Papers by Mingyu Derek Ma
Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction? (2023.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to biomedical relation extraction (RE) are limited due to the scarcity of annotations and the prevalence of instances without explicitly pre-defined labels. |
| Approach: | They propose a method which converts biomedical relation extraction (RE) as natural language inference formulation through indirect supervision. |
| Outcome: | Extensive experiments on three widely-used biomedical RE benchmarks show that indirect supervision improves biomedically relation extraction even when a domain gap exists. |
HyperExpan: Taxonomy Expansion with Hyperbolic Representation Learning (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing taxonomies have limited coverage due to expensive manual curation process. |
| Approach: | They propose an algorithm that expands existing taxonomies to preserve their structure in a more expressive hyperbolic embedding space and learns to represent concepts and their relations with a hyperbolical Graph Neural Network. |
| Outcome: | The proposed algorithm outperforms baseline models with representation learning in a Euclidean feature space and achieves state-of-the-art performance on the taxonomy expansion benchmarks. |
Multi-hop Evidence Retrieval for Cross-document Relation Extraction (2023.findings-acl)
Copied to clipboard
| Challenge: | Relation Extraction (RE) is a task that seeks to identify the relation of entities described according to some context. |
| Approach: | They propose a multi-hop evidence retrieval method based on evidence path mining and ranking to support cross-document relation extraction. |
| Outcome: | The proposed method acquires cross-document evidence and boosts performance in both closed and open environments. |
EventPlus: A Temporal Event Understanding Pipeline (2021.naacl-demos)
Copied to clipboard
Mingyu Derek Ma, Jiao Sun, Mu Yang, Kung-Hsiang Huang, Nuan Wen, Shikhar Singh, Rujun Han, Nanyun Peng
| Challenge: | Event information is a type of common sense knowledge that helps people understand how stories evolve and provides predictive hints for future events. |
| Approach: | They propose a temporal event understanding pipeline that integrates state-of-the-art components. |
| Outcome: | The proposed pipeline can be easily adapted to other domains, including biomedical domains. |
Summarization as Indirect Supervision for Relation Extraction (2022.findings-emnlp)
Copied to clipboard
| Challenge: | Relation extraction (RE) models rely on training data with expensive annotations . et al., 2018; Zhao e.t al, 2018) . |
| Approach: | They propose a method that converts RE into a summarization formulation by using constraint decoding techniques. |
| Outcome: | The proposed method improves relation extraction models with high-resource and high-contrast inferences. |
Inferring from Logits: Exploring Best Practices for Decoding-Free Generative Candidate Selection (2025.acl-long)
Copied to clipboard
| Challenge: | Existing work has been using decoding-free candidate selection methods to obtain candidate probability from initial output logits over vocabulary. |
| Approach: | They propose to evaluate a set of tasks using decoding-free candidate selection methods on a comprehensive set of questions. |
| Outcome: | The proposed methods are evaluated on a set of tasks including five multiple-choice QA tasks with a small candidate pool and four clinical decision tasks with 10k+ options. |
Implicit Discourse Relation Identification for Open-domain Dialogues (P19-1)
Copied to clipboard
| Challenge: | Discourse relation identification is a challenging problem in open-domain dialogue systems . previous work relies on formal text but this data is not suitable for informal dialogue . |
| Approach: | They propose a method to automatically extract the implicit discourse relation argument pairs from dialogic turns and a pipeline to identify them. |
| Outcome: | The proposed pipeline extracts argument pairs from dialogic turns and improves it by performing feature ablation and incorporating dialogue features. |
DICE: Data-Efficient Clinical Event Extraction with Generative Models (2023.acl-long)
Copied to clipboard
| Challenge: | EE tasks target specific domains with vague entity boundaries, resulting in a lack of training data. |
| Approach: | They propose a robust and data-efficient generative model for clinical event extraction . they frame event extraction as a conditional generation problem and introduce a contrastive learning objective to decide the boundaries of biomedical mentions. |
| Outcome: | The proposed model is robust and data-efficient for clinical event extraction . it trains an auxiliary mention identification task and event extraction tasks to better identify entity mention boundaries . |