Papers by Junxian Lin

2 papers
Benchmarking Automated Clinical Language Simplification: Dataset, Algorithm, and Evaluation (2022.coling-1)

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Challenge: Existing studies to translate medical jargon into layperson-understandable language focus on accuracy and readability aspects of clinical language.
Approach: They propose to construct a dataset to support automated clinical language simplification and propose a model that mimics the human annotation procedure.
Outcome: The proposed model matches human annotation procedures and achieves state-of-the-art performance compared with baselines.
Choosing Transfer Languages for Cross-Lingual Learning (P19-1)

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Challenge: Cross-lingual transfer is a useful tool for improving performance of natural language processing (NLP) on low-resource languages.
Approach: They propose to use cross-lingual transfer to improve accuracy of low-resource languages . they build models that consider features to perform prediction on such languages based on ranking problem .
Outcome: The proposed model predicts good transfer languages much better than baselines considering single features in isolation.

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