Papers by Yuichiroh Matsubayashi
Distance-Free Modeling of Multi-Predicate Interactions in End-to-End Japanese Predicate-Argument Structure Analysis (C18-1)
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| Challenge: | Existing models for analyzing PASs in Japanese are lacking in identifying elliptical arguments. |
| Approach: | They propose to extend the input and last layers of a bidirectional recurrent neural network model to capture the potential interactions among multiple PASs. |
| Outcome: | The proposed models improve prediction accuracy on a benchmark corpus and achieve state-of-the-art on standardized corpus. |
A Melody-Conditioned Lyrics Language Model (N18-1)
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Kento Watanabe, Yuichiroh Matsubayashi, Satoru Fukayama, Masataka Goto, Kentaro Inui, Tomoyasu Nakano
| Challenge: | Existing models for lyrics generation are insufficient to capture relationship between lyrics and melody. |
| Approach: | They propose a data-driven language model that generates entire lyrics for a given melody. |
| Outcome: | The proposed model generates fluent lyrics while maintaining compatibility between lyrics and melodies. |
Pseudo Zero Pronoun Resolution Improves Zero Anaphora Resolution (2021.emnlp-main)
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| Challenge: | Masked language models have contributed to drastic performance improvements with regard to zero anaphora resolution (ZAR). |
| Approach: | They propose a pretraining task that trains MLMs on anaphoric relations with explicit supervision and a finetuning method that remedies a notorious discrepancy. |
| Outcome: | The proposed method improves zero anaphora resolution in Japanese ZAR . it uses a pretrain task and finetuning task to correct the discrepancy . |
Preventing Critical Scoring Errors in Short Answer Scoring with Confidence Estimation (2020.acl-srw)
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Hiroaki Funayama, Shota Sasaki, Yuichiroh Matsubayashi, Tomoya Mizumoto, Jun Suzuki, Masato Mita, Kentaro Inui
| Challenge: | Recent Short Answer Scoring systems use Quadratic Weighted Kappa (QWK) but it is unsatisfactory when measuring their effectiveness in actual usage. |
| Approach: | They propose a task formulation of Short Answer Scoring (SAS) that matches actual usage and extracts as many scoring predictions that are not critical scoring errors (CSEs). |
| Outcome: | The proposed system predicts scores with zero critical scoring errors (CSEs) for 50% of test data at maximum by filtering out low-reliability predictions on the basis of a certain confidence estimation. |
To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in Japanese (2024.lrec-main)
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| Challenge: | Speakers sometimes omit certain arguments of a predicate in a sentence; such omission is especially frequent in pro-drop languages. |
| Approach: | They collect large-scale human annotations of whether and why a particular argument should be omitted across over 2,000 data points in Japanese, a prototypical pro-drop language. |
| Outcome: | The proposed model can explain why certain arguments are omitted in Japanese, a prototypical pro-drop language. |
Japanese-English Sentence Translation Exercises Dataset for Automatic Grading (2024.eacl-srw)
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| Challenge: | Sentence translation exercises (STEs) are used in the early stages of L2 language learning. |
| Approach: | They propose to formalize the task of automatic assessment of Sentence Translation Exercises (STEs) they use a dataset of 21 questions and 3,498 student responses to test the framework . |
| Outcome: | The proposed task is based on a dataset of 21,498 student responses between Japanese and English and compares them to baseline models with fine-tuned BERT and GPT-3.5 with few-shot learning. |
An Empirical Study of Contextual Data Augmentation for Japanese Zero Anaphora Resolution (2020.coling-main)
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| Challenge: | Existing methods to augment labeled data are limited by the scarcity of labeles . a method called contextual data augmentation (CDA) can be used to augment labels . |
| Approach: | They propose a data augmentation method that generates labeled training instances using a pretrained language model. |
| Outcome: | The proposed method can improve the quality of augmented training data compared to the conventional method. |