no code implementations • 17 Apr 2024 • Yukiko Ishizuki, Tatsuki Kuribayashi, Yuichiroh Matsubayashi, Ryohei Sasano, Kentaro Inui
Speakers sometimes omit certain arguments of a predicate in a sentence; such omission is especially frequent in pro-drop languages.
no code implementations • 6 Mar 2024 • Naoki Miura, Hiroaki Funayama, Seiya Kikuchi, Yuichiroh Matsubayashi, Yuya Iwase, Kentaro Inui
Using this dataset, we demonstrate the performance of baselines including finetuned BERT and GPT models with few-shot in-context learning.
no code implementations • 16 Jun 2022 • Hiroaki Funayama, Tasuku Sato, Yuichiroh Matsubayashi, Tomoya Mizumoto, Jun Suzuki, Kentaro Inui
Towards guaranteeing high-quality predictions, we present the first study of exploring the use of human-in-the-loop framework for minimizing the grading cost while guaranteeing the grading quality by allowing a SAS model to share the grading task with a human grader.
1 code implementation • EMNLP 2021 • Ryuto Konno, Shun Kiyono, Yuichiroh Matsubayashi, Hiroki Ouchi, Kentaro Inui
Masked language models (MLMs) have contributed to drastic performance improvements with regard to zero anaphora resolution (ZAR).
no code implementations • COLING 2020 • Ryuto Konno, Yuichiroh Matsubayashi, Shun Kiyono, Hiroki Ouchi, Ryo Takahashi, Kentaro Inui
This study addresses two underexplored issues on CDA, that is, how to reduce the computational cost of data augmentation and how to ensure the quality of the generated data.
no code implementations • ACL 2020 • Hiroaki Funayama, Shota Sasaki, Yuichiroh Matsubayashi, Tomoya Mizumoto, Jun Suzuki, Masato Mita, Kentaro Inui
We introduce a new task formulation of SAS that matches the actual usage.
no code implementations • COLING 2018 • Yuichiroh Matsubayashi, Kentaro Inui
Capturing interactions among multiple predicate-argument structures (PASs) is a crucial issue in the task of analyzing PAS in Japanese.
1 code implementation • NAACL 2018 • Kento Watanabe, Yuichiroh Matsubayashi, Satoru Fukayama, Masataka Goto, Kentaro Inui, Tomoyasu Nakano
This paper presents a novel, data-driven language model that produces entire lyrics for a given input melody.
no code implementations • IJCNLP 2017 • Yuichiroh Matsubayashi, Kentaro Inui
The research trend in Japanese predicate-argument structure (PAS) analysis is shifting from pointwise prediction models with local features to global models designed to search for globally optimal solutions.
no code implementations • COLING 2016 • Kento Watanabe, Yuichiroh Matsubayashi, Naho Orita, Naoaki Okazaki, Kentaro Inui, Satoru Fukayama, Tomoyasu Nakano, Jordan Smith, Masataka Goto
This study proposes a computational model of the discourse segments in lyrics to understand and to model the structure of lyrics.
no code implementations • COLING 2016 • Naoya Inoue, Yuichiroh Matsubayashi, Masayuki Ono, Naoaki Okazaki, Kentaro Inui
This paper proposes a novel problem setting of selectional preference (SP) between a predicate and its arguments, called as context-sensitive SP (CSP).
no code implementations • LREC 2012 • Yuichiroh Matsubayashi, Yusuke Miyao, Akiko Aizawa
In this paper, we report our framework for creating the corpus and the current status of creating an LCS dictionary for Japanese predicates.