# Dual Path Modeling for Semantic Matching by Perceiving Subtle Conflicts

arXiv: https://arxiv.org/abs/2302.12530 · HTML: https://mlsanity.com/paper/2302.12530/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 30 references that are in the map.
- First posted on arXiv 24 Feb 2023.

## Builds on (most cited first, 18 of 18)

- [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)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://mlsanity.com/paper/1804.07461/) (2018)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [Supervised Learning of Universal Sentence Representations from Natural Language Inference Data](https://mlsanity.com/paper/1705.02364/) (2017)
- [From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification](https://mlsanity.com/paper/1602.02068/) (2016)
- [Enhanced LSTM for Natural Language Inference](https://mlsanity.com/paper/1609.06038/) (2016)
- [Semantics-aware BERT for Language Understanding](https://mlsanity.com/paper/1909.02209/) (2019)
- [Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution](https://mlsanity.com/paper/2108.12777/) (2021)
- [Syntax-BERT: Improving Pre-trained Transformers with Syntax Trees](https://mlsanity.com/paper/2103.04350/) (2021)
- [ExplainaBoard: An Explainable Leaderboard for NLP](https://mlsanity.com/paper/2104.06387/) (2021)
- [Using Prior Knowledge to Guide BERT's Attention in Semantic Textual Matching Tasks](https://mlsanity.com/paper/2102.10934/) (2021)
- [DABERT: Dual Attention Enhanced BERT for Semantic Matching](https://mlsanity.com/paper/2210.03454/) (2022)
- [Scheduled Multi-Task Learning: From Syntax to Translation](https://mlsanity.com/paper/1804.08915/) (2018)
- [Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion](https://mlsanity.com/paper/2210.08471/) (2022)
- [Multi-level Head-wise Match and Aggregation in Transformer for Textual Sequence Matching](https://mlsanity.com/paper/2001.07234/) (2020)

## Built on by (most cited first, 21 of 21)

- [Question Calibration and Multi-Hop Modeling for Temporal Question Answering](https://mlsanity.com/paper/2402.13188/) (2024)
- [SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models](https://mlsanity.com/paper/2601.03555/) (2026)
- [Not All Parameters Are Created Equal: Smart Isolation Boosts Fine-Tuning Performance](https://mlsanity.com/paper/2508.21741/) (2025)
- [LLM-Enhanced Multimodal Fusion for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2506.17966/) (2025)
- [Structural Reward Model: Enhancing Interpretability, Efficiency, and Scalability in Reward Modeling](https://mlsanity.com/paper/2509.25361/) (2025)
- [Comateformer: Combined Attention Transformer for Semantic Sentence Matching](https://mlsanity.com/paper/2412.07220/) (2024)
- [StructCoh: Structured Contrastive Learning for Context-Aware Text Semantic Matching](https://mlsanity.com/paper/2509.02033/) (2025)
- [Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models](https://mlsanity.com/paper/2604.10079/) (2026)
- [DPI: Exploiting Parameter Heterogeneity for Interference-Free Fine-Tuning](https://mlsanity.com/paper/2601.17777/) (2026)
- [MemGuard: Persisting Verifier Signals for LLM-Agent Memory Governance](https://mlsanity.com/paper/2608.21867/) (2026)
- [Sparse Growing Transformer: Training-Time Sparse Depth Allocation via Progressive Attention Looping](https://mlsanity.com/paper/2603.23998/) (2026)
- [Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning](https://mlsanity.com/paper/2604.14010/) (2026)
- [Using External knowledge to Enhanced PLM for Semantic Matching](https://mlsanity.com/paper/2505.06605/) (2025)
- [Boosting Neural Language Inference via Cascaded Interactive Reasoning](https://mlsanity.com/paper/2505.06607/) (2025)
- [Tag-Enriched Multi-Attention with Large Language Models for Cross-Domain Sequential Recommendation](https://mlsanity.com/paper/2510.09224/) (2025)
- [Temporal-Aware Heterogeneous Graph Reasoning with Multi-View Fusion for Temporal Question Answering](https://mlsanity.com/paper/2602.19569/) (2026)
- [Efficient Task Adaptation in Large Language Models via Selective Parameter Optimization](https://mlsanity.com/paper/2604.17051/) (2026)
- [Multi-Granularity Reasoning for Natural Language Inference](https://mlsanity.com/paper/2606.05181/) (2026)
- [Scope3Trace: Evidence-Based Identification and Extraction of Scope 3 GHG Emissions from Sustainability Reports](https://mlsanity.com/paper/2607.17122/) (2026)
- [AlignDiff: Exploiting Model-Intrinsic Information for Better Preference Data Selection](https://mlsanity.com/paper/2609.05899/) (2026)
- [Counterfactual Rollout Replay: Forkable Environments as Free Process Rewards for Software Engineering Agents](https://mlsanity.com/paper/2609.33875/) (2026)

## Abstract (arXiv)

Transformer-based pre-trained models have achieved great improvements in semantic matching. However, existing models still suffer from insufficient ability to capture subtle differences. The modification, addition and deletion of words in sentence pairs may make it difficult for the model to predict their relationship. To alleviate this problem, we propose a novel Dual Path Modeling Framework to enhance the model's ability to perceive subtle differences in sentence pairs by separately modeling affinity and difference semantics. Based on dual-path modeling framework we design the Dual Path Modeling Network (DPM-Net) to recognize semantic relations. And we conduct extensive experiments on 10 well-studied semantic matching and robustness test datasets, and the experimental results show that our proposed method achieves consistent improvements over baselines.