mlsanityData to 7 Oct 2026

Adversarial Domain Adaptation for Duplicate Question Detection

arXiv 1809.02255 ↗First posted 7 Sep 2018cs.CLcited by 30

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Abstract (arXiv)

We address the problem of detecting duplicate questions in forums, which is an important step towards automating the process of answering new questions. As finding and annotating such potential duplicates manually is very tedious and costly, automatic methods based on machine learning are a viable alternative. However, many forums do not have annotated data, i.e., questions labeled by experts as duplicates, and thus a promising solution is to use domain adaptation from another forum that has such annotations. Here we focus on adversarial domain adaptation, deriving important findings about when it performs well and what properties of the domains are important in this regard. Our experiments with StackExchange data show an average improvement of 5.6% over the best baseline across multiple pairs of domains.

Key facts. Cited by 30 papers in the map (data through 7 Oct 2026). First posted on arXiv 7 Sep 2018.

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Citing papers per year: 2021: 7; 2022: 8; 2023: 3; 2024: 5; 2025: 0; 2026: 7.

30 papers.

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  1. 2021BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Modelscited by 1,225
  2. 2022MTEB: Massive Text Embedding Benchmarkcited by 623
  3. 2022A Holistic Approach to Undesired Content Detection in the Real Worldcited by 337
  4. 2021UDALM: Unsupervised Domain Adaptation through Language Modelingcited by 33
  5. 2021Cross-Domain Label-Adaptive Stance Detectioncited by 30
  6. 2021Factorising Meaning and Form for Intent-Preserving Paraphrasingcited by 22
  7. 2022Hierarchical Sketch Induction for Paraphrase Generationcited by 21
  8. 2022Searching for Optimal Subword Tokenization in Cross-domain NERcited by 17
  9. 2022Improving the Sample Efficiency of Prompt Tuning with Domain Adaptationcited by 15
  10. 2023Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Casescited by 14
  11. 2024Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisionscited by 11
  12. 2024Beyond Borders: Investigating Cross-Jurisdiction Transfer in Legal Case Summarizationcited by 8
18 more
  1. 2022Explainable Misinformation Detection Across Multiple Social Media Platformscited by 5
  2. 2026CSRv2: Unlocking Ultra-Sparse Embeddingscited by 5
  3. 2022Domain Confused Contrastive Learning for Unsupervised Domain Adaptationcited by 4
  4. 2023Smart Word Suggestions for Writing Assistancecited by 4
  5. 2026The Embedder's Dilemma: LLMs Are Better, but at What Cost?cited by 4
  6. 2021Unsupervised Domain Adaptation with Global and Local Graph Neural Networks in Limited Labeled Data Scenario: Application to Disaster Managementcited by 2
  7. 2023Non-contrastive sentence representations via self-supervisioncited by 2
  8. 2024Quantum Transfer Learning for Acceptability Judgementscited by 2
  9. 2021Predicting the Success of Domain Adaptation in Text Similaritycited by 1
  10. 2021Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selectioncited by 1
  11. 2022Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systemscited by 1
  12. 2024GASE: Generatively Augmented Sentence Encodingcited by 1
  13. 2026Learning Concepts, Not Tokens: Self-Supervised Semantic Alignment for Language Modelscited by 1
  14. 2026Layer-wise Representation Dynamics: An Empirical Investigation Across Embedders and Base LLMscited by 1
  15. 2026The Harder Text Embedding Benchmark (HTEB): Beyond One-dimensional Static Robustnesscited by 1
  16. 2024Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learningcited by 0
  17. 2026Geometry-Preserving Aggregation for Mixture-of-Experts Embedding Modelscited by 0
  18. 2026R-DEIM Net: An Efficient Rationale-Augmented Dual-Expert Interaction Model for Paraphrase Detectioncited by 0