mlsanityData to 7 Oct 2026

A Compare-Aggregate Model for Matching Text Sequences

arXiv 1611.01747 ↗First posted 6 Nov 2016cs.CL cs.AIcited by 23

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Abstract (arXiv)

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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Nov 2016.

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Citing papers per year: 2021: 8; 2022: 7; 2023: 4; 2024: 0; 2025: 4.

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