A Compare-Aggregate Model for Matching Text Sequences

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Authors Shuohang Wang, Jing Jiang
Journal/Conference Name 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings
Paper Category
Paper Abstract Many NLP tasks including machine comprehension, answer selection and text entailment require the comparison between sequences. Matching the important units between sequences is a key to solve these problems. In this paper, we present a general "compare-aggregate" framework that performs word-level matching followed by aggregation using Convolutional Neural Networks. We particularly focus on the different comparison functions we can use to match two vectors. We use four different datasets to evaluate the model. We find that some simple comparison functions based on element-wise operations can work better than standard neural network and neural tensor network.
Date of publication 2016
Code Programming Language Multiple

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