Papers by Aomi Koyama
Comparison of Grammatical Error Correction Using Back-Translation Models (2021.naacl-srw)
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| Challenge: | Currently, a mainstream approach to generate pseudo data is back-translation (BT). |
| Approach: | They propose to use back-translation to generate pseudo data that contains grammatical and ungrammatically produced sentences. |
| Outcome: | The proposed methods improve or interpolate the performance of each error type compared with a single BT model with different seeds. |
Construction of an Evaluation Corpus for Grammatical Error Correction for Learners of Japanese as a Second Language (2020.lrec-1)
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| Challenge: | The Lang-8 corpus is suitable as a training dataset for machine translation-based grammatical error correction systems but it is not suitable as an evaluation dataset because corrected sentences sometimes include inappropriate sentences. |
| Approach: | They created an evaluation corpus for correcting grammatical errors made by Japanese as a second language learners using neural machine translation and statistical machine translation techniques. |
| Outcome: | The proposed corpus has less noise and its annotation scheme reflects the characteristics of the dataset, making it ideal for correcting grammatical errors in sentences written by learners of Japanese as a Second Language (JSL). |
Targeted Syntactic Evaluation for Grammatical Error Correction (2025.acl-long)
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| Challenge: | Existing evaluation datasets based on learner-produced texts are insufficient for evaluating models . Currently, sequence-to-sequence models and sequence tagging models perform well on beginner-level grammar items . |
| Approach: | They propose a new evaluation paradigm that assesses GEC models using minimal pairs of ungrammatical and grammatically paired sentences for each grammar item. |
| Outcome: | The proposed evaluation paradigm assesses models using minimal pairs of ungrammatical and grammatically-spaced sentences for each grammar item. |