Papers by Kazuaki Hanawa
Exploring the Capacity of a Large-scale Masked Language Model to Recognize Grammatical Errors (2022.findings-acl)
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| Challenge: | a language model-based error detection method can learn errors with a small training sample. |
| Approach: | They propose a language model-based method for grammatical error detection with feedback comments. |
| Outcome: | The proposed method can learn errors with a little training data and improve recall faster than non-language models. |
Predicting Stances from Social Media Posts using Factorization Machines (C18-1)
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| Challenge: | Social media provide platforms to express, discuss, and shape opinions about events and issues in the real world. |
| Approach: | They propose to use factorization machines to model user preferences toward topics from social media data to predict whether a given text/user is in favor (agree), against (disagreer), or neutral toward a target topic. |
| Outcome: | The proposed method can predict stances of silent users based on their stance toward other topics and the social media posts of the user. |
Exploring Methods for Generating Feedback Comments for Writing Learning (2021.emnlp-main)
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| Challenge: | Existing methods for generating explanatory notes for language learners are inadequate . nagata et al. demonstrates that neural-retrieval-based methods can generate feedback comments for preposition use . |
| Approach: | They investigate three different methods for generating feedback comments for preposition use . grammatical and writing items can also be used to generate feedback comments . |
| Outcome: | The proposed methods outperform neural-retrieval-based methods in generating feedback comments for preposition use. |
Taking the Correction Difficulty into Account in Grammatical Error Correction Evaluation (2020.coling-main)
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| Challenge: | a paper aims to improve performance measures for grammatical error correction . conventional measures treat all errors equally, but some are easier to correct . |
| Approach: | They propose a way to determine the difficulty of error correction and to motivate researchers . paper examines performance measures for grammatical error correction using a scorer and weighting algorithm . |
| Outcome: | The proposed measures agree with our intuition of correction difficulty . the results show that the measures are more complex than conventional measures . |
PheMT: A Phenomenon-wise Dataset for Machine Translation Robustness on User-Generated Contents (2020.coling-main)
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| Challenge: | Existing studies suggest that Neural Machine Translation still struggles with certain kinds of input with considerable noise, such as User-Generated Contents (UGC) on the Internet. |
| Approach: | They propose to evaluate the robustness of Neural Machine Translation models against specific linguistic phenomena in Japanese-English translation. |
| Outcome: | The proposed model can handle user-generated content (UGC) on the Internet, but it is difficult to translate clean inputs. |