# Predicting the Success of Domain Adaptation in Text Similarity

arXiv: https://arxiv.org/abs/2106.04641 · HTML: https://mlsanity.com/paper/2106.04641/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 26 references that are in the map.
- First posted on arXiv 8 Jun 2021.

## Builds on (most cited first, 15 of 15)

- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Don't Stop Pretraining: Adapt Language Models to Domains and Tasks](https://mlsanity.com/paper/2004.10964/) (2020)
- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [PAWS: Paraphrase Adversaries from Word Scrambling](https://mlsanity.com/paper/1904.01130/) (2019)
- [Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation](https://mlsanity.com/paper/1809.09478/) (2018)
- [Neural Unsupervised Domain Adaptation in NLP---A Survey](https://mlsanity.com/paper/2006.00632/) (2020)
- [Bilateral Multi-Perspective Matching for Natural Language Sentences](https://mlsanity.com/paper/1702.03814/) (2017)
- [An introduction to domain adaptation and transfer learning](https://mlsanity.com/paper/1812.11806/) (2018)
- [Medical image denoising using convolutional denoising autoencoders](https://mlsanity.com/paper/1608.04667/) (2016)
- [Adversarial Domain Adaptation for Duplicate Question Detection](https://mlsanity.com/paper/1809.02255/) (2018)
- [Domain Divergences: a Survey and Empirical Analysis](https://mlsanity.com/paper/2010.12198/) (2020)
- [How Transferable are Neural Networks in NLP Applications?](https://mlsanity.com/paper/1603.06111/) (2016)
- [Using Similarity Measures to Select Pretraining Data for NER](https://mlsanity.com/paper/1904.00585/) (2019)
- [Simplified Neural Unsupervised Domain Adaptation](https://mlsanity.com/paper/1905.09153/) (2019)
- [Neural Paraphrase Identification of Questions with Noisy Pretraining](https://mlsanity.com/paper/1704.04565/) (2017)

## Built on by (most cited first, 1 of 1)

- [Konooz: Multi-domain Multi-dialect Corpus for Named Entity Recognition](https://mlsanity.com/paper/2506.12615/) (2025)

## Abstract (arXiv)

Transfer learning methods, and in particular domain adaptation, help exploit labeled data in one domain to improve the performance of a certain task in another domain. However, it is still not clear what factors affect the success of domain adaptation. This paper models adaptation success and selection of the most suitable source domains among several candidates in text similarity. We use descriptive domain information and cross-domain similarity metrics as predictive features. While mostly positive, the results also point to some domains where adaptation success was difficult to predict.