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.
Feb 22, 2024
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.
Jan 13, 2021
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
Jul 28, 2019