Papers by Chaochen Gao
Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks (2022.acl-short)
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| Challenge: | Experimental results show text smoothing outperforms data augmentation methods by a substantial margin. |
| Approach: | They propose to use a masked language model to convert a token to a smoothed representation by converting a sentence from its one-hot representation to 'controllable smoothes' they propose to combine text smoothing with other data augmentation methods to achieve better performance. |
| Outcome: | The proposed method outperforms mainstream data augmentation methods by a substantial margin on different datasets in a low-resource regime. |
ESimCSE: Enhanced Sample Building Method for Contrastive Learning of Unsupervised Sentence Embedding (2022.coling-1)
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| Challenge: | a new method for learning unsupervised sentence embeddings is proposed . unsup-SimCSE is biased because of the length information encoded into the sentence embeds . |
| Approach: | They propose a new unsupervised sentence embedding method that uses dropout to obtain positive pairs from a pre-trained Transformer encoder. |
| Outcome: | The proposed method outperforms the state-of-the-art unsup-SimCSE on a STS task. |
RaP: Redundancy-aware Video-language Pre-training for Text-Video Retrieval (2022.findings-emnlp)
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| Challenge: | sparse sampling of videos suffers from inter-modal redundancy and visual redundancies . et al., 2021) proposes to sparsestly sample frames from videos to alleviate temporal redundance . |
| Approach: | They propose to use sparse sampling to alleviate temporal redundancy in videos . they propose to penalize high-redundant video patches and text tokens . |
| Outcome: | The proposed method improves on four benchmark datasets. |
Smoothed Contrastive Learning for Unsupervised Sentence Embedding (2022.coling-1)
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| Challenge: | Unsupervised contrastive sentence embedding models use InfoNCE loss function . increasing batch size leads to performance degradation when it exceeds threshold . |
| Approach: | They propose a simple smoothing strategy upon the InfoNCE loss function to reduce the number of false-negative pairs in a batch without increasing the batch size. |
| Outcome: | The proposed smoothing strategy improves unsupervised SimCSE on semantic similarity tasks. |
InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings (2022.findings-emnlp)
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| Challenge: | Existing studies on contrastive learning for sentence embeddings are weak . researchers have started to use contrastive training to learn better unsupervised sentences. |
| Approach: | They propose an information-aggregated contrastive learning framework for learning unsupervised sentence embeddings. |
| Outcome: | The proposed framework outperforms SimCSE on several benchmark datasets w.r.t the semantic text similarity task. |