Expressive Interviewing: A Conversational System for Coping with COVID-19

Welch, Charles; Lahnala, Allison; Pérez-Rosas, Verónica; Shen, Siqi; Seraj, Sarah; An, Larry; Resnicow, Kenneth; Pennebaker, James W.; Mihalcea, Rada

doi:10.18653/v1/2020.nlpcovid19-2.6

Cited by 16 publications

(4 citation statements)

References 13 publications

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“…The pandemic outbreak led to the development of a few other DSs in this domain. Welch et al (2020) introduce expressive interviewing -an interview style aiming to encourage users to express their thoughts and feelings by asking them questions about how COVID-19 has impacted their lives. Chalaguine and Hunter (2021) built and studied a DS specifically addressing COVID-19 vaccine hesitancy and showed that 20% of study participants changed their stance in favor of the vaccine after conversing with the system.…”

Section: Related Workmentioning

confidence: 99%

Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy

Gretz¹,

Toledo²,

Friedman³

et al. 2022

Preprint

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The COVID-19 pandemic has made a huge global impact and cost millions of lives. As COVID-19 vaccines were rolled out, they were quickly met with widespread hesitancy. To address the concerns of hesitant people, we launched VIRA, a public dialogue system aimed at addressing questions and concerns surrounding the COVID-19 vaccines. Here, we release VIRADialogs, a dataset of over 8k dialogues conducted by actual users with VIRA, providing a unique real-world conversational dataset. In light of rapid changes in users' intents, due to updates in guidelines or as a response to new information, we highlight the important task of intent discovery in this use-case. We introduce a novel automatic evaluation framework for intent discovery, leveraging the existing intent classifier of a given dialogue system. We use this framework to report baseline intent-discovery results over VIRADialogs, that highlight the difficulty of this task.

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Section: Related Workmentioning

confidence: 99%

Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy

Gretz¹,

Toledo²,

Friedman³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…It is recognized that won't be possible to treat people by professionals alone, and even if possible, some people might require to use alternative modalities to receive mental health support [23]; such situation has become more evident with the current COVID-19 pandemic. Examples of recent efforts building technology toward this direction are the Woebot [24] and Wysa [25] dialog systems for health and therapy support for patients that have depression symptoms; Expressive Interviewing [26], which is a conversational agent aiming at support users to cope with COVID-19 issues. The underlying hypothesis of most these works relies on the notion of the language as a powerful indicator about our personality, social, or emotional status, and mental health [3,27].…”

Section: Related Workmentioning

confidence: 99%

Applying Attention-Based Models for Detecting Cognitive Processes and Mental Health Conditions

Villatoro-Tello

Parida

Kumar³

et al. 2021

Cogn Comput

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According to the psychological literature, implicit motives allow for the characterization of behavior, subsequent success, and long-term development. Contrary to personality traits, implicit motives are often deemed to be rather stable personality characteristics. Normally, implicit motives are obtained by Operant Motives, unconscious intrinsic desires measured by the Operant Motive Test (OMT). The OMT test requires participants to write freely descriptions associated with a set of provided images and questions. In this work, we explore different recent machine learning techniques and various text representation techniques for facing the problem of the OMT classification task. We focused on advanced language representations (e.g, BERT, XLM, and DistilBERT) and deep Supervised Autoencoders for solving the OMT task. We performed an exhaustive analysis and compared their performance against fully connected neural networks and traditional support vector classifiers. Our comparative study highlights the importance of BERT which outperforms the traditional machine learning techniques by a relative improvement of 7.9%. In addition, we performed an analysis of how the BERT attention mechanism is being modified. Our findings indicate that the writing style features acquire higher importance at the moment of accurately identifying the different OMT categories. This is the first time that a study to determine the performance of different transformer-based architectures in the OMT task is performed. Similarly, our work propose, for the first time, using deep supervised autoencoders in the OMT classification task. Our experiments demonstrate that transformer-based methods exhibit the best empirical results, obtaining a relative improvement of 7.9% over the competitive baseline suggested as part of the GermEval 2020 challenge. Additionally, we show that features associated with the writing style are more important than content-based words. Some of these findings show strong connections to previously reported behavioral research on the implicit psychometrics theory.

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“…Subsequently, NLP services such as Amazon Kendra were deployed to help organize the research knowledge around COVID-19 (Bhatia et al, 2020). The NLP research community worked on several problems like the question-answering and summarization system CAiRE-COVID (Su et al, 2020), the expressive interviewing conversational system (Welch et al, 2020) and annotation schemas to help fight COVID-19 misinformation online (Alam et al, 2020;Hossain et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact

Jin¹,

Chauhan²,

Tse³

et al. 2021

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021

Self Cite

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Recent years have seen many breakthroughs in natural language processing (NLP), transitioning it from a mostly theoretical field to one with many real-world applications. Noting the rising number of applications of other machine learning and AI techniques with pervasive societal impact, we anticipate the rising importance of developing NLP technologies for social good. Inspired by theories in moral philosophy and global priorities research, we aim to promote a guideline for social good in the context of NLP. We lay the foundations via the moral philosophy definition of social good, propose a framework to evaluate the direct and indirect real-world impact of NLP tasks, and adopt the methodology of global priorities research to identify priority causes for NLP research. Finally, we use our theoretical framework to provide some practical guidelines for future NLP research for social good. 1

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Expressive Interviewing: A Conversational System for Coping with COVID-19

Cited by 16 publications

References 13 publications

Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy

Benchmark Data and Evaluation Framework for Intent Discovery Around COVID-19 Vaccine Hesitancy

Applying Attention-Based Models for Detecting Cognitive Processes and Mental Health Conditions

How Good Is NLP? A Sober Look at NLP Tasks through the Lens of Social Impact

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