972 citations · 2.3k across the 31 of their papers we have counts for
12 papers · 1 filter
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 (…
Multimodal Convolutional Neural Networks for Matching Image and Sentence
Lin Ma, Zhengdong Lu, Lifeng Shang +1
In this paper, we propose multimodal convolutional neural networks (m-CNNs) for matching image and sentence. Our m-CNN provides an end-to-end framework with convolutional architect…
Convolutional Neural Network Architectures for Matching Natural Language Sentences
Baotian Hu, Zhengdong Lu, Hang Li +1
Semantic matching is of central importance to many natural language tasks \cite{bordes2014semantic,RetrievalQA}. A successful matching algorithm needs to adequately model the inter…
Syntax-based Deep Matching of Short Texts
Mingxuan Wang, Zhengdong Lu, Hang Li +1
Many tasks in natural language processing, ranging from machine translation to question answering, can be reduced to the problem of matching two sentences or more generally two sho…
Context-Dependent Translation Selection Using Convolutional Neural Network
Zhaopeng Tu, Baotian Hu, Zhengdong Lu +1
We propose a novel method for translation selection in statistical machine translation, in which a convolutional neural network is employed to judge the similarity between a phrase…
Encoding Source Language with Convolutional Neural Network for Machine Translation
Fandong Meng, Zhengdong Lu, Mingxuan Wang +3
The recently proposed neural network joint model (NNJM) (Devlin et al., 2014) augments the n-gram target language model with a heuristically chosen source context window, achieving…