MINGYU DEREK MA
MINGYU DEREK MA
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Improving Event Definition Following For Zero-Shot Event Detection
We aim to improve zero-shot event detection by training models to better follow event definitions. We hypothesize that a diverse set of event types and definitions are the key for models to learn to follow event definitions while existing event extraction datasets focus on annotating many high-quality examples for a few event types. Our experiments verify our hypothesis.
Zefan Cai
,
Po-Nien Kung
,
Ashima Suvarna
,
Mingyu Derek Ma
,
Hritik Bansal
,
Baobao Chang
,
P. Jeffrey Brantingham
,
Wei Wang
,
Nanyun Peng
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DOI
New!
STAR: Boosting Low-Resource Information Extraction by Structure-to-Text Data Generation with Large Language Models
We propose STAR, a structure-to-text data generation method for complicated structure prediction tasks that first generates complicated event structures (Y) and then generates input passages (X), all with Large Language Models. We further reduce errors and improve data quality through self-reflection error identification and self-refinement with iterative revision. We show that the data generated by STAR significantly improves the performance of low-resource event extraction and relation extraction tasks, even surpassing the effectiveness of human-curated data.
Mingyu Derek Ma
,
Xiaoxuan Wang
,
Po-Nien Kung
,
P. Jeffrey Brantingham
,
Nanyun Peng
,
Wei Wang
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Code
Poster
DOI
DICE: Data-Efficient Clinical Event Extraction with Generative Models
We introduce DICE, a robust and data-efficient generative model for clinical event extraction, which specializes in clinical mention identification, and MACCROBAT-EE, the first clinical event extraction dataset with event argument annotation.
Mingyu Derek Ma
,
Alexander K. Taylor
,
Wei Wang
,
Nanyun Peng
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Poster
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Video
DOI
ACL Anthology
Parameter-Efficient Low-Resource Dialogue State Tracking by Prompt Tuning
We use soft prompt tokens to learn task properties, incorporate segment information and reiterate the task before predicting value. Our method drastically reduces the number of parameters needed to less than 0.5% of prior works while achieving better low-resource dialogue state tracking performance.
Mingyu Derek Ma
,
Jiun-Yu Kao
,
Shuyang Gao
,
Arpit Gupta
,
Di Jin
,
Tagyoung Chung
,
Nanyun Peng
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Poster
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Video
DOI
ISCA Archive
ISCA PDF
Amazon Science
EventPlus: A Temporal Event Understanding Pipeline
A temporal event understanding pipeline that integrates various state-of-the-art event understanding components including event trigger and type detection, event argument detection, event duration and temporal relation extraction.
Mingyu Derek Ma
,
Jiao Sun
,
Mu Yang
,
Kung-Hsiang Huang
,
Nuan Wen
,
Shikhar Singh
,
Rujun Han
,
Nanyun Peng
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Poster
Slides
Video
DOI
Demo
ACL Anthology
Implicit Discourse Relation Identification for Open-domain Dialogues
A novel dataset of implicit discourse relation argument pairs and labels for dialogic turns and a novel discourse relation identification pipeline specifically tuned for open-domain dialogue systems
Mingyu Derek Ma
,
Kevin K. Bowden
,
Jiaqi Wu
,
Wen Cui
,
Marilyn Walker
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Poster
DOI
ACL Anthology
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