Sunny Rai


2024

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Building Knowledge-Guided Lexica to Model Cultural Variation
Shreya Havaldar | Salvatore Giorgi | Sunny Rai | Thomas Talhelm | Sharath Chandra Guntuku | Lyle Ungar
Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Cultural variation exists between nations (e.g., the United States vs. China), but also within regions (e.g., California vs. Texas, Los Angeles vs. San Francisco). Measuring this regional cultural variation can illuminate how and why people think and behave differently. Historically, it has been difficult to computationally model cultural variation due to a lack of training data and scalability constraints. In this work, we introduce a new research problem for the NLP community: How do we measure variation in cultural constructs across regions using language? We then provide a scalable solution: building knowledge-guided lexica to model cultural variation, encouraging future work at the intersection of NLP and cultural understanding. We also highlight modern LLMs’ failure to measure cultural variation or generate culturally varied language.

2023

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NewsMet : A ‘do it all’ Dataset of Contemporary Metaphors in News Headlines
Rohan Joseph | Timothy Liu | Aik Beng Ng | Simon See | Sunny Rai
Findings of the Association for Computational Linguistics: ACL 2023

Metaphors are highly creative constructs of human language that grow old and eventually die. Popular datasets used for metaphor processing tasks were constructed from dated source texts. In this paper, we propose NewsMet, a large high-quality contemporary dataset of news headlines hand-annotated with metaphorical verbs. The dataset comprises headlines from various sources including political, satirical, reliable and fake. Our dataset serves the purpose of evaluation for the tasks of metaphor interpretation and generation. The experiments reveal several insights and limitations of using LLMs to automate metaphor processing tasks as frequently seen in the recent literature. The dataset is publicly available for research purposes https://github.com/AxleBlaze3/NewsMet_Metaphor_Dataset.

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Multilingual Language Models are not Multicultural: A Case Study in Emotion
Shreya Havaldar | Bhumika Singhal | Sunny Rai | Langchen Liu | Sharath Chandra Guntuku | Lyle Ungar
Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis

Emotions are experienced and expressed differently across the world. In order to use Large Language Models (LMs) for multilingual tasks that require emotional sensitivity, LMs must reflect this cultural variation in emotion. In this study, we investigate whether the widely-used multilingual LMs in 2023 reflect differences in emotional expressions across cultures and languages. We find that embeddings obtained from LMs (e.g., XLM-RoBERTa) are Anglocentric, and generative LMs (e.g., ChatGPT) reflect Western norms, even when responding to prompts in other languages. Our results show that multilingual LMs do not successfully learn the culturally appropriate nuances of emotion and we highlight possible research directions towards correcting this.

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An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives
Young Min Cho | Sunny Rai | Lyle Ungar | João Sedoc | Sharath Guntuku
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Mental health conversational agents (a.k.a. chatbots) are widely studied for their potential to offer accessible support to those experiencing mental health challenges. Previous surveys on the topic primarily consider papers published in either computer science or medicine, leading to a divide in understanding and hindering the sharing of beneficial knowledge between both domains. To bridge this gap, we conduct a comprehensive literature review using the PRISMA framework, reviewing 534 papers published in both computer science and medicine. Our systematic review reveals 136 key papers on building mental health-related conversational agents with diverse characteristics of modeling and experimental design techniques. We find that computer science papers focus on LLM techniques and evaluating response quality using automated metrics with little attention to the application while medical papers use rule-based conversational agents and outcome metrics to measure the health outcomes of participants. Based on our findings on transparency, ethics, and cultural heterogeneity in this review, we provide a few recommendations to help bridge the disciplinary divide and enable the cross-disciplinary development of mental health conversational agents.

2022

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Identifying Human Needs through Social Media: A study on Indian cities during COVID-19
Sunny Rai | Rohan Joseph | Prakruti Singh Thakur | Mohammed Abdul Khaliq
Proceedings of the Tenth International Workshop on Natural Language Processing for Social Media

In this paper, we present a minimally-supervised approach to identify human needs expressed in tweets. Taking inspiration from Frustration-Aggression theory, we trained RoBERTa model to classify tweets expressing frustration which serves as an indicator of unmet needs. Although the notion of frustration is highly subjective and complex, the findings support the use of pretrained language model in identifying tweets with unmet needs. Our study reveals the major causes behind feeling frustrated during the lockdown and the second wave of the COVID-19 pandemic in India. Our proposed approach can be useful in timely identification and prioritization of emerging human needs in the event of a crisis.

2021

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#covid is war and #vaccine is weapon? COVID-19 metaphors in India
Mohammed Khaliq | Rohan Joseph | Sunny Rai
Proceedings of the 18th International Conference on Natural Language Processing (ICON)

Metaphors are creative cognitive constructs that are employed in everyday conversation to describe abstract concepts and feelings. Prevalent conceptual metaphors such as WAR, MONSTER, and DARKNESS in COVID-19 online discourse sparked a multi-faceted debate over their efficacy in communication, resultant psychological impact on listeners, and their appropriateness in social discourse. In this work, we investigate metaphors used in discussions around COVID-19 on Indian Twitter. We observe subtle transitions in metaphorical mappings as the pandemic progressed. Our experiments, however, didn’t indicate any affective impact of WAR metaphors on the COVID-19 discourse.

2016

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Supervised Metaphor Detection using Conditional Random Fields
Sunny Rai | Shampa Chakraverty | Devendra K. Tayal
Proceedings of the Fourth Workshop on Metaphor in NLP