Zhengdong Lu

Towards Neural Network-based Reasoning

2015 · arXiv (Cornell University)

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 facts and find an answer to the question in specific forms. …

$gen$CNN: A Convolutional Architecture for Word Sequence Prediction

2015 · arXiv (Cornell University)

We propose a novel convolutional architecture, named $gen$CNN, for word sequence prediction. Different from previous work on neural network-based language modeling and generation (e.g., RNN or LSTM), we choose not to greedily summarize the history …

Neural Enquirer: Learning to Query Tables with Natural Language

2015 · arXiv (Cornell University)

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 distributed representation of a query and then executes …

Neural Responding Machine for Short-Text Conversation

2015 · arXiv (Cornell University)

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 the generation of response as a decoding process based on the latent representation …

Convolutional Neural Network Architectures for Matching Natural Language Sentences

2015 · arXiv (Cornell University)

Semantic matching is of central importance to many natural language tasks \cite{bordes2014semantic,RetrievalQA}. A successful matching algorithm needs to adequately model the internal structures of language objects and the interaction between them. As a step toward …

Neural Generative Question Answering

2015 · arXiv (Cornell University)

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 facts in a knowledge-base. More specifically, the model is built …

Incorporating Copying Mechanism in Sequence-to-Sequence Learning

2016 · arXiv (Cornell University)

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 output sequence. A similar phenomenon is observable in human …

Modeling Coverage for Neural Machine Translation

2016 · arXiv (Cornell University)

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, however, which often leads to over-translation and under-translation. To address this problem, …

Neural Generative Question Answering

2016

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 facts in a knowledge-base.More specifically, the model is built on …