Papers by Chak Yan Yeung
Personalized Text Retrieval for Learners of Chinese as a Foreign Language (C18-1)
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| Challenge: | a personalized text retrieval algorithm helps language learners select the most suitable reading material in terms of vocabulary complexity. |
| Approach: | They propose a personalized text retrieval algorithm that helps language learners select the most suitable reading material in terms of vocabulary complexity. |
| Outcome: | The proposed algorithm is effective in identifying simpler texts for low-proficiency learners, and more challenging ones for high-proficient learners. |
Personalizing Lexical Simplification (C18-1)
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| Challenge: | Experimental results show that even a simple personalized CWI model can help the system avoid some unnecessary simplifications and produce more readable output. |
| Approach: | They evaluate the performance of a state-of-the-art LS system on individual learners of English at different proficiency levels and measure the benefits of using complex word identification models to personalize the system. |
| Outcome: | The proposed system produces a more readable output for learners with special needs and those with language disabilities. |
A Dataset for Investigating the Impact of Feedback on Student Revision Outcome (2020.lrec-1)
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| Challenge: | Despite numerous studies on the kinds of feedback that can best promote learning, this question remains an open debate in the area of Second Language Acquisition (SLA). |
| Approach: | They annotate a corpus of student-written sentences with teacher feedback provided for the errors. |
| Outcome: | The proposed annotation scheme and the teacher feedback dataset are based on student-written sentences in their original and revised versions with teacher feedback provided for the errors. |