Yuxuan Lu, Jing Huang, Yan Han, Bingsheng Yao, Sisong Bei, Yaochen Xie, Yisi Sang, Qi He, Dakuo Wang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Ziyi Wang, Yuxuan Lu, Yimeng Zhang, Pei Chen, Ziwei Dong, Jing Huang, Jiri Gesi, Xianfeng Tang, Chen Luo, Qun Liu, Yisi Sang, Hanqing Lu, Manling Li, Jin Lai, Dakuo Wang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

When reading a story, humans can quickly understand new fictional characters with a few observations, mainly by drawing analogies to fictional and real people they already know. This reflects the few-shot and meta-learning essence of humans' inference of characters' mental states, *i.e.*, theory-of-mind (ToM), which is largely ignored in existing research. We fill this gap with a novel NLP dataset in a realistic narrative understanding scenario, ToM-in-AMC. Our dataset consists of $\sim$1,000 parsed movie scripts, each corresponding to a few-shot character understanding task that requires models to mimic humans' ability of fast digesting characters with a few starting scenes in a new movie. We further propose a novel ToM prompting approach designed to explicitly assess the influence of multiple ToM dimensions. It surpasses existing baseline models, underscoring the significance of modeling multiple ToM dimensions for our task. Our extensive human study verifies that humans are capable of solving our problem by inferring characters' mental states based on their previously seen movies. In comparison, all the AI systems lag $>20\%$ behind humans, highlighting a notable limitation in existing approaches' ToM capabilities. Code and data are available at https://github.com/ShunchiZhang/ToM-in-AMC

Due to the limitations imposed by the COVID-19 pandemic, customers have shifted their shopping patterns from offline to online. Livestream shopping has become popular as one of the online shopping media. However, various streamers’ malicious selling behaviors have been reported. In this research, we sought to explore streamers’ malicious selling strategies and understand how viewers perceive these strategies. First, we recorded 40 livestream shopping sessions from two popular livestream platforms in China—Taobao, and TikTok. We identified 16 malicious selling strategies that were used to deceive, coerce, or manipulate viewers and found that platform designs enhanced nine of the malicious selling strategies. Second, through an interview study with 13 viewers, we report three challenges of overcoming malicious selling in relation to imbalanced power between viewers, streamers, and the platforms. We conclude by discussing the policy and design implications of countering malicious selling.

Ying Xu, Dakuo Wang, Mo Yu, Daniel Ritchie, Bingsheng Yao, Tongshuang Wu, Zheng Zhang, Toby Li, Nora Bradford, Branda Sun, Tran Hoang, Yisi Sang, Yufang Hou, Xiaojuan Ma, Diyi Yang, Nanyun Peng, Zhou Yu, Mark Warschauer. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.

As the body of research on machine narrative comprehension grows, there is a critical need for consideration of performance assessment strategies as well as the depth and scope of different benchmark tasks. Based on narrative theories, reading comprehension theories, as well as existing machine narrative reading comprehension tasks and datasets, we propose a typology that captures the main similarities and differences among assessment tasks; and discuss the implications of our typology for new task design and the challenges of narrative reading comprehension.

Yisi Sang, Xiangyang Mou, Mo Yu, Shunyu Yao, Jing Li, Jeffrey Stanton. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2022.

A large body of literature is dedicated to understanding people's check-in behavior when they use location sharing services to pair their location with a venue, e.g., a restaurant, a park, etc. Check-in behavior in higher education settings, e.g., where students and instructors have academic purposes for check-ins, is under-studied. In this work, we explore how university students apply two different mechanisms, i.e., automatic and manual location-sharing services, to conduct check-ins for an academic purpose (i.e., students sharing their class attendance with their instructor). More specifically, a Bluetooth Low Energy beacon-based technology is applied to enable automatic class check-ins. We conducted two field trials with a total of 141 university students. Our findings showed that several social, technological, and psychological factors impacted the use of auto and manual check-ins. Feedback from the student participants suggested that future higher education check-in systems may need to consider the integration of check-ins for a variety of purposes.

Danmaku is a new video comment feature that is gaining popularity. Unlike typical forum comments that are displayed with user names below videos, danmaku comments are overlaid on the screen of videos without showing users' information. Prior work studied forum comments and danmaku separately, and little work compared how these two features were used. We collected 38,399 danmaku comments and 16,414 forum comments posted in 2017 on 30 popular videos on Bilibili.com. We examined the usage of these two features in terms of user participation, language used, and ways of sharing knowledge. We found that more users posted danmaku comments, and they also posted these more frequently than forum comments. Even though, in total, more negative language was used in danmaku comments than in forum comments, active users appeared to post more positive comments in danmaku. There was no such correlation in forum comments. It is interesting to find that danmaku and forum comments enabled knowledge sharing in a complementary manner, where danmaku comments involved more explicit knowledge sharing and forum comments exhibited more tacit knowledge sharing. We discuss design implications to promote social interactions for online video systems.