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https://github.com/shizhediao/awesome-domain-adaptation-NLP

domain adaptation in NLP
https://github.com/shizhediao/awesome-domain-adaptation-NLP

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domain adaptation in NLP

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# awesome-domain-adaptation-NLP

domain adaptation in NLP



[![MIT License](https://img.shields.io/badge/license-MIT-green.svg)](https://opensource.org/licenses/MIT) 



This repo is a collection of AWESOME things about domain adaptation in NLP, including papers, code, etc. Feel free to star and fork.

Please feel free to pull requests or report issues.



# Contents

- [awsome-domain-adaptation](#awsome-domain-adaptation)

- [Contents](#contents)

- [Papers](#papers)

  - [Survey](#survey)

  - [Theory](#theory)

- [Code Repos](#code-repos)

- [Lectures and Tutorials](#lectures-and-tutorials)

- [Other Resources](#other-resources)



# Papers



## ACL 2021

* Measuring Fine-Grained Domain Relevance of Terms: A Hierarchical Core-Fringe Approach [ACL 2021 Long] [[__pdf__](https://arxiv.org/pdf/2105.13255.pdf)][[__code__](https://github.com/jeffhj/domain-relevance)]

* Domain-Adaptive Pretraining Methods for Dialogue Understanding [ACL 2021 Short] [[__pdf__](https://aclanthology.org/2021.acl-short.84.pdf)]

* Enhancing the Generalization for Intent Classification and Out-of-Domain Detection in SLU [ACL 2021 Long] [[__pdf__](https://arxiv.org/pdf/2106.14464.pdf)]

* Adapt-and-Distill: Developing Small, Fast and Effective Pretrained Language Models for Domains [ACL 2021 Findings Long] [[__pdf__](https://arxiv.org/pdf/2106.13474.pdf)][[__code__](https://github.com/microsoft/unilm/tree/master/adalm)]

* Preview, Attend and Review Schema-Aware Curriculum Learning for Multi-Domain Dialog State Tracking [ACL 2021 Short] [[__pdf__](https://arxiv.org/pdf/2106.00291.pdf)]

* Crowdsourcing Learning as Domain Adaptation A Case Study on Named Entity Recognition [ACL 2021 Long] [[__pdf__](https://arxiv.org/pdf/2105.14980v1.pdf)][[__code__](https://github.com/izhx/CLasDA)]

* Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking [ACL 2021 Short] [[__pdf__](https://arxiv.org/pdf/2105.14398.pdf)][[__code__](https://github.com/cambridgeltl/sapbert)]

* Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning [ACL 2021 Short] [[__pdf__](https://arxiv.org/pdf/2105.14289.pdf)][[__code__](https://github.com/parZival27/supervised-contrastive-learning-for-out-of-domain-detection)]

* Unsupervised Out-of-Domain Detection via Pre-trained Transformers [ACL 2021 Long] [[__pdf__](https://arxiv.org/pdf/2106.00948.pdf)][[__code__](https://github.com/rivercold/BERT-unsupervised-OOD)]

* Adapt-and-Distill Developing Small, Fast and Effective Pretrained Language Models for Domains [ACL 2021 Findings Long] [[__pdf__](https://aclanthology.org/2021.findings-acl.40.pdf)][[__code__](https://github.com/microsoft/unilm/tree/master/adalm)]



## NAACL 2021

* Meta-Learning for Domain Generalization in Semantic Parsing [NAACL 2021] [[__pdf__](https://arxiv.org/pdf/2010.11988.pdf)][[__code__](https://github.com/berlino/tensor2struct-public)]

* UmlsBERT: Clinical Domain Knowledge Augmentation of Contextual Embeddings Using the Unified Medical Language System Metathesaurus [NAACL 2021] [[__pdf__](https://arxiv.org/pdf/2010.10391.pdf)][[__code__](https://github.com/gmichalo/UmlsBERT)]

* DART: Open-Domain Structured Data Record to Text Generation [NAACL 2021] [[__pdf__](https://arxiv.org/pdf/2007.02871.pdf)][[__code__](https://github.com/Yale-LILY/dart)]

* OodGAN: Generative Adversarial Network for Out-of-Domain Data [NAACL 2021 Industry track] [[__pdf__](https://arxiv.org/pdf/2104.02484.pdf)]

* Leaving No Valuable Knowledge Behind: Weak Supervision with Self-training and Domain-specific Rules [NAACL 2021] [[__pdf__](https://www.microsoft.com/en-us/research/uploads/prod/2021/03/SelftrainWS-NAACL2021.pdf)][[__code__](https://github.com/microsoft/ASTRA)]

* QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization [NAACL 2021] [[__pdf__](https://arxiv.org/pdf/2104.05938.pdf)][[__code__](https://github.com/Yale-LILY/QMSum)]

* UDALM: Unsupervised Domain Adaptation through Language Modeling [NAACL 2021] [[__pdf__](https://arxiv.org/pdf/2104.07078.pdf)][[__code__](https://github.com/ckarouzos/slp_daptmlm)]



## EMNLP 2020

* Wasserstein Distance Regularized Sequence Representation for Text Matching in Asymmetrical Domains [EMNLP 2020] [[__pdf__](https://arxiv.org/pdf/2010.07717.pdf)][[__code__](https://github.com/RUC-WSM/WD-Match)]

* Transformer Based Multi-Source Domain Adaptation [EMNLP 2020] [[__pdf__](https://arxiv.org/pdf/2009.07806.pdf)][[__code__](https://github.com/copenlu/xformer-multi-source-domain-adaptation)]

* Multi-Stage Pre-training for Low-Resource Domain Adaptation [EMNLP 2020] [[__pdf__](https://arxiv.org/pdf/2010.05904.pdf)]

* Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining [EMNLP 2020] [[__pdf__](https://arxiv.org/pdf/2003.13003.pdf)]

* Grammatical Error Correction in Low Error Density Domains: A New Benchmark and Analyses [EMNLP 2020] [[__pdf__](https://arxiv.org/pdf/2010.07574.pdf)][[__code__](https://github.com/SimonHFL/CWEB)]

* Feature Adaptation of Pre-Trained Language Models across Languages and Domains with Robust Self-Training [EMNLP 2020] [[__pdf__](https://arxiv.org/pdf/2009.11538.pdf)]

* End-to-End Synthetic Data Generation for Domain Adaptation of Question Answering Systems [EMNLP 2020] [[__pdf__](https://arxiv.org/pdf/2010.06028.pdf)]

* Effective Unsupervised Domain Adaptation with Adversarially Trained Language Models [EMNLP 2020] [[__pdf__](https://arxiv.org/pdf/2010.01739.pdf)][[__code__](https://github.com/trangvu/mlm4uda)]

* An Empirical Investigation Towards Efficient Multi-Domain Language Model Pre-training [EMNLP 2020] [[__pdf__](https://arxiv.org/pdf/2010.00784.pdf)][[__code__](https://github.com/aws-health-ai/multi_domain_lm)]

* MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models [EMNLP 2020 main conference] [[__pdf__](https://arxiv.org/pdf/2010.00840.pdf)]



## Survey

* Neural Unsupervised Domain Adaptation in NLP—A Survey [arXiv 2020 May] [[__pdf__](https://arxiv.org/pdf/2006.00632.pdf)]  [[__code__](https://github.com/bplank/awesome-neural-adaptation-in-NLP)]  

* 迁移学习简明手册 Jindong Wang et al. Transfer Learning Tutorial. [[__pdf__](https://tutorial.transferlearning.xyz/)]  



## Theory



## Negative Transfer 

* Characterizing and Avoiding Negative Transfer [CVPR 2019] [[__pdf__](https://arxiv.org/pdf/1811.09751.pdf)]  



## Data Selection

* Reinforced Training Data Selection for Domain Adaptation [ACL 2019] [[__pdf__](https://www.aclweb.org/anthology/P19-1189.pdf)] [[__code__](https://github.com/timerstime/SDG4DA)] 

* Entropy-based Training Data Selection for Domain Adaptation [COLING 2012] [[__pdf__](https://www.aclweb.org/anthology/C12-2116.pdf)]



## Pretraining-based

* Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing [arXiv 2020 Aug] [[__pdf__](https://arxiv.org/pdf/2007.15779.pdf)]  [[__code__](https://microsoft.github.io/BLURB/)] 

* Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks [ACL 2020] [[__pdf__](https://arxiv.org/pdf/2004.10964.pdf)]  [[__code__](https://github.com/allenai/dont-stop-pretraining)] 

* Using Similarity Measures to Select Pretraining Data for NER [NAACL 2019] [[__pdf__](https://arxiv.org/pdf/1904.00585.pdf)]  [[__code__](https://github.com/daixiangau/naacl2019-select-pretraining-data-for-ner)] 

* Unsupervised Domain Clusters in Pretrained Language Models [ACL 2020] [[__pdf__](https://arxiv.org/pdf/2004.02105.pdf)]  [[__code__](https://github.com/roeeaharoni/unsupervised-domain-clusters)] 

* Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling [EMNLP 2019] [[__pdf__](https://arxiv.org/pdf/1904.02817.pdf)]  [[__code__](https://github.com/xhan77/AdaptaBERT)]  



## Alignment-based

* Curriculum Learning for Domain Adaptation in Neural Machine Translation [NAACL 2019] [[__pdf__](https://arxiv.org/pdf/1905.05816.pdf)]  [[__code__](https://github.com/kevinduh/sockeye-recipes/tree/master/egs/curriculum)] 

* To Annotate or Not? Predicting Performance Drop under Domain Shift  [EMNLP 2019] [[__pdf__](https://www.aclweb.org/anthology/D19-1222.pdf)]  [[__code__](https://github.com/hadyelsahar/domain-shift-prediction)] 

* Active Adversarial Domain Adaptation [WACV 2020] [[__pdf__](https://arxiv.org/pdf/1904.07848.pdf)]

* BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning [NeurIPS 2019] [[__pdf__](https://arxiv.org/pdf/1906.08158.pdf)]  [[__code__](https://github.com/BlackHC/BatchBALD)] 

* Multi-Source Domain Adaptation for Text Classification via DistanceNet-Bandits [AAAI 2020] [[__pdf__](https://arxiv.org/pdf/2001.04362.pdf)] 

* Bayesian Uncertainty Matching for Unsupervised Domain Adaptation [IJCAI 2019] [[__pdf__](https://arxiv.org/pdf/1906.09693.pdf)]

* Unsupervised Domain Adaptation via Calibrating Uncertainties [CVPR Workshop 19] [[__pdf__](https://arxiv.org/pdf/1907.11202.pdf)]



# Code Repos

Unsupervised domain adaptation method for relation extraction [[__code__](https://github.com/AnthonyMRios/adversarial-relation-classification)]  

Unsupervised domain adaptation with BERT for Amazon food product reviews sentiment analysis. [[__code__](https://github.com/EmreTaha/Unsupervised-Domain-Adaptation-with-BERT)]

  



# Lectures and Tutorials



# Other Resources