Papers by Yufang Huang

2 papers
Cycle-Consistent Adversarial Autoencoders for Unsupervised Text Style Transfer (2020.coling-main)

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Challenge: Existing methods for unsupervised text style transfer lack parallel data and difficulties in content preservation.
Approach: They propose a neural approach to unsupervised text style transfer using non-parallel data.
Outcome: The proposed approach can be trained end-to-end on two widely-used public datasets.
Few Clean Instances Help Denoising Distant Supervision (2022.coling-1)

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Challenge: Existing distantly supervised entity relation extractors rely on noisy data for training and evaluation.
Approach: They propose a criterion for clean instance selection based on influence functions to collect sample-level evidence for recognizing good instances.
Outcome: The proposed method shows strong performance on real and synthetic noisy datasets.

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