972 citations · 2.3k across the 31 of their papers we have counts for
31 papers
Self-Balanced Dropout
Shen Li, Chenhao Su, Renfen Hu +1
Dropout is known as an effective way to reduce overfitting via preventing co-adaptations of units. In this paper, we theoretically prove that the co-adaptation problem still exists…
Zooming Network
Yukun Yan, Daqi Zheng, Zhengdong Lu +1
Structural information is important in natural language understanding. Although some current neural net-based models have a limited ability to take local syntactic information, the…
Neural Entity Reasoner for Global Consistency in NER
Xiaoxiao Yin, Daqi Zheng, Zhengdong Lu +1
We propose Neural Entity Reasoner (NE-Reasoner), a framework to introduce global consistency of recognized entities into Neural Reasoner over Named Entity Recognition (NER) task. G…
Generalize Symbolic Knowledge With Neural Rule Engine
Shen Li, Hengru Xu, Zhengdong Lu
As neural networks have dominated the state-of-the-art results in a wide range of NLP tasks, it attracts considerable attention to improve the performance of neural models by integ…
JUMPER: Learning When to Make Classification Decisions in Reading
Xianggen Liu, Lili Mou, Haotian Cui +2
In early years, text classification is typically accomplished by feature-based machine learning models; recently, deep neural networks, as a powerful learning machine, make it poss…
Event Identification as a Decision Process with Non-linear Representation of Text
Yukun Yan, Daqi Zheng, Zhengdong Lu +1
We propose scale-free Identifier Network(sfIN), a novel model for event identification in documents. In general, sfIN first encodes a document into multi-scale memory stacks, then…
Object-oriented Neural Programming (OONP) for Document Understanding
Zhengdong Lu, Xianggen Liu, Haotian Cui +2
We propose Object-oriented Neural Programming (OONP), a framework for semantically parsing documents in specific domains. Basically, OONP reads a document and parses it into a pred…
Deep Neural Machine Translation with Linear Associative Unit
Mingxuan Wang, Zhengdong Lu, Jie Zhou +1
Deep Neural Networks (DNNs) have provably enhanced the state-of-the-art Neural Machine Translation (NMT) with their capability in modeling complex functions and capturing complex l…
Coupling Distributed and Symbolic Execution for Natural Language Queries
Lili Mou, Zhengdong Lu, Hang Li +1
Building neural networks to query a knowledge base (a table) with natural language is an emerging research topic in deep learning. An executor for table querying typically requires…
Interactive Attention for Neural Machine Translation
Fandong Meng, Zhengdong Lu, Hang Li +1
Conventional attention-based Neural Machine Translation (NMT) conducts dynamic alignment in generating the target sentence. By repeatedly reading the representation of source sente…
Neural Machine Translation Advised by Statistical Machine Translation
Xing Wang, Zhengdong Lu, Zhaopeng Tu +3
Neural Machine Translation (NMT) is a new approach to machine translation that has made great progress in recent years. However, recent studies show that NMT generally produces flu…
Context Gates for Neural Machine Translation
Zhaopeng Tu, Yang Liu, Zhengdong Lu +2
In neural machine translation (NMT), generation of a target word depends on both source and target contexts. We find that source contexts have a direct impact on the adequacy of a…
Memory-enhanced Decoder for Neural Machine Translation
Mingxuan Wang, Zhengdong Lu, Hang Li +1
We propose to enhance the RNN decoder in a neural machine translator (NMT) with external memory, as a natural but powerful extension to the state in the decoding RNN. This memory-e…
Neural Machine Translation with External Phrase Memory
Yaohua Tang, Fandong Meng, Zhengdong Lu +2
In this paper, we propose phraseNet, a neural machine translator with a phrase memory which stores phrase pairs in symbolic form, mined from corpus or specified by human experts. F…
Incorporating Copying Mechanism in Sequence-to-Sequence Learning
Jiatao Gu, Zhengdong Lu, Hang Li +1
We address an important problem in sequence-to-sequence (Seq2Seq) learning referred to as copying, in which certain segments in the input sequence are selectively replicated in the…
Neural Enquirer: Learning to Query Tables with Natural Language
Pengcheng Yin, Zhengdong Lu, Hang Li +1
We proposed Neural Enquirer as a neural network architecture to execute a natural language (NL) query on a knowledge-base (KB) for answers. Basically, Neural Enquirer finds the dis…
Modeling Coverage for Neural Machine Translation
Zhaopeng Tu, Zhengdong Lu, Yang Liu +2
Attention mechanism has enhanced state-of-the-art Neural Machine Translation (NMT) by jointly learning to align and translate. It tends to ignore past alignment information, howeve…
Neural Generative Question Answering
Jun Yin, Xin Jiang, Zhengdong Lu +3
This paper presents an end-to-end neural network model, named Neural Generative Question Answering (GENQA), that can generate answers to simple factoid questions, based on the fact…
Towards Neural Network-based Reasoning
Baolin Peng, Zhengdong Lu, Hang Li +1
We propose Neural Reasoner, a framework for neural network-based reasoning over natural language sentences. Given a question, Neural Reasoner can infer over multiple supporting fac…
A Deep Memory-based Architecture for Sequence-to-Sequence Learning
Fandong Meng, Zhengdong Lu, Zhaopeng Tu +2
We propose DEEPMEMORY, a novel deep architecture for sequence-to-sequence learning, which performs the task through a series of nonlinear transformations from the representation of…
Learning to Answer Questions From Image Using Convolutional Neural Network
Lin Ma, Zhengdong Lu, Hang Li
In this paper, we propose to employ the convolutional neural network (CNN) for the image question answering (QA). Our proposed CNN provides an end-to-end framework with convolution…
Self-Adaptive Hierarchical Sentence Model
Han Zhao, Zhengdong Lu, Pascal Poupart
The ability to accurately model a sentence at varying stages (e.g., word-phrase-sentence) plays a central role in natural language processing. As an effort towards this goal we pro…
Neural Responding Machine for Short-Text Conversation
Lifeng Shang, Zhengdong Lu, Hang Li
We propose Neural Responding Machine (NRM), a neural network-based response generator for Short-Text Conversation. NRM takes the general encoder-decoder framework: it formalizes th…
CNN: A Convolutional Architecture for Word Sequence Prediction
Mingxuan Wang, Zhengdong Lu, Hang Li +2
We propose a novel convolutional architecture, named CNN, for word sequence prediction. Different from previous work on neural network-based language modeling and generation (…