Papers by Jonathan Chang
Cross-lingual Structure Transfer for Relation and Event Extraction (D19-1)
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| Challenge: | Existing approaches to identify complex semantic structures are difficult to train from under-annotated sources. |
| Approach: | They exploit relation- and event-relevant language-universal features to train relation or event extractors from source annotations and apply them to target languages. |
| Outcome: | The proposed approach achieves comparable performance to state-of-the-art models trained on 3,000 manually annotated mentions. |
Conversations Gone Awry: Detecting Early Signs of Conversational Failure (P18-1)
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Justine Zhang, Jonathan Chang, Cristian Danescu-Niculescu-Mizil, Lucas Dixon, Yiqing Hua, Dario Taraborelli, Nithum Thain
| Challenge: | Prior work focused on characterizing and detecting content exhibiting antisocial online behavior. |
| Approach: | They propose a task of predicting from the very start of a conversation whether it will get out of hand. |
| Outcome: | The proposed framework can detect early warning signs of antisocial behavior in online conversations. |
Cross-lingual Structure Transfer for Zero-resource Event Extraction (2020.lrec-1)
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| Challenge: | Existing approaches for information extraction only use name tagging . Currently, most successful cross-lingual transfer learning methods are limited to sequence labeling . |
| Approach: | They propose a share-and-transfer framework to transfer graph structures across languages . they propose to convert sentences in any language to language-universal graph structures . |
| Outcome: | The proposed framework performs comparable to state-of-the-art models on three languages without annotations. |
Using Linguistic Entrainment to Evaluate Large Language Models for Use in Cognitive Behavioral Therapy (2025.findings-naacl)
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Mina Kian, Kaleen Shrestha, Katrin Fischer, Xiaoyuan Zhu, Jonathan Ong, Aryan Trehan, Jessica Wang, Gloria Chang, Séb Arnold, Maja Mataric
| 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. |
CapWAP: Image Captioning with a Purpose (2020.emnlp-main)
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| Challenge: | a traditional image captioning task uses generic reference captions to provide textual information about images. |
| Approach: | They propose a task that uses question-answer pairs to provide visual information instead of generic reference captions. |
| Outcome: | The proposed captioning with a purpose task can be tailored to meet user needs . question-answer pairs are used as a source of supervision for learning visual information needs a new task is proposed . |
StreamHover: Livestream Transcript Summarization and Annotation (2021.emnlp-main)
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Sangwoo Cho, Franck Dernoncourt, Tim Ganter, Trung Bui, Nedim Lipka, Walter Chang, Hailin Jin, Jonathan Brandt, Hassan Foroosh, Fei Liu
| Challenge: | StreamHover is a framework for annotating and summarizing livestream transcripts . the problem is that there is n't enough annotated datasets to summarize livestreams based on the informal nature of spoken language . |
| Approach: | They propose a framework for annotating and summarizing livestream transcripts using a text preview. |
| Outcome: | The proposed model generalizes better and improves over strong baselines. |
WinoQueer: A Community-in-the-Loop Benchmark for Anti-LGBTQ+ Bias in Large Language Models (2023.acl-long)
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| Challenge: | Existing benchmarks for measuring anti-LGBTQ+ bias are poorly defined and insufficiently grounded in real-world harms. |
| Approach: | They propose a bias benchmark that is community-sourced and generates a community survey. |
| Outcome: | The proposed method is community-sourced and improves on WinoQueer-v0. |
A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation (2022.naacl-main)
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David Adelani, Jesujoba Alabi, Angela Fan, Julia Kreutzer, Xiaoyu Shen, Machel Reid, Dana Ruiter, Dietrich Klakow, Peter Nabende, Ernie Chang, Tajuddeen Gwadabe, Freshia Sackey, Bonaventure F. P. Dossou, Chris Emezue, Colin Leong, Michael Beukman, Shamsuddeen Muhammad, Guyo Jarso, Oreen Yousuf, Andre Niyongabo Rubungo, Gilles Hacheme, Eric Peter Wairagala, Muhammad Umair Nasir, Benjamin Ajibade, Tunde Ajayi, Yvonne Gitau, Jade Abbott, Mohamed Ahmed, Millicent Ochieng, Anuoluwapo Aremu, Perez Ogayo, Jonathan Mukiibi, Fatoumata Ouoba Kabore, Godson Kalipe, Derguene Mbaye, Allahsera Auguste Tapo, Victoire Memdjokam Koagne, Edwin Munkoh-Buabeng, Valencia Wagner, Idris Abdulmumin, Ayodele Awokoya, Happy Buzaaba, Blessing Sibanda, Andiswa Bukula, Sam Manthalu
| Challenge: | Low-resource languages are left out of large-scale pretraining datasets . authors explore how to leverage existing pre-trained models to create low-resourced translation systems for 16 African languages. |
| Approach: | They investigate how large-scale pre-trained models can be used to create low-resource translation systems for 16 African languages. |
| Outcome: | The proposed models can translate between hundreds of languages even though there is little parallel data available for training. |