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20162022
most citedRecurrent Neural Networks With Limited Numerical Precision

49 citations · 86 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL2022

Text Editing as Imitation Game

Ning Shi, Bin Tang, Bo Yuan +4

Text editing, such as grammatical error correction, arises naturally from imperfect textual data. Recent works frame text editing as a multi-round sequence tagging task, where oper…

cs.CL2022

Syntax-guided Localized Self-attention by Constituency Syntactic Distance

Shengyuan Hou, Jushi Kai, Haotian Xue +5

Recent works have revealed that Transformers are implicitly learning the syntactic information in its lower layers from data, albeit is highly dependent on the quality and scale of…

cs.CL20221 cited

INFINITY: A Simple Yet Effective Unsupervised Framework for Graph-Text Mutual Conversion

Yi Xu, Luoyi Fu, Zhouhan Lin +2

Graph-to-text (G2T) generation and text-to-graph (T2G) triple extraction are two essential tasks for constructing and applying knowledge graphs. Existing unsupervised approaches tu…

cs.CL2021

Block-Skim: Efficient Question Answering for Transformer

Yue Guan, Zhengyi Li, Jingwen Leng +3

Transformer models have achieved promising results on natural language processing (NLP) tasks including extractive question answering (QA). Common Transformer encoders used in NLP…

cs.NE201636 cited

Recurrent Neural Networks With Limited Numerical Precision

Joachim Ott, Zhouhan Lin, Ying Zhang +2

Recurrent Neural Networks (RNNs) produce state-of-art performance on many machine learning tasks but their demand on resources in terms of memory and computational power are often…

cs.NE201649 cited

Recurrent Neural Networks With Limited Numerical Precision

Joachim Ott, Zhouhan Lin, Ying Zhang +2

Recurrent Neural Networks (RNNs) produce state-of-art performance on many machine learning tasks but their demand on resources in terms of memory and computational power are often…