most citedDynamically Fused Graph Network for Multi-hop Reasoning

43 citations · 100 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CL201931 cited

Imitation Learning for Non-Autoregressive Neural Machine Translation

Bingzhen Wei, Mingxuan Wang, Hao Zhou +3

Non-autoregressive translation models (NAT) have achieved impressive inference speedup. A potential issue of the existing NAT algorithms, however, is that the decoding is conducted…

cs.CL201913 cited

Generating Sentences from Disentangled Syntactic and Semantic Spaces

Yu Bao, Hao Zhou, Shujian Huang +5

Variational auto-encoders (VAEs) are widely used in natural language generation due to the regularization of the latent space. However, generating sentences from the continuous lat…

cs.CL201943 cited

Dynamically Fused Graph Network for Multi-hop Reasoning

Yunxuan Xiao, Yanru Qu, Lin Qiu +4

Text-based question answering (TBQA) has been studied extensively in recent years. Most existing approaches focus on finding the answer to a question within a single paragraph. How…

cs.CL20172 cited

Modeling Past and Future for Neural Machine Translation

Zaixiang Zheng, Hao Zhou, Shujian Huang +4

Existing neural machine translation systems do not explicitly model what has been translated and what has not during the decoding phase. To address this problem, we propose a novel…

cs.CL201710 cited

Why Do Neural Dialog Systems Generate Short and Meaningless Replies? A Comparison between Dialog and Translation

Bolin Wei, Shuai Lu, Lili Mou +4

This paper addresses the question: Why do neural dialog systems generate short and meaningless replies? We conjecture that, in a dialog system, an utterance may have multiple equal…

cs.CL20171 cited

Dynamic Oracle for Neural Machine Translation in Decoding Phase

Zi-Yi Dou, Hao Zhou, Shu-Jian Huang +2

The past several years have witnessed the rapid progress of end-to-end Neural Machine Translation (NMT). However, there exists discrepancy between training and inference in NMT whe…