# Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents

arXiv: https://arxiv.org/abs/2203.02898 · HTML: https://mlsanity.com/paper/2203.02898/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 52 references that are in the map.
- First posted on arXiv 6 Mar 2022.

## Builds on (most cited first, 30 of 34)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems](https://mlsanity.com/paper/1905.00537/) (2019)
- [SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation](https://mlsanity.com/paper/1708.00055/) (2017)
- [Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks](https://mlsanity.com/paper/1503.00075/) (2015)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [A Decomposable Attention Model for Natural Language Inference](https://mlsanity.com/paper/1606.01933/) (2016)
- [Enhanced LSTM for Natural Language Inference](https://mlsanity.com/paper/1609.06038/) (2016)
- [Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing](https://mlsanity.com/paper/2006.03236/) (2020)
- [Long Short-Term Memory-Networks for Machine Reading](https://mlsanity.com/paper/1601.06733/) (2016)
- [Bilateral Multi-Perspective Matching for Natural Language Sentences](https://mlsanity.com/paper/1702.03814/) (2017)
- [BOND: BERT-Assisted Open-Domain Named Entity Recognition with Distant Supervision](https://mlsanity.com/paper/2006.15509/) (2020)
- [Convolutional Neural Network Architectures for Matching Natural Language Sentences](https://mlsanity.com/paper/1503.03244/) (2015)
- [Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval](https://mlsanity.com/paper/1502.06922/) (2015)
- [Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning](https://mlsanity.com/paper/1906.01378/) (2019)
- [Explicit Bias Discovery in Visual Question Answering Models](https://mlsanity.com/paper/1811.07789/) (2018)
- [Distantly-Supervised Named Entity Recognition with Noise-Robust Learning and Language Model Augmented Self-Training](https://mlsanity.com/paper/2109.05003/) (2021)
- [Natural Language Inference over Interaction Space](https://mlsanity.com/paper/1709.04348/) (2017)
- [Decomposable Neural Paraphrase Generation](https://mlsanity.com/paper/1906.09741/) (2019)
- [Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach](https://mlsanity.com/paper/2005.12021/) (2020)
- [Simple and Effective Text Matching with Richer Alignment Features](https://mlsanity.com/paper/1908.00300/) (2019)
- [Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models](https://mlsanity.com/paper/2105.11136/) (2021)
- [Semantic Sentence Matching with Densely-connected Recurrent and Co-attentive Information](https://mlsanity.com/paper/1805.11360/) (2018)
- [MultiCQA: Zero-Shot Transfer of Self-Supervised Text Matching Models on a Massive Scale](https://mlsanity.com/paper/2010.00980/) (2020)
- [Sentence Similarity Learning by Lexical Decomposition and Composition](https://mlsanity.com/paper/1602.07019/) (2016)

## Built on by (most cited first, 9 of 9)

- [Disentangled Representation Learning](https://mlsanity.com/paper/2211.11695/) (2022)
- [Multi-Intent Attribute-Aware Text Matching in Searching](https://mlsanity.com/paper/2402.07788/) (2024)
- [Explanation based Bias Decoupling Regularization for Natural Language Inference](https://mlsanity.com/paper/2404.13390/) (2024)
- [WDMIR: Wavelet-Driven Multimodal Intent Recognition](https://mlsanity.com/paper/2506.10011/) (2025)
- [Pastiche Novel Generation Creating: Fan Fiction You Love in Your Favorite Author's Style](https://mlsanity.com/paper/2502.15616/) (2025)
- [Highlighting Case Studies in LLM Literature Review of Interdisciplinary System Science](https://mlsanity.com/paper/2503.16515/) (2025)
- [The Short Text Matching Model Enhanced with Knowledge via Contrastive Learning](https://mlsanity.com/paper/2304.03898/) (2023)
- [Inducing Causal Structure for Abstractive Text Summarization](https://mlsanity.com/paper/2308.12888/) (2023)
- [A General and Flexible Multi-concept Parsing Framework for Multilingual Semantic Matching](https://mlsanity.com/paper/2403.02975/) (2024)

## Abstract (arXiv)

Text semantic matching is a fundamental task that has been widely used in various scenarios, such as community question answering, information retrieval, and recommendation. Most state-of-the-art matching models, e.g., BERT, directly perform text comparison by processing each word uniformly. However, a query sentence generally comprises content that calls for different levels of matching granularity. Specifically, keywords represent factual information such as action, entity, and event that should be strictly matched, while intents convey abstract concepts and ideas that can be paraphrased into various expressions. In this work, we propose a simple yet effective training strategy for text semantic matching in a divide-and-conquer manner by disentangling keywords from intents. Our approach can be easily combined with pre-trained language models (PLM) without influencing their inference efficiency, achieving stable performance improvements against a wide range of PLMs on three benchmarks.