Papers with JSL
Sentence Suggestion of Japanese Functional Expressions for Chinese-speaking Learners (P18-4)
Copied to clipboard
| Challenge: | a large number of Chinese characters are commonly used both in Chinese and Japanese. |
| Approach: | They propose a computer-assisted learning system for Chinese-speaking learners of Japanese as a second language (JSL) they use a free Japanese morphological analyzer MeCab to learn Japanese functional expressions with suggestion of appropriate example sentences. |
| Outcome: | The proposed system automatically recognizes Japanese functional expressions using a free Japanese morphological analyzer and is retrained on a new conditional random field model. |
Construction of an Evaluation Corpus for Grammatical Error Correction for Learners of Japanese as a Second Language (2020.lrec-1)
Copied to clipboard
| 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). |
Deep JSLC: A Multimodal Corpus Collection for Data-driven Generation of Japanese Sign Language Expressions (L18-1)
Copied to clipboard
| Challenge: | Existing technologies for CG-supported data display are not able to depict all relevant features of a natural signing sequence such as facial expression, spatial references or inter-sign movement. |
| Approach: | They collected a corpus of Japanese Sign Language sentences for deep neural network learning. |
| Outcome: | The proposed model could be used to train language features in Japanese Sign Language (JSL) |
J-Shuwa: A Large-Scale Web-Collected Japanese Sign Language-Japanese Parallel Corpus (2026.findings-acl)
Copied to clipboard
| Challenge: | Japanese Sign Language (JSL) is a low-resource sign language that has received limited attention in the AI community due to the lack of large-scale, publicly available parallel corpora. |
| Approach: | They propose a large-scale JSL-Japanese parallel corpus constructed from YouTube videos with hard-coded subtitles and closed captions. |
| Outcome: | The proposed model is effective for training models and can be used for future research across a wide range of tasks. |