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20172021
most citedGenerative Adversarial Network for Abstractive Text Summarization

32 citations · 90 across the 9 of their papers we have counts for

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10 papers · 1 filter

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.CL202115 cited

Iterative Network Pruning with Uncertainty Regularization for Lifelong Sentiment Classification

Binzong Geng, Min Yang, Fajie Yuan +3

Lifelong learning capabilities are crucial for sentiment classifiers to process continuous streams of opinioned information on the Web. However, performing lifelong learning is non…

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.CL2020

BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover's Distance

Jianquan Li, Xiaokang Liu, Honghong Zhao +3

Pre-trained language models (e.g., BERT) have achieved significant success in various natural language processing (NLP) tasks. However, high storage and computational costs obstruc…

cs.CL2020

Discovering Protagonist of Sentiment with Aspect Reconstructed Capsule Network

Chi Xu, Hao Feng, Guoxin Yu +3

Most recent existing aspect-term level sentiment analysis (ATSA) approaches combined various neural network models with delicately carved attention mechanisms built upon given aspe…

cs.CL20195 cited

Improving Knowledge-aware Dialogue Generation via Knowledge Base Question Answering

Jian Wang, Junhao Liu, Wei Bi +4

Neural network models usually suffer from the challenge of incorporating commonsense knowledge into the open-domain dialogue systems. In this paper, we propose a novel knowledge-aw…