# Text Matching as Image Recognition

arXiv: https://arxiv.org/abs/1602.06359 · HTML: https://mlsanity.com/paper/1602.06359/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2016.

## Built on by (most cited first, 30 of 59)

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- [Learning Fine-grained Fact-Article Correspondence in Legal Cases](https://mlsanity.com/paper/2104.10726/) (2021)
- [Dialogue History Matters! Personalized Response Selectionin Multi-turn Retrieval-based Chatbots](https://mlsanity.com/paper/2103.09534/) (2021)
- [Sentence Similarity Based on Contexts](https://mlsanity.com/paper/2105.07623/) (2021)
- [BERM: Training the Balanced and Extractable Representation for Matching to Improve Generalization Ability of Dense Retrieval](https://mlsanity.com/paper/2305.11052/) (2023)
- [Match-Prompt: Improving Multi-task Generalization Ability for Neural Text Matching via Prompt Learning](https://mlsanity.com/paper/2204.02725/) (2022)
- [Graph-based Multilingual Product Retrieval in E-commerce Search](https://mlsanity.com/paper/2105.02978/) (2021)
- [Activation Space Selectable Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2408.08338/) (2024)
- [MAWSEO: Adversarial Wiki Search Poisoning for Illicit Online Promotion](https://mlsanity.com/paper/2304.11300/) (2023)
- [Hypencoder: Hypernetworks for Information Retrieval](https://mlsanity.com/paper/2502.05364/) (2025)
- [Contrastive Document Representation Learning with Graph Attention Networks](https://mlsanity.com/paper/2110.10778/) (2021)
- [Efficient Document Ranking with Learnable Late Interactions](https://mlsanity.com/paper/2406.17968/) (2024)
- [Pathway to Relevance: How Cross-Encoders Implement a Semantic Variant of BM25](https://mlsanity.com/paper/2502.04645/) (2025)
- [Match-Ignition: Plugging PageRank into Transformer for Long-form Text Matching](https://mlsanity.com/paper/2101.06423/) (2021)
- [Towards Boosting LLMs-driven Relevance Modeling with Progressive Retrieved Behavior-augmented Prompting](https://mlsanity.com/paper/2408.09439/) (2024)
- [On Length Divergence Bias in Textual Matching Models](https://mlsanity.com/paper/2109.02431/) (2021)
- [A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data](https://mlsanity.com/paper/2211.02533/) (2022)
- [Unified Embedding Based Personalized Retrieval in Etsy Search](https://mlsanity.com/paper/2306.04833/) (2023)
- [Beyond Semantics: Learning a Behavior Augmented Relevance Model with Self-supervised Learning](https://mlsanity.com/paper/2308.05379/) (2023)
- [Deep Bag-of-Words Model: An Efficient and Interpretable Relevance Architecture for Chinese E-Commerce](https://mlsanity.com/paper/2407.09395/) (2024)
- [Explainable LLM-driven Multi-dimensional Distillation for E-Commerce Relevance Learning](https://mlsanity.com/paper/2411.13045/) (2024)
- [Robust Interaction-Based Relevance Modeling for Online e-Commerce Search](https://mlsanity.com/paper/2406.02135/) (2024)
- [Boosting LLM-based Relevance Modeling with Distribution-Aware Robust Learning](https://mlsanity.com/paper/2412.12504/) (2024)
- [Modeling Relevance Ranking under the Pre-training and Fine-tuning Paradigm](https://mlsanity.com/paper/2108.05652/) (2021)
- [Embracing Structure in Data for Billion-Scale Semantic Product Search](https://mlsanity.com/paper/2110.06125/) (2021)

## Abstract (arXiv)

Matching two texts is a fundamental problem in many natural language processing tasks. An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score. Inspired by the success of convolutional neural network in image recognition, where neurons can capture many complicated patterns based on the extracted elementary visual patterns such as oriented edges and corners, we propose to model text matching as the problem of image recognition. Firstly, a matching matrix whose entries represent the similarities between words is constructed and viewed as an image. Then a convolutional neural network is utilized to capture rich matching patterns in a layer-by-layer way. We show that by resembling the compositional hierarchies of patterns in image recognition, our model can successfully identify salient signals such as n-gram and n-term matchings. Experimental results demonstrate its superiority against the baselines.