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.
Recently, Wang and Jiang REF present the compare-aggregate framework for word-level matching to measure the similarity of sentences.
A Compare-Aggregate Model for Matching Text Sequences
ArXiv
ArXiv
Computer Science