Papers by Youmi Ma
Building a Japanese Document-Level Relation Extraction Dataset Assisted by Cross-Lingual Transfer (2024.lrec-main)
Copied to clipboard
| Challenge: | Document-level Relation Extraction (DocRE) is the task of extracting all semantic relationships from a document. |
| Approach: | They propose to transfer an English document to Japanese to promote DocRE in other languages. |
| Outcome: | The proposed model reduces the human edit steps by 50% compared with the previous approach. |
Sampling-based Pseudo-Likelihood for Membership Inference Attacks (2025.findings-acl)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are trained on large-scale web data, which makes it difficult to grasp the contribution of each text. |
| Approach: | They propose a membership-inference attack method that uses only the input text to detect leaks. |
| Outcome: | The proposed method performs on par with existing likelihood-based methods even without likelihoods. |
Semi-Supervised Semantic Dependency Parsing Using CRF Autoencoders (2020.acl-main)
Copied to clipboard
| Challenge: | Semantic dependency parsing allows words to have multiple dependency heads, resulting in graph-structured representations. |
| Approach: | They propose an approach to semi-supervised learning of semantic dependency parsers based on the CRF autoencoder framework. |
| Outcome: | The proposed model improves over the baseline model and is arc-factored. |
Generative Data Augmentation for Aspect Sentiment Quad Prediction (2023.starsem-1)
Copied to clipboard
| Challenge: | Existing approaches to analyze text contain rewrites and inconsistency between text and quads. |
| Approach: | They propose a new approach to analyze aspect terms, opinion terms, sentiment polarity in text . they augment quads and train a quads-to-text model to generate corresponding texts . |
| Outcome: | The proposed method outperforms existing methods and achieves state-of-the-art performance on two datasets. |
From Interpretability to Performance: Optimizing Retrieval Heads for Long-Context Language Models (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent studies on mechanistic interpretability revealed that long-context factuality is closely related to a set of attention heads, retrieval heads. |
| Approach: | They propose a method that generates training signals by contrasting normal model outputs with those from an ablated variant. |
| Outcome: | The proposed method achieves significant improvements on LLMs with a sparse retrieval score distribution. |
DREEAM: Guiding Attention with Evidence for Improving Document-Level Relation Extraction (2023.eacl-main)
Copied to clipboard
| Challenge: | Document-level relation extraction (DocRE) is a task of identifying relations between entities in a document. evidence retrieval (ER) in DocRE faces two major issues: high memory consumption and limited availability of annotations. |
| Approach: | They propose a memory-efficient approach that uses evidence as the supervisory signal . they propose er self-training to learn ER from automatically-generated evidence . |
| Outcome: | The proposed method exhibits state-of-the-art performance on the DocRED benchmark . it uses evidence as the supervisory signal and self-trains on massive data without annotations . |