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Erschienen in:
BOWL: Bag of Word Clusters Text Representation Using Word Embeddings Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Context-Aware Tree-Based Convolutional Neural Networks for Natural Language Inference

verfasst von : Zhao Meng, Lili Mou, Ge Li, Zhi Jin

Erschienen in: Knowledge Science, Engineering and Management

Verlag: Springer International Publishing

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Abstract

Natural language inference (NLI) aims to judge the relation between a premise sentence and a hypothesis sentence. In this paper, we propose a context-aware tree-based convolutional neural network (TBCNN) to improve the performance of NLI. In our method, we utilize tree-based convolutional neural networks, which are proposed in our previous work, to capture the premise’s and hypothesis’s information. In this paper, to enhance our previous model, we summarize the premise’s information in terms of both word level and convolution level by dynamic pooling and feed such information to the convolutional layer when we model the hypothesis. In this way, the tree-based convolutional sentence model is context-aware. Then we match the sentence vectors by heuristics including vector concatenation, element-wise difference/product so as to remain low computational complexity. Experiments show that the performance of our context-aware variant achieves better performance than individual TBCNNs.

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Vorheriges Kapitel A Subset Space Perspective on Agents Cooperating for Knowledge
Nächstes Kapitel Learning Embeddings of API Tokens to Facilitate Deep Learning Based Program Processing
Fußnoten
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Metadaten
Titel
Context-Aware Tree-Based Convolutional Neural Networks for Natural Language Inference
verfasst von
Zhao Meng
Lili Mou
Ge Li
Zhi Jin
Copyright-Jahr
2016
Buch
Knowledge Science, Engineering and Management

Print ISBN: 978-3-319-47649-0

Electronic ISBN: 978-3-319-47650-6

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-47650-6_41

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