Hongli Henry Zhan

Office: RLP 4.400, E4

I am a 3rd year Ph.D. student in Computational Linguistics at The University of Texas at Austin, where I’m blessed to be advised by Professor Junyi Jessy Li. My research interest is in Natural Language Processing as well as Computational Linguistics, with a focus on emotions. I am part of the UT Austin NLP community as well as the Associated Students of Linguistics. Casually, I go by Henry. My Chinese name is 詹弘立.

You can find my CV here. Additionally, my resume is provided here. Media coverage of my research can be found here. The profile photo here was taken at the presentation of my first-ever paper, which was accepted to EMNLP 2022.

I’m also a cellist since 4 years old. In my free time, I like to practice the cello and play golf. You can find more details about my cello journey here.

Education

• Ph.D. in Computational Linguistics (Minor in Computer Science), 2021 – Present

The University of Texas at Austin
Advisor: Professor Junyi Jessy Li

• B.A. in English Linguistics (Second Major in Law), 2017 – 2021

Shanghai Jiao Tong University
Awards: Outstanding Undergraduate; Outstanding Undergraduate Thesis Award

Publications

* denotes equal contributions

ACL 2023

Unsupervised Extractive Summarization of Emotion Triggers

Tiberiu Sosea*, Hongli Zhan*, Junyi Jessy Li, and Cornelia Caragea

In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics Jul 2023

Abs PDF Code

Understanding what leads to emotions during large-scale crises is important as it can provide groundings for expressed emotions and subsequently improve the understanding of ongoing disasters. Recent approaches trained supervised models to both detect emotions and explain emotion triggers (events and appraisals) via abstractive summarization. However, obtaining timely and qualitative abstractive summaries is expensive and extremely time-consuming, requiring highly-trained expert annotators. In time-sensitive, high-stake contexts, this can block necessary responses. We instead pursue unsupervised systems that extract triggers from text. First, we introduce CovidET-EXT, augmenting (Zhan et al., 2022)’s abstractive dataset (in the context of the COVID-19 crisis) with extractive triggers. Second, we develop new unsupervised learning models that can jointly detect emotions and summarize their triggers. Our best approach, entitled Emotion-Aware Pagerank, incorporates emotion information from external sources combined with a language understanding module, and outperforms strong baselines. We release our data and code at https://github.com/tsosea2/CovidET-EXT.
EMNLP 2022

Why Do You Feel This Way? Summarizing Triggers of Emotions in Social Media Posts

Hongli Zhan*, Tiberiu Sosea*, Cornelia Caragea, and Junyi Jessy Li

In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing Dec 2022

Abs PDF Code Video Poster

Crises such as the COVID-19 pandemic continuously threaten our world and emotionally affect billions of people worldwide in distinct ways. Understanding the triggers leading to people’s emotions is of crucial importance. Social media posts can be a good source of such analysis, yet these texts tend to be charged with multiple emotions, with triggers scattering across multiple sentences. This paper takes a novel angle, namely, emotion detection and trigger summarization, aiming to both detect perceived emotions in text, and summarize events and their appraisals that trigger each emotion. To support this goal, we introduce CovidET (Emotions and their Triggers during Covid-19), a dataset of ~1,900 English Reddit posts related to COVID-19, which contains manual annotations of perceived emotions and abstractive summaries of their triggers described in the post. We develop strong baselines to jointly detect emotions and summarize emotion triggers. Our analyses show that CovidET presents new challenges in emotion-specific summarization, as well as multi-emotion detection in long social media posts.