# Adversarial Domain Adaptation for Duplicate Question Detection

arXiv: https://arxiv.org/abs/1809.02255 · HTML: https://mlsanity.com/paper/1809.02255/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Sep 2018.

## Built on by (most cited first, 30 of 30)

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- [Improving the Sample Efficiency of Prompt Tuning with Domain Adaptation](https://mlsanity.com/paper/2210.02952/) (2022)
- [Zero-shot Transfer of Article-aware Legal Outcome Classification for European Court of Human Rights Cases](https://mlsanity.com/paper/2302.00609/) (2023)
- [Are Large Language Models Good Classifiers? A Study on Edit Intent Classification in Scientific Document Revisions](https://mlsanity.com/paper/2410.02028/) (2024)
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- [The Embedder's Dilemma: LLMs Are Better, but at What Cost?](https://mlsanity.com/paper/2608.12875/) (2026)
- [Domain Confused Contrastive Learning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2207.04564/) (2022)
- [Smart Word Suggestions for Writing Assistance](https://mlsanity.com/paper/2305.09975/) (2023)
- [Unsupervised Domain Adaptation with Global and Local Graph Neural Networks in Limited Labeled Data Scenario: Application to Disaster Management](https://mlsanity.com/paper/2104.01436/) (2021)
- [Non-contrastive sentence representations via self-supervision](https://mlsanity.com/paper/2310.17690/) (2023)
- [Quantum Transfer Learning for Acceptability Judgements](https://mlsanity.com/paper/2401.07777/) (2024)
- [Predicting the Success of Domain Adaptation in Text Similarity](https://mlsanity.com/paper/2106.04641/) (2021)
- [Generalised Unsupervised Domain Adaptation of Neural Machine Translation with Cross-Lingual Data Selection](https://mlsanity.com/paper/2109.04292/) (2021)
- [Using Interventions to Improve Out-of-Distribution Generalization of Text-Matching Recommendation Systems](https://mlsanity.com/paper/2210.10636/) (2022)
- [GASE: Generatively Augmented Sentence Encoding](https://mlsanity.com/paper/2411.04914/) (2024)
- [Learning Concepts, Not Tokens: Self-Supervised Semantic Alignment for Language Models](https://mlsanity.com/paper/2603.29123/) (2026)
- [Layer-wise Representation Dynamics: An Empirical Investigation Across Embedders and Base LLMs](https://mlsanity.com/paper/2605.12714/) (2026)
- [The Harder Text Embedding Benchmark (HTEB): Beyond One-dimensional Static Robustness](https://mlsanity.com/paper/2605.28190/) (2026)
- [Data-CUBE: Data Curriculum for Instruction-based Sentence Representation Learning](https://mlsanity.com/paper/2401.03563/) (2024)
- [Geometry-Preserving Aggregation for Mixture-of-Experts Embedding Models](https://mlsanity.com/paper/2602.14039/) (2026)
- [R-DEIM Net: An Efficient Rationale-Augmented Dual-Expert Interaction Model for Paraphrase Detection](https://mlsanity.com/paper/2609.30100/) (2026)

## 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.