Papers by Kengo Hotate
Controlling Grammatical Error Correction Using Word Edit Rate (P19-2)
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| Challenge: | Existing models for grammatical error correction only consider the single degree of correction suited for training corpus. |
| Approach: | They propose a neural grammar error correction method that can control the degree of correction by using new training data annotated with word edit rate. |
| Outcome: | The proposed method improves correction accuracy by using training data annotated with word edit rate. |
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. |
Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation (2021.naacl-srw)
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| Challenge: | Neural machine translation is known to show poor performance at long sentence translations . however, when the sentence length exceeds a certain value, the quality of NMT becomes inferior to that of statistical machine translation. |
| Approach: | They propose a method that uses given parallel corpora as train data to generate long sentences by concatenating two sentences at random. |
| Outcome: | The proposed method improves translation quality more when combined with back-translation. |
Generating Diverse Corrections with Local Beam Search for Grammatical Error Correction (2020.coling-main)
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| Challenge: | Existing methods of acquiring diverse outputs focus on revising all tokens of a sentence. |
| Approach: | They propose a beam search method to obtain diverse outputs in a local sequence transduction task where most of the tokens in the source and target sentences overlap. |
| Outcome: | The proposed method generates more diverse corrections without losing accuracy in the local sequence transduction task. |