Papers by Yujin Takahashi
Construction of a Quality Estimation Dataset for Automatic Evaluation of Japanese Grammatical Error Correction (2022.lrec-1)
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Daisuke Suzuki, Yujin Takahashi, Ikumi Yamashita, Taichi Aida, Tosho Hirasawa, Michitaka Nakatsuji, Masato Mita, Mamoru Komachi
| Challenge: | Existing studies on automatic evaluation of grammatical error correction (GEC) have shown that quality estimation models built from manual evaluation can achieve high performance in automatic evaluation in English. |
| Approach: | They used a dataset with manual evaluation to build an automatic evaluation model for Japanese GEC. |
| Outcome: | The proposed model is based on a Japanese dataset with manual evaluation and meta-evaluation. |
Grammatical Error Correction Using Pseudo Learner Corpus Considering Learner’s Error Tendency (2020.acl-srw)
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| Challenge: | Recent studies have focused on improving the performance of grammatical error correction (GEC) tasks using pseudo data. |
| Approach: | They propose to extract sentences similar to those written by language learners and generate pseudo errors by considering error types that learners often make. |
| Outcome: | The proposed model significantly improves the performance of the Russian GEC task compared with other models using pseudo data. |
ProQE: Proficiency-wise Quality Estimation dataset for Grammatical Error Correction (2022.lrec-1)
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| Challenge: | Prior work has shown that QE models of grammatical error correction are biased toward data by learners with relatively high proficiency levels. |
| Approach: | They investigated whether learners' proficiency affects supervised quality estimation models of grammatical error correction (GEC) . they created a QE dataset that includes multiple proficiency levels and explored the necessity of performing proficiency-wise evaluation for QE of GEC. |
| Outcome: | The proposed model is based on multiple proficiency levels and can be performed in real-world scenarios. |