14 citations · 38 across the 10 of their papers we have counts for
10 papers
Tell Me Who Your Students Are: GPT Can Generate Valid Multiple-Choice Questions When Students' (Mis)Understanding Is Hinted
Machi Shimmei, Masaki Uto, Yuichiroh Matsubayashi +3
The primary goal of this study is to develop and evaluate an innovative prompting technique, AnaQuest, for generating multiple-choice questions (MCQs) using a pre-trained large lan…
Automatic Feedback Generation for Short Answer Questions using Answer Diagnostic Graphs
Momoka Furuhashi, Hiroaki Funayama, Yuya Iwase +5
Short-reading comprehension questions help students understand text structure but lack effective feedback. Students struggle to identify and correct errors, while manual feedback c…
Reducing the Cost: Cross-Prompt Pre-Finetuning for Short Answer Scoring
Hiroaki Funayama, Yuya Asazuma, Yuichiroh Matsubayashi +2
Automated Short Answer Scoring (SAS) is the task of automatically scoring a given input to a prompt based on rubrics and reference answers. Although SAS is useful in real-world app…
To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in Japanese
Yukiko Ishizuki, Tatsuki Kuribayashi, Yuichiroh Matsubayashi +2
Speakers sometimes omit certain arguments of a predicate in a sentence; such omission is especially frequent in pro-drop languages. This study addresses a question about ellipsis -…
Japanese-English Sentence Translation Exercises Dataset for Automatic Grading
Naoki Miura, Hiroaki Funayama, Seiya Kikuchi +3
This paper proposes the task of automatic assessment of Sentence Translation Exercises (STEs), that have been used in the early stage of L2 language learning. We formalize the task…
Balancing Cost and Quality: An Exploration of Human-in-the-loop Frameworks for Automated Short Answer Scoring
Hiroaki Funayama, Tasuku Sato, Yuichiroh Matsubayashi +3
Short answer scoring (SAS) is the task of grading short text written by a learner. In recent years, deep-learning-based approaches have substantially improved the performance of SA…
Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution
Ryuto Konno, Shun Kiyono, Yuichiroh Matsubayashi +2
Masked language models (MLMs) have contributed to drastic performance improvements with regard to zero anaphora resolution (ZAR). To further improve this approach, in this study, w…
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution
Ryuto Konno, Yuichiroh Matsubayashi, Shun Kiyono +3
One critical issue of zero anaphora resolution (ZAR) is the scarcity of labeled data. This study explores how effectively this problem can be alleviated by data augmentation. We ad…
Distance-Free Modeling of Multi-Predicate Interactions in End-to-End Japanese Predicate-Argument Structure Analysis
Yuichiroh Matsubayashi, Kentaro Inui
Capturing interactions among multiple predicate-argument structures (PASs) is a crucial issue in the task of analyzing PAS in Japanese. In this paper, we propose new Japanese PAS a…
Revisiting the Design Issues of Local Models for Japanese Predicate-Argument Structure Analysis
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…