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5 papers · 2 filters
ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs
Wenpeng Yin, Hinrich Schütze, Bing Xiang +1
How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i…
Good, Better, Best: Choosing Word Embedding Context
James Cross, Bing Xiang, Bowen Zhou
We propose two methods of learning vector representations of words and phrases that each combine sentence context with structural features extracted from dependency trees. Using se…
Applying Deep Learning to Answer Selection: A Study and An Open Task
Minwei Feng, Bing Xiang, Michael R. Glass +2
We apply a general deep learning framework to address the non-factoid question answering task. Our approach does not rely on any linguistic tools and can be applied to different la…
Very Deep Multilingual Convolutional Neural Networks for LVCSR
Tom Sercu, Christian Puhrsch, Brian Kingsbury +1
Convolutional neural networks (CNNs) are a standard component of many current state-of-the-art Large Vocabulary Continuous Speech Recognition (LVCSR) systems. However, CNNs in LVCS…
Dependency-based Convolutional Neural Networks for Sentence Embedding
Mingbo Ma, Liang Huang, Bing Xiang +1
In sentence modeling and classification, convolutional neural network approaches have recently achieved state-of-the-art results, but all such efforts process word vectors sequenti…