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• W2890226085 abstract "State-of-the-art networks that model relations between two pieces of text often use complex architectures and attention. In this paper, instead of focusing on architecture engineering, we take advantage of small amounts of labelled data that model semantic phenomena in text to encode matching features directly in the word representations. This greatly boosts the accuracy of our reference network, while keeping the model simple and fast to train. Our approach also beats a tree kernel model that uses similar input encodings, and neural models which use advanced attention and compare-aggregate mechanisms." @default.
• W2890226085 created "2018-09-27" @default.
• W2890226085 creator A5018267482 @default.
• W2890226085 creator A5056376686 @default.
• W2890226085 date "2018-01-01" @default.
• W2890226085 modified "2023-10-16" @default.
• W2890226085 title "Semantic Linking in Convolutional Neural Networks for Answer Sentence Selection" @default.
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• W2890226085 doi "https://doi.org/10.18653/v1/d18-1133" @default.
• W2890226085 hasPublicationYear "2018" @default.
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