giuseppe
%Y Shivade, Chaitanya
%Y Gangadharaiah, Rashmi
%Y Gella, Spandana
%Y Konam, Sandeep
%Y Yuan, Shaoqing
%Y Zhang, Yi
%Y Bhatia, Parminder
%Y Wallace, Byron
%S Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F mousavi-etal-2021-like
%X The acquisition of a dialogue corpus is a key step in the process of training a dialogue model. In this context, corpora acquisitions have been designed either for open-domain information retrieval or slot-filling (e.g. restaurant booking) tasks. However, there has been scarce research in the problem of collecting personal conversations with users over a long period of time. In this paper we focus on the types of dialogues that are required for mental health applications. One of these types is the follow-up dialogue that a psychotherapist would initiate in reviewing the progress of a Cognitive Behavioral Therapy (CBT) intervention. The elicitation of the dialogues is achieved through textual stimuli presented to dialogue writers. We propose an automatic algorithm that generates textual stimuli from personal narratives collected during psychotherapy interventions. The automatically generated stimuli are presented as a seed to dialogue writers following principled guidelines. We analyze the linguistic quality of the collected corpus and compare the performances of psychotherapists and non-expert dialogue writers. Moreover, we report the human evaluation of a corpus-based response-selection model.
%R 10.18653/v1/2021.nlpmc-1.1
%U https://aclanthology.org/2021.nlpmc-1.1
%U https://doi.org/10.18653/v1/2021.nlpmc-1.1
%P 1-9
Markdown (Informal)
[Would you like to tell me more? Generating a corpus of psychotherapy dialogues](https://aclanthology.org/2021.nlpmc-1.1) (Mousavi et al., NLPMC 2021)
ACL