Papers by ChangTien Lu

2 papers
Modeling the Relationship between User Comments and Edits in Document Revision (D19-1)

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Challenge: Managing collaborative documents can be difficult due to the profusion of edits and comments that multiple authors make during a document’s evolution.
Approach: They propose a hierarchical multi-layer deep neural network to model the relationship between edits and comments by encoding specific edit actions such as additions and deletions while accounting for document context.
Outcome: The proposed model outperforms baselines in a number of evaluation settings and achieves a precision@1 of 71.0% and precision@3 of 94.4% for Comment Ranking while achieving 74.4% accuracy on Edit Anchoring.
Towards More Accurate Uncertainty Estimation In Text Classification (2020.emnlp-main)

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Challenge: Existing models of uncertainty score depend on winning score, which is the maximum probability in a semantic vector.
Approach: They propose to generate accurate uncertainty score by improving the confidence of winning scores by reducing the effect of overconfidence of winning score and considering the impact of different categories simultaneously.
Outcome: The proposed model reduces the effect of overconfidence of winning score and considers impact of different categories of uncertainty simultaneously.

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