Papers by Sky CH-Wang

8 papers
Toward a Critical Toponymy Framework for Named Entity Recognition: A Case Study of Airbnb in New York City (2023.emnlp-main)

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Challenge: Critical toponymy studies the dynamics of power, capital, and resistance through place names and the sites to which they refer.
Approach: They propose a model that measures how cultural and economic capital shape the ways in which people refer to places through an annotated dataset of Airbnb listings in New York City.
Outcome: The proposed model can identify important discourse categories integral to the characterization of place.
Do Androids Know They’re Only Dreaming of Electric Sheep? (2024.findings-acl)

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Challenge: Detecting hallucinations in grounded generation tasks is commonly framed as a textual entailment problem.
Approach: They develop probes that are narrowly trained to predict hallucination in a transformer language model.
Outcome: The probes can detect hallucinations at many transformer layers outperforming baselines and human annotators on two out of three generation tasks.
NormDial: A Comparable Bilingual Synthetic Dialog Dataset for Modeling Social Norm Adherence and Violation (2023.emnlp-main)

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Challenge: Social norms fundamentally shape interpersonal communication.
Approach: They propose a human-in-the-loop pipeline to synthesize a bilingual dyadic dialogue dataset with turn-by-turn annotations of social norms for Chinese and American cultures.
Outcome: The proposed dataset is high-quality through human evaluation and compares with existing models.
Sociocultural Norm Similarities and Differences via Situational Alignment and Explainable Textual Entailment (2023.emnlp-main)

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Challenge: Current research on developing computational models of social norms has focused on American society.
Approach: They propose to leverage a Chinese Q&A platform and a socialchiemistry dataset as proxies for contrasting cultural axes and align social situations cross-culturally.
Outcome: The proposed model can reason across cultures using a Chinese Q&A platform and the existing socialChemistry dataset.
Affective Idiosyncratic Responses to Music (2022.emnlp-main)

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Challenge: Affective responses to music are highly personal, but it's difficult to measure marginal effects of these variables . a study of 403M listener comments on a social music platform in china aims to address this gap .
Approach: They propose to measure affective responses to music from 403M listener comments on a Chinese social music platform.
Outcome: The proposed method identifies musical, lyrical, contextual, demographic, and mental health effects that drive listener affective responses from over 403M listener comments on a Chinese social music platform.
Using Sociolinguistic Variables to Reveal Changing Attitudes Towards Sexuality and Gender (2021.emnlp-main)

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Challenge: Existing studies show that word choice is driven by demographics within the United States.
Approach: They develop computational methods to study word choice within a sociolinguistic lexical variable . they use two variables to test for attitudes towards sexuality and gender in the u.s.
Outcome: The proposed methods allow us to examine attitudes towards sexuality and gender in the United States through two lexical variables.
MindCraft: Theory of Mind Modeling for Situated Dialogue in Collaborative Tasks (2021.emnlp-main)

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Challenge: Creating embodied, situated agents able to move in, communicate naturally about, and collaborate on human terms in the physical world has been a persisting goal in artificial intelligence (Winograd, 1972).
Approach: They propose to use a 3D Minecraft dataset to model the beliefs of human partners in situ to enable theory of mind modeling in situated interactions.
Outcome: The proposed model can be used to model human collaborative behaviors in the 3D virtual blocks world of Minecraft.
Browsing Lost Unformed Recollections: A Benchmark for Tip-of-the-Tongue Search and Reasoning (2025.acl-long)

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Challenge: BLUR is a tip-of-the-tongue known-item search and reasoning benchmark for general AI assistants.
Approach: They introduce a tip-of-the-tongue known-item search and reasoning benchmark for general AI assistants.
Outcome: The proposed benchmark demands searching and reasoning across multimodal and multilingual inputs, as well as proficient tool use, in order to excel on.

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