1 code implementation • spnlp (ACL) 2022 • Youmi Ma, Tatsuya Hiraoka, Naoaki Okazaki
We adopt table representations to model the entities and relations, casting the entity and relation extraction as a table-labeling problem.
no code implementations • 25 Apr 2024 • Youmi Ma, An Wang, Naoaki Okazaki
In our proposal, annotators edit relation predictions from a model trained on the transferred dataset.
1 code implementation • 17 Apr 2024 • Masahiro Kaneko, Youmi Ma, Yuki Wata, Naoaki Okazaki
In this study, we propose a Sampling-based Pseudo-Likelihood (\textbf{SPL}) method for MIA (\textbf{SaMIA}) that calculates SPL using only the text generated by an LLM to detect leaks.
1 code implementation • starsem 2023 • An Wang, Junfeng Jiang, Youmi Ma, Ao Liu, Naoaki Okazaki
Aspect sentiment quad prediction (ASQP) analyzes the aspect terms, opinion terms, sentiment polarity, and aspect categories in a text.
Ranked #3 on Aspect-Based Sentiment Analysis (ABSA) on ASQP
1 code implementation • 17 Feb 2023 • Youmi Ma, An Wang, Naoaki Okazaki
First, we propose DREEAM, a memory-efficient approach that adopts evidence information as the supervisory signal, thereby guiding the attention modules of the DocRE system to assign high weights to evidence.
Ranked #1 on Relation Extraction on ReDocRED
1 code implementation • Journal of Natural Language Processing 2022 • Youmi Ma, Tatsuya Hiraoka, Naoaki Okazaki
In this study, a novel method for extracting named entities and relations from unstructured text based on the table representation is presented.
Ranked #3 on Relation Extraction on CoNLL04 (NER Micro F1 metric)
1 code implementation • ACL 2020 • Zixia Jia, Youmi Ma, Jiong Cai, Kewei Tu
Semantic dependency parsing, which aims to find rich bi-lexical relationships, allows words to have multiple dependency heads, resulting in graph-structured representations.