Lattice CNNs for Matching Based Chinese Question Answering

Short text matching often faces the challenges that there are great word mismatch and expression diversity between the two texts, which would be further aggravated in languages like Chinese where there is no natural space to segment words explicitly. In this paper, we propose a novel lattice based CNN model (LCNs) to utilize multi-granularity information inherent in the word lattice while maintaining strong ability to deal with the introduced noisy information for matching based question answering in Chinese. We conduct extensive experiments on both document based question answering and knowledge based question answering tasks, and experimental results show that the LCNs models can significantly outperform the state-of-the-art matching models and strong baselines by taking advantages of better ability to distill rich but discriminative information from the word lattice input.

[1]  Bowen Zhou,et al.  Improved Neural Relation Detection for Knowledge Base Question Answering , 2017, ACL.

[2]  Xueqi Cheng,et al.  A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations , 2015, AAAI.

[3]  Joan Bruna,et al.  Spectral Networks and Locally Connected Networks on Graphs , 2013, ICLR.

[4]  Dongyan Zhao,et al.  A Chinese Question Answering System for Single-Relation Factoid Questions , 2017, NLPCC.

[5]  Zhipeng Xie.  Enhancing Document-Based Question Answering via Interaction Between Question Words and POS Tags , 2017, NLPCC.

[6]  Ali Farhadi,et al.  Bidirectional Attention Flow for Machine Comprehension , 2016, ICLR.

[7]  Nick Craswell,et al.  Learning to Match using Local and Distributed Representations of Text for Web Search , 2016, WWW.

[8]  Xuanjing Huang,et al.  Convolutional Deep Neural Networks for Document-Based Question Answering , 2016, NLPCC/ICCPOL.

[10]  Xueqi Cheng,et al.  MatchZoo: A Toolkit for Deep Text Matching , 2017, ArXiv.

[11]  Yelong Shen,et al.  Learning semantic representations using convolutional neural networks for web search , 2014, WWW.

[12]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Partha Talukdar,et al.  Dating Documents using Graph Convolution Networks , 2018, ACL.

[14]  Nanyun Peng,et al.  Cross-Sentence N-ary Relation Extraction with Graph LSTMs , 2017, TACL.

[15]  Anil K. Jain,et al.  On-line signature verification, , 2002, Pattern Recognit..

[16]  Nan Duan,et al.  Overview of the NLPCC-ICCPOL 2016 Shared Task: Open Domain Chinese Question Answering , 2016, NLPCC/ICCPOL.

[17]  Hang Li,et al.  Convolutional Neural Network Architectures for Matching Natural Language Sentences , 2014, NIPS.

[18]  Xavier Bresson,et al.  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.

[19]  Yue Zhang,et al.  Chinese NER Using Lattice LSTM , 2018, ACL.

[20]  Rongrong Ji,et al.  Lattice-Based Recurrent Neural Network Encoders for Neural Machine Translation , 2016, AAAI.

[21]  Vadim Sheinin,et al.  SQL-to-Text Generation with Graph-to-Sequence Model , 2018, EMNLP.

[22]  Zhiyuan Liu,et al.  Convolutional Neural Networks for Soft-Matching N-Grams in Ad-hoc Search , 2018, WSDM.

[23]  Ming-Wei Chang,et al.  Semantic Parsing via Staged Query Graph Generation: Question Answering with Knowledge Base , 2015, ACL.

[24]  Wenpeng Yin,et al.  MultiGranCNN: An Architecture for General Matching of Text Chunks on Multiple Levels of Granularity , 2015, ACL.

[25]  Diego Marcheggiani,et al.  Encoding Sentences with Graph Convolutional Networks for Semantic Role Labeling , 2017, EMNLP.

[26]  Xueqi Cheng,et al.  Text Matching as Image Recognition , 2016, AAAI.

[27]  Christopher Meek,et al.  Semantic Parsing for Single-Relation Question Answering , 2014, ACL.

[28]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[29]  Yue Zhang,et al.  A Graph-to-Sequence Model for AMR-to-Text Generation , 2018, ACL.

[30]  Yann LeCun,et al.  Signature Verification Using A "Siamese" Time Delay Neural Network , 1993, Int. J. Pattern Recognit. Artif. Intell..

[31]  Zhiguo Wang,et al.  Bilateral Multi-Perspective Matching for Natural Language Sentences , 2017, IJCAI.

[32]  Lei Yu,et al.  Deep Learning for Answer Sentence Selection , 2014, ArXiv.

[33]  Dong Liu,et al.  MIX: Multi-Channel Information Crossing for Text Matching , 2018, KDD.

[34]  Yue Zhang,et al.  Subword Encoding in Lattice LSTM for Chinese Word Segmentation , 2018, NAACL.

[35]  Tsuyoshi Murata,et al.  {m , 1934, ACML.