activity
20202022
most citedPrototypical Representation Learning for Relation Extraction

38 citations · 43 across the 7 of their papers we have counts for

collaborators

8 papers

cs.CL20221 cited

Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing Generation

Shulin Huang, Shirong Ma, Yinghui Li +4

Controllable Text Generation (CTG) has obtained great success due to its fine-grained generation ability obtained by focusing on multiple attributes. However, most existing CTG res…

cs.CL20221 cited

Linguistic Rules-Based Corpus Generation for Native Chinese Grammatical Error Correction

Shirong Ma, Yinghui Li, Rongyi Sun +9

Chinese Grammatical Error Correction (CGEC) is both a challenging NLP task and a common application in human daily life. Recently, many data-driven approaches are proposed for the…

cs.CL2021

HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model Compression

Chenhe Dong, Yaliang Li, Ying Shen +1

On many natural language processing tasks, large pre-trained language models (PLMs) have shown overwhelming performances compared with traditional neural network methods. Neverthel…

cs.CL2021

Continual Learning for Task-oriented Dialogue System with Iterative Network Pruning, Expanding and Masking

Binzong Geng, Fajie Yuan, Qiancheng Xu +3

This ability to learn consecutive tasks without forgetting how to perform previously trained problems is essential for developing an online dialogue system. This paper proposes an…

cs.CL20213 cited

Contextualized Knowledge-aware Attentive Neural Network: Enhancing Answer Selection with Knowledge

Yang Deng, Yuexiang Xie, Yaliang Li +3

Answer selection, which is involved in many natural language processing applications such as dialog systems and question answering (QA), is an important yet challenging task in pra…

cs.CL202138 cited

Prototypical Representation Learning for Relation Extraction

Ning Ding, Xiaobin Wang, Yao Fu +7

Recognizing relations between entities is a pivotal task of relational learning. Learning relation representations from distantly-labeled datasets is difficult because of the abund…