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Convolutional Neural Network Architectures for Matching Natural Language Sentences

Neural Information Processing Systems

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 this goal, we propose convolutional neural network models for matching two sentences, by adapting the convolutional strategy in vision and speech. The proposed models not only nicely represent the hierarchical structures of sentences with their layer-by-layer composition and pooling, but also capture the rich matching patterns at different levels. Our models are rather generic, requiring no prior knowledge on language, and can hence be applied to matching tasks of different nature and in different languages. The empirical study on a variety of matching tasks demonstrates the efficacy of the proposed model on a variety of matching tasks and its superiority to competitor models.


Text Matching as Image Recognition

AAAI Conferences

Matching two texts is a fundamental problem in many natural language processing tasks. An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score. Inspired by the success of convolutional neural network in image recognition, where neurons can capture many complicated patterns based on the extracted elementary visual patterns such as oriented edges and corners, we propose to model text matching as the problem of image recognition. Firstly, a matching matrix whose entries represent the similarities between words is constructed and viewed as an image. Then a convolutional neural network is utilized to capture rich matching patterns in a layer-by-layer way. We show that by resembling the compositional hierarchies of patterns in image recognition, our model can successfully identify salient signals such as n-gram and n-term matchings. Experimental results demonstrate its superiority against the baselines.


Understanding Convolutional Networks with APPLE : Automatic Patch Pattern Labeling for Explanation

arXiv.org Machine Learning

With the success of deep learning, recent efforts have been focused on analyzing how learned networks make their classifications. We are interested in analyzing the network output based on the network structure and information flow through the network layers. We contribute an algorithm for 1) analyzing a deep network to find neurons that are 'important' in terms of the network classification outcome, and 2)automatically labeling the patches of the input image that activate these important neurons. We propose several measures of importance for neurons and demonstrate that our technique can be used to gain insight into, and explain how a network decomposes an image to make its final classification.


Question/Answer Matching for CQA System via Combining Lexical and Sequential Information

AAAI Conferences

Community-based Question Answering (CQA) has become popular in knowledge sharing sites since it allows users to get answers to complex, detailed, and personal questions directly from other users. Large archives of historical questions and associated answers have been accumulated. Retrieving relevant historical answers that best match a question is an essential component of a CQA service. Most state of the art approaches are based on bag-of-words models, which have been proven successful in a range of text matching tasks, but are insufficient for capturing the important word sequence information in short text matching. In this paper, a new architecture is proposed to more effectively model the complicated matching relations between questions and answers. It utilises a similarity matrix which contains both lexical and sequential information. Afterwards the information is put into a deep architecture to find potentially suitable answers. The experimental study shows its potential in improving matching accuracy of question and answer.


Multi-Task Medical Concept Normalization Using Multi-View Convolutional Neural Network

AAAI Conferences

Medical concept normalization is a critical problem in biomedical research and clinical applications. In this paper, we focus on normalizing diagnostic and procedure names in Chinese discharge summaries to standard entities, which is formulated as a semantic matching problem. However, non-standard Chinese expressions, short-text normalization and heterogeneity of tasks pose critical challenges in our problem. This paper presents a general framework which introduces a tensor generator and a novel multi-view convolutional neural network (CNN) with multi-task shared structure to tackle the two tasks simultaneously. We propose that the key to address non-standard expressions and short-text problem is to incorporate a matching tensor with multiple granularities. Then multi-view CNN is adopted to extract semantic matching patterns and learn to synthesize them from different views. Finally, multi-task shared structure allows the model to exploit medical correlations between disease and procedure names to better perform disambiguation tasks. Comprehensive experimental analysis indicates our model outperforms existing baselines which demonstrates the effectiveness of our model.