# Transfer learning for conflict and duplicate detection in software requirement pairs

arXiv: https://arxiv.org/abs/2301.03709 · HTML: https://mlsanity.com/paper/2301.03709/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 60 references that are in the map.
- First posted on arXiv 9 Jan 2023.

## Builds on (most cited first, 19 of 19)

- [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)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [A large annotated corpus for learning natural language inference](https://mlsanity.com/paper/1508.05326/) (2015)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://mlsanity.com/paper/1904.05255/) (2019)
- [Robust Transfer Learning with Pretrained Language Models through Adapters](https://mlsanity.com/paper/2108.02340/) (2021)
- [Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks](https://mlsanity.com/paper/1703.06345/) (2017)
- [A Neural Grammatical Error Correction System Built On Better Pre-training and Sequential Transfer Learning](https://mlsanity.com/paper/1907.01256/) (2019)
- [Neural Network Models for Paraphrase Identification, Semantic Textual Similarity, Natural Language Inference, and Question Answering](https://mlsanity.com/paper/1806.04330/) (2018)
- [Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference](https://mlsanity.com/paper/2002.04815/) (2020)
- [Improving Natural Language Inference with a Pretrained Parser](https://mlsanity.com/paper/1909.08217/) (2019)
- [Automatically detecting the conflicts between software requirements based on finer semantic analysis](https://mlsanity.com/paper/2103.02255/) (2021)
- [The Use of NLP-Based Text Representation Techniques to Support Requirement Engineering Tasks: A Systematic Mapping Review](https://mlsanity.com/paper/2206.00421/) (2022)
- [Supervised Semantic Similarity-based Conflict Detection Algorithm: S3CDA](https://mlsanity.com/paper/2206.13690/) (2022)
- [TAPHSIR: Towards AnaPHoric Ambiguity Detection and ReSolution In Requirements](https://mlsanity.com/paper/2206.10227/) (2022)
- [ARSENAL: Automatic Requirements Specification Extraction from Natural Language](https://mlsanity.com/paper/1403.3142/) (2014)

## Built on by (most cited first, 5 of 5)

- [PassionNet: An Innovative Framework for Duplicate and Conflicting Requirements Identification](https://mlsanity.com/paper/2412.01657/) (2024)
- [GVD: Governed Versioning and Deduplication for Document Repositories](https://mlsanity.com/paper/2609.17696/) (2026)
- [Generative Language Models Potential for Requirement Engineering Applications: Insights into Current Strengths and Limitations](https://mlsanity.com/paper/2412.00959/) (2024)
- [A transfer learning approach for automatic conflicts detection in software requirement sentence pairs based on dual encoders](https://mlsanity.com/paper/2511.23007/) (2025)
- [Towards Energy-aware Requirements Dependency Classification: Knowledge-Graph vs. Vector-Retrieval Augmented Inference with SLMs](https://mlsanity.com/paper/2603.23954/) (2026)

## Abstract (arXiv)

Consistent and holistic expression of software requirements is important for the success of software projects. In this study, we aim to enhance the efficiency of the software development processes by automatically identifying conflicting and duplicate software requirement specifications. We formulate the conflict and duplicate detection problem as a requirement pair classification task. We design a novel transformers-based architecture, SR-BERT, which incorporates Sentence-BERT and Bi-encoders for the conflict and duplicate identification task. Furthermore, we apply supervised multi-stage fine-tuning to the pre-trained transformer models. We test the performance of different transfer models using four different datasets. We find that sequentially trained and fine-tuned transformer models perform well across the datasets with SR-BERT achieving the best performance for larger datasets. We also explore the cross-domain performance of conflict detection models and adopt a rule-based filtering approach to validate the model classifications. Our analysis indicates that the sentence pair classification approach and the proposed transformer-based natural language processing strategies can contribute significantly to achieving automation in conflict and duplicate detection