activity
20162023
most citedCombining Discrete and Neural Features for Sequence Labeling

36 citations · 89 across the 17 of their papers we have counts for

collaborators

14 papers

cs.CL20231 cited

Opinion Tree Parsing for Aspect-based Sentiment Analysis

Xiaoyi Bao, Xiaotong Jiang, Zhongqing Wang +2

Extracting sentiment elements using pre-trained generative models has recently led to large improvements in aspect-based sentiment analysis benchmarks. However, these models always…

cs.CL2023

Target-Side Augmentation for Document-Level Machine Translation

Guangsheng Bao, Zhiyang Teng, Yue Zhang

Document-level machine translation faces the challenge of data sparsity due to its long input length and a small amount of training data, increasing the risk of learning spurious p…

cs.CL20232 cited

NaSGEC: a Multi-Domain Chinese Grammatical Error Correction Dataset from Native Speaker Texts

Yue Zhang, Bo Zhang, Haochen Jiang +4

We introduce NaSGEC, a new dataset to facilitate research on Chinese grammatical error correction (CGEC) for native speaker texts from multiple domains. Previous CGEC research prim…

cs.HC2023

EASE: An Easily-Customized Annotation System Powered by Efficiency Enhancement Mechanisms

Naihao Deng, Yikai Liu, Mingye Chen +5

The performance of current supervised AI systems is tightly connected to the availability of annotated datasets. Annotations are usually collected through annotation tools, which a…

cs.CL20221 cited

Lost in Context? On the Sense-wise Variance of Contextualized Word Embeddings

Yile Wang, Yue Zhang

Contextualized word embeddings in language models have given much advance to NLP. Intuitively, sentential information is integrated into the representation of words, which can help…

cs.CL20221 cited

Exploiting Unlabeled Data for Target-Oriented Opinion Words Extraction

Yidong Wang, Hao Wu, Ao Liu +6

Target-oriented Opinion Words Extraction (TOWE) is a fine-grained sentiment analysis task that aims to extract the corresponding opinion words of a given opinion target from the se…