activity
20182020
most citedConditional BERT Contextual Augmentation

13 citations · 16 across the 3 of their papers we have counts for

collaborators

5 papers

cs.IR2020

AutoSUM: Automating Feature Extraction and Multi-user Preference Simulation for Entity Summarization

Dongjun Wei, Yaxin Liu, Fuqing Zhu +4

Withthegrowthofknowledgegraphs, entity descriptions are becoming extremely lengthy. Entity summarization task, aiming to generate diverse, comprehensive, and representative summari…

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…

cs.CL201813 cited

Conditional BERT Contextual Augmentation

Xing Wu, Shangwen Lv, Liangjun Zang +2

We propose a novel data augmentation method for labeled sentences called conditional BERT contextual augmentation. Data augmentation methods are often applied to prevent overfittin…