Search Results for author: Masato Mita

Found 16 papers, 6 papers with code

ProQE: Proficiency-wise Quality Estimation dataset for Grammatical Error Correction

no code implementations LREC 2022 Yujin Takahashi, Masahiro Kaneko, Masato Mita, Mamoru Komachi

This study investigates how supervised quality estimation (QE) models of grammatical error correction (GEC) are affected by the learners’ proficiency with the data.

Grammatical Error Correction

Towards Automated Document Revision: Grammatical Error Correction, Fluency Edits, and Beyond

1 code implementation23 May 2022 Masato Mita, Keisuke Sakaguchi, Masato Hagiwara, Tomoya Mizumoto, Jun Suzuki, Kentaro Inui

Natural language processing technology has rapidly improved automated grammatical error correction tasks, and the community begins to explore document-level revision as one of the next challenges.

Grammatical Error Correction Language Modelling

Proficiency Matters Quality Estimation in Grammatical Error Correction

no code implementations17 Jan 2022 Yujin Takahashi, Masahiro Kaneko, Masato Mita, Mamoru Komachi

This study investigates how supervised quality estimation (QE) models of grammatical error correction (GEC) are affected by the learners' proficiency with the data.

Grammatical Error Correction

Do Grammatical Error Correction Models Realize Grammatical Generalization?

no code implementations Findings (ACL) 2021 Masato Mita, Hitomi Yanaka

There has been an increased interest in data generation approaches to grammatical error correction (GEC) using pseudo data.

Grammatical Error Correction

Taking the Correction Difficulty into Account in Grammatical Error Correction Evaluation

1 code implementation COLING 2020 Takumi Gotou, Ryo Nagata, Masato Mita, Kazuaki Hanawa

The performance measures are based on the simple idea that the more systems successfully correct an error, the easier it is considered to be.

Grammatical Error Correction

Encoder-Decoder Models Can Benefit from Pre-trained Masked Language Models in Grammatical Error Correction

1 code implementation ACL 2020 Masahiro Kaneko, Masato Mita, Shun Kiyono, Jun Suzuki, Kentaro Inui

The answer to this question is not as straightforward as one might expect because the previous common methods for incorporating a MLM into an EncDec model have potential drawbacks when applied to GEC.

Grammatical Error Correction Language Modelling

GitHub Typo Corpus: A Large-Scale Multilingual Dataset of Misspellings and Grammatical Errors

1 code implementation LREC 2020 Masato Hagiwara, Masato Mita

The lack of large-scale datasets has been a major hindrance to the development of NLP tasks such as spelling correction and grammatical error correction (GEC).

Grammatical Error Correction Spelling Correction

An Empirical Study of Incorporating Pseudo Data into Grammatical Error Correction

1 code implementation IJCNLP 2019 Shun Kiyono, Jun Suzuki, Masato Mita, Tomoya Mizumoto, Kentaro Inui

The incorporation of pseudo data in the training of grammatical error correction models has been one of the main factors in improving the performance of such models.

Grammatical Error Correction

The AIP-Tohoku System at the BEA-2019 Shared Task

no code implementations WS 2019 Hiroki Asano, Masato Mita, Tomoya Mizumoto, Jun Suzuki

We introduce the AIP-Tohoku grammatical error correction (GEC) system for the BEA-2019 shared task in Track 1 (Restricted Track) and Track 2 (Unrestricted Track) using the same system architecture.

Grammatical Error Detection

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