mlsanityData to 14 Oct 2026

Unveiling Dual Quality in Product Reviews: An NLP-Based Approach

arXiv 2505.19254 ↗First posted 25 May 2025cs.CLcited by 0

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Abstract (arXiv)

Consumers often face inconsistent product quality, particularly when identical products vary between markets, a situation known as the dual quality problem. To identify and address this issue, automated techniques are needed. This paper explores how natural language processing (NLP) can aid in detecting such discrepancies and presents the full process of developing a solution. First, we describe in detail the creation of a new Polish-language dataset with 1,957 reviews, 540 highlighting dual quality issues. We then discuss experiments with various approaches like SetFit with sentence-transformers, transformer-based encoders, and LLMs, including error analysis and robustness verification. Additionally, we evaluate multilingual transfer using a subset of opinions in English, French, and German. The paper concludes with insights on deployment and practical applications.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 48 references that are in the map. First posted on arXiv 25 May 2025.

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References · 15

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  10. 2023Exploring Zero and Few-shot Techniques for Intent Classificationcited by 21
  11. 2023Learn over Past, Evolve for Future: Forecasting Temporal Trends for Fake News Detectioncited by 20
  12. 2024PL-MTEB: Polish Massive Text Embedding Benchmarkcited by 19
3 more
  1. 2023xPQA: Cross-Lingual Product Question Answering across 12 Languagescited by 10
  2. 2023Bringing the State-of-the-Art to Customers: A Neural Agent Assistant Framework for Customer Service Supportcited by 6
  3. 2023Consistent Text Categorization using Data Augmentation in e-Commercecited by 3

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