Hang Li

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 …

Effective Representing of Information Network by Variational Autoencoder

2017

Network representation is the basis of many applications and of extensive interest in various fields, such as information retrieval, social network analysis, and recommendation systems. Most previous methods for network representation only consider the incomplete …

Unbiased LambdaMART: An Unbiased Pairwise Learning-to-Rank Algorithm

2018 · arXiv (Cornell University)

Although click data is widely used in search systems in practice, so far the inherent bias, most notably position bias, has prevented it from being used in training of a ranker for search, i.e., learning-to-rank. …

Heralding quantum entanglement between two room-temperature atomic ensembles

2021 · Optica

Establishing quantum entanglement between individual nodes is crucial for building large-scale quantum networks, enabling secure quantum communications, distributed quantum computing, enhanced quantum metrology, and fundamental tests of quantum mechanics. However, the shared entanglements have been …

CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations

2021 · arXiv (Cornell University)

Existing audio-language task-specific predictive approaches focus on building complicated late-fusion mechanisms. However, these models are facing challenges of overfitting with limited labels and low model generalization abilities. In this paper, we present a Cross-modal Transformer …

Secoco: Self-Correcting Encoding for Neural Machine Translation

2021 · arXiv (Cornell University)

This paper presents Self-correcting Encoding (Secoco), a framework that effectively deals with input noise for robust neural machine translation by introducing self-correcting predictors. Different from previous robust approaches, Secoco enables NMT to explicitly correct noisy …

Improving Query Representations for Dense Retrieval with Pseudo Relevance Feedback: A Reproducibility Study

2021 · arXiv (Cornell University)

Pseudo-Relevance Feedback (PRF) utilises the relevance signals from the top-k passages from the first round of retrieval to perform a second round of retrieval aiming to improve search effectiveness. A recent research direction has been …

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, …

Paraphrase Generation with Deep Reinforcement Learning

2018

Automatic generation of paraphrases from a given sentence is an important yet challenging task in natural language processing (NLP). In this paper, we present a deep reinforcement learning approach to paraphrase generation. Specifically, we propose …

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 …