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20192024
most citedData Augmentation for Copy-Mechanism in Dialogue State Tracking

3 citations · 9 across the 5 of their papers we have counts for

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cs.CL20223 cited

InfoCSE: Information-aggregated Contrastive Learning of Sentence Embeddings

Xing Wu, Chaochen Gao, Zijia Lin +3

Contrastive learning has been extensively studied in sentence embedding learning, which assumes that the embeddings of different views of the same sentence are closer. The constrai…

cs.CL20203 cited

Distilling Knowledge from Pre-trained Language Models via Text Smoothing

Xing Wu, Yibing Liu, Xiangyang Zhou +1

This paper studies compressing pre-trained language models, like BERT (Devlin et al.,2019), via teacher-student knowledge distillation. Previous works usually force the student mod…

cs.CL20203 cited

Data Augmentation for Copy-Mechanism in Dialogue State Tracking

Xiaohui Song, Liangjun Zang, Yipeng Su +3

While several state-of-the-art approaches to dialogue state tracking (DST) have shown promising performances on several benchmarks, there is still a significant performance gap bet…

cs.CL2019

TransSent: Towards Generation of Structured Sentences with Discourse Marker

Xing Wu, Dongjun Wei, Liangjun Zang +2

Structured sentences are important expressions in human writings and dialogues. Previous works on neural text generation fused semantic and structural information by encoding the e…

cs.CL2019

"Mask and Infill" : Applying Masked Language Model to Sentiment Transfer

Xing Wu, Tao Zhang, Liangjun Zang +2

This paper focuses on the task of sentiment transfer on non-parallel text, which modifies sentiment attributes (e.g., positive or negative) of sentences while preserving their attr…