Papers by Tomoya Mizumoto
An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction (D19-1)
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| Challenge: | incorporating pseudo data in the training of grammatical error correction models has been a key factor in improving performance of such models. |
| Approach: | They investigate the choice of how pseudo data should be generated or used in a grammatical error correction model and show that the results are state-of-the-art. |
| Outcome: | The proposed method achieves state-of-the-art on the CoNLL-2014 test set and the official test set of the BEA-2019 shared task without making any modifications to the model architecture. |
Persona-Consistent Dialogue Generation via Pseudo Preference Tuning (2025.coling-main)
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| Challenge: | Existing methods for improving persona consistency in dialogues require external resources. |
| Approach: | They propose a method for enhancing persona consistency in dialogue response generation using direct preference optimization using persona data. |
| Outcome: | The proposed method produces more consistent and natural responses than previous methods. |
Dialogue Systems Can Generate Appropriate Responses without the Use of Question Marks?– a Study of the Effects of “?” for Spoken Dialogue Systems – (2024.lrec-main)
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Tomoya Mizumoto, Takato Yamazaki, Katsumasa Yoshikawa, Masaya Ohagi, Toshiki Kawamoto, Toshinori Sato
| Challenge: | Existing systems for spoken dialogues do not append question marks to recognized queries . lack of punctuation marks in spoken dialogue can negatively impact comprehension . |
| Approach: | They investigate the impact of question marks on spoken dialogue systems . they analyze examples to determine which types of utterances have the impact . |
| Outcome: | The proposed method shows that question marks have a significant impact on spoken dialogue systems. |
Cross-Corpora Evaluation and Analysis of Grammatical Error Correction Models — Is Single-Corpus Evaluation Enough? (N19-1)
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| Challenge: | Existing studies have evaluated grammatical error correction models on a single corpus, but the evaluation is incomplete because the task difficulty varies depending on the corpus and conditions such as proficiency levels of the writers and essay topics. |
| Approach: | They evaluate the performance of several GEC models against various learner corpora and compare their rankings against the corpus. |
| Outcome: | The evaluation of several models against learner corpora shows that the models’ rankings vary depending on the corpus, indicating that single-corpus evaluation is insufficient for GEC models. |
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. |
Inject Rubrics into Short Answer Grading System (D19-61)
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| Challenge: | Short Answer Grading (SAG) is a task of scoring students’ answers in examinations. Existing SAG systems only predict scores based on the answers, but they ignore important evaluation criteria such as rubrics. |
| Approach: | They propose to inject rubrics into SAG models by introducing word-level attention mechanism into the model to locate information in each answer that are highly related to the score. |
| Outcome: | The proposed model outperforms the state-of-the-art model on the widely used ASAP-SAS dataset under low-resource settings. |