Papers by Jessica Wang

5 papers
Using Linguistic Entrainment to Evaluate Large Language Models for Use in Cognitive Behavioral Therapy (2025.findings-naacl)

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Challenge: Entrainment is a communication process that builds a strong relationship between a mental health therapist and their client.
Approach: They evaluate the linguistic entrainment of an LLM in a mental health dialog setting and compare it to trained therapists and non-expert online peer supporters.
Outcome: The proposed model outperforms humans in a cognitive behavioral therapy setting.
“Mistakes Help Us Grow”: Facilitating and Evaluating Growth Mindset Supportive Language in Classrooms (2023.emnlp-main)

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Challenge: GMSL has been shown to significantly reduce disparities in academic achievement and enhance students’ learning outcomes.
Approach: They develop a coaching tool to reframe unsupportive utterances to GMSL using large language models.
Outcome: The proposed model outperforms the GMSL-trained teachers in fostering a growth mindset and promoting challenge-seeking behavior.
Multimodal Dialogue Response Generation (2022.acl-long)

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Challenge: Existing studies focus on multimodal dialogue models but neglect generation methods.
Approach: They propose a multimodal dialogue response generation task which requires multimodal dialogs containing both texts and images which are difficult to obtain.
Outcome: Experiments show that the proposed model can generate informative text and high-resolution image responses.
AfriMTE and AfriCOMET: Enhancing COMET to Embrace Under-resourced African Languages (2024.naacl-long)

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Challenge: Recent advances in machine translation (MT) have focused on scaling multilingual machine translation models and evaluation data to hundreds of languages, including multiple under-resourced languages.
Approach: They propose to use n-gram matching metrics to measure progress in multilingual machine translation to 13 typologically diverse African languages to create high-quality human evaluation data with simplified MQM guidelines.
Outcome: The proposed metrics have a higher correlation with human judgments than n-gram matching metrics such as BLEU and METEOR.
Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs (2022.naacl-main)

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Challenge: NLP-powered automatic question generation (QG) techniques have not been widely adopted in classrooms to date.
Approach: They propose to identify key impediments and improve the usability of NLP-powered automatic question generation techniques by understanding how instructors construct questions and identifying touch points to enhance the underlying NLP models.
Outcome: The proposed methods can be used by 11 instructors across 7 universities and highlight their needs and needs when creating questions.

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