Inter-Weighted Alignment Network for Sentence Pair Modeling

Sentence pair modeling is a crucial problem in the field of natural language processing. In this paper, we propose a model to measure the similarity of a sentence pair focusing on the interaction information. We utilize the word level similarity matrix to discover fine-grained alignment of two sentences. It should be emphasized that each word in a sentence has a different importance from the perspective of semantic composition, so we exploit two novel and efficient strategies to explicitly calculate a weight for each word. Although the proposed model only use a sequential LSTM for sentence modeling without any external resource such as syntactic parser tree and additional lexicon features, experimental results show that our model achieves state-of-the-art performance on three datasets of two tasks.

[1]  Sanja Fidler,et al.  Skip-Thought Vectors , 2015, NIPS.

[2]  Jonas Mueller,et al.  Siamese Recurrent Architectures for Learning Sentence Similarity , 2016, AAAI.

[3]  Andrew Y. Ng,et al.  Semantic Compositionality through Recursive Matrix-Vector Spaces , 2012, EMNLP.

[4]  Lukás Burget,et al.  Recurrent neural network based language model , 2010, INTERSPEECH.

[5]  Yan Pan,et al.  Modelling Sentence Pairs with Tree-structured Attentive Encoder , 2016, COLING.

[6]  Zhiguo Wang,et al.  FAQ-based Question Answering via Word Alignment , 2015, ArXiv.

[7]  Jun Zhao,et al.  Inner Attention based Recurrent Neural Networks for Answer Selection , 2016, ACL.

[8]  Christopher Potts,et al.  A large annotated corpus for learning natural language inference , 2015, EMNLP.

[9]  Phil Blunsom,et al.  A Convolutional Neural Network for Modelling Sentences , 2014, ACL.

[10]  Jeffrey Pennington,et al.  Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions , 2011, EMNLP.

[11]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..

[12]  M. Marelli,et al.  SemEval-2014 Task 1: Evaluation of Compositional Distributional Semantic Models on Full Sentences through Semantic Relatedness and Textual Entailment , 2014, *SEMEVAL.

[13]  Shuohang Wang,et al.  Learning Natural Language Inference with LSTM , 2015, NAACL.

[14]  Phil Blunsom,et al.  Reasoning about Entailment with Neural Attention , 2015, ICLR.

[15]  Jakob Uszkoreit,et al.  A Decomposable Attention Model for Natural Language Inference , 2016, EMNLP.

[16]  Larry P. Heck,et al.  Learning deep structured semantic models for web search using clickthrough data , 2013, CIKM.

[17]  Yoram Singer,et al.  Adaptive Subgradient Methods for Online Learning and Stochastic Optimization , 2011, J. Mach. Learn. Res..

[18]  Jimmy J. Lin,et al.  Multi-Perspective Sentence Similarity Modeling with Convolutional Neural Networks , 2015, EMNLP.

[19]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[20]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

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

[22]  Alexander F. Gelbukh,et al.  UNAL-NLP: Combining Soft Cardinality Features for Semantic Textual Similarity, Relatedness and Entailment , 2014, *SEMEVAL.

[23]  Zhiguo Wang,et al.  Sentence Similarity Learning by Lexical Decomposition and Composition , 2016, COLING.

[24]  Jeffrey Pennington,et al.  Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection , 2011, NIPS.

[25]  Yoon Kim,et al.  Convolutional Neural Networks for Sentence Classification , 2014, EMNLP.

[26]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[27]  Rabab Kreidieh Ward,et al.  Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[28]  Jimmy J. Lin An exploration of the principles underlying redundancy-based factoid question answering , 2007, TOIS.

[29]  Bowen Zhou,et al.  A Structured Self-attentive Sentence Embedding , 2017, ICLR.

[30]  Jimmy J. Lin,et al.  Pairwise Word Interaction Modeling with Deep Neural Networks for Semantic Similarity Measurement , 2016, NAACL.

[31]  Man Lan,et al.  ECNU: One Stone Two Birds: Ensemble of Heterogenous Measures for Semantic Relatedness and Textual Entailment , 2014, *SEMEVAL.

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

[33]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[34]  Noah A. Smith,et al.  What is the Jeopardy Model? A Quasi-Synchronous Grammar for QA , 2007, EMNLP.

[35]  Bowen Zhou,et al.  Attentive Pooling Networks , 2016, ArXiv.

[36]  Phil Blunsom,et al.  Neural Variational Inference for Text Processing , 2015, ICML.

[37]  Xuanjing Huang,et al.  Convolutional Neural Tensor Network Architecture for Community-Based Question Answering , 2015, IJCAI.

[38]  Christopher D. Manning,et al.  Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks , 2015, ACL.

[39]  Bowen Zhou,et al.  LSTM-based Deep Learning Models for non-factoid answer selection , 2015, ArXiv.

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

[41]  Yi Yang,et al.  WikiQA: A Challenge Dataset for Open-Domain Question Answering , 2015, EMNLP.

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

[43]  Phil Blunsom,et al.  Teaching Machines to Read and Comprehend , 2015, NIPS.