# A transfer learning approach for automatic conflicts detection in software requirement sentence pairs based on dual encoders

arXiv: https://arxiv.org/abs/2511.23007 · HTML: https://mlsanity.com/paper/2511.23007/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 56 references that are in the map.
- First posted on arXiv 28 Nov 2025.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://mlsanity.com/paper/2104.08821/) (2021)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [Syntactic Structure from Deep Learning](https://mlsanity.com/paper/2004.10827/) (2020)
- [LESSON: Multi-Label Adversarial False Data Injection Attack for Deep Learning Locational Detection](https://mlsanity.com/paper/2401.16001/) (2024)
- [Reliable Estimation of KL Divergence using a Discriminator in Reproducing Kernel Hilbert Space](https://mlsanity.com/paper/2109.14688/) (2021)
- [Automatically detecting the conflicts between software requirements based on finer semantic analysis](https://mlsanity.com/paper/2103.02255/) (2021)
- [Confidence Adaptive Regularization for Deep Learning with Noisy Labels](https://mlsanity.com/paper/2108.08212/) (2021)
- [Supervised Semantic Similarity-based Conflict Detection Algorithm: S3CDA](https://mlsanity.com/paper/2206.13690/) (2022)
- [Transfer learning for conflict and duplicate detection in software requirement pairs](https://mlsanity.com/paper/2301.03709/) (2023)
- [Towards Causality Extraction from Requirements](https://mlsanity.com/paper/2006.15871/) (2020)
- [Dronology: An Incubator for Cyber-Physical System Research](https://mlsanity.com/paper/1804.02423/) (2018)
- [Data Augmentation for Conflict and Duplicate Detection in Software Engineering Sentence Pairs](https://mlsanity.com/paper/2305.09608/) (2023)

## Abstract (arXiv)

Software Requirement Document (RD) typically contain tens of thousands of individual requirements, and ensuring consistency among these requirements is critical for the success of software engineering projects. Automated detection methods can significantly enhance efficiency and reduce costs; however, existing approaches still face several challenges, including low detection accuracy on imbalanced data, limited semantic extraction due to the use of a single encoder, and suboptimal performance in cross-domain transfer learning. To address these issues, this paper proposes a Transferable Software Requirement Conflict Detection Framework based on SBERT and SimCSE, termed TSRCDF-SS. First, the framework employs two independent encoders, Sentence-BERT (SBERT) and Simple Contrastive Sentence Embedding (SimCSE), to generate sentence embeddings for requirement pairs, followed by a six-element concatenation strategy. Furthermore, the classifier is enhanced by a two-layer fully connected feedforward neural network (FFNN) with a hybrid loss optimization strategy that integrates a variant of Focal Loss, domain-specific constraints, and a confidence-based penalty term. Finally, the framework synergistically integrates sequential and cross-domain transfer learning. Experimental results demonstrate that the proposed framework achieves a 10.4% improvement in both macro-F1 and weighted-F1 scores in in-domain settings, and an 11.4% increase in macro-F1 in cross-domain scenarios.