Papers by Kenji Kobayashi
Pointwise HSIC: A Linear-Time Kernelized Co-occurrence Norm for Sparse Linguistic Expressions (D18-1)
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| Challenge: | Empirically, PHSIC is learned thousands of times faster than an RNN-based PMI while outperforming PMI in accuracy. |
| Approach: | They propose a new kernel-based co-occurrence measure that can be applied to sparse linguistic expressions with a very short learning time. |
| Outcome: | The proposed measure can be applied to sparse linguistic expressions with a very short learning time, and is called the pointwise HSIC. |
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). |