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20202023
most citedA Survey of Natural Language Generation

151 citations · 217 across the 16 of their papers we have counts for

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Showing 2021Show all

6 papers · 1 filter

cs.CL2021★ 151 cited

A Survey of Natural Language Generation

Chenhe Dong, Yinghui Li, Haifan Gong +4

This paper offers a comprehensive review of the research on Natural Language Generation (NLG) over the past two decades, especially in relation to data-to-text generation and text-…

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.CL2021★ 3 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.CL2021★ 38 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…

cs.CL2021

Learning to Augment for Data-Scarce Domain BERT Knowledge Distillation

Lingyun Feng, Minghui Qiu, Yaliang Li +2

Despite pre-trained language models such as BERT have achieved appealing performance in a wide range of natural language processing tasks, they are computationally expensive to be…