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https://github.com/shizhediao/awesome-domain-adaptation-NLP
domain adaptation in NLP
https://github.com/shizhediao/awesome-domain-adaptation-NLP
List: awesome-domain-adaptation-NLP
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domain adaptation in NLP
- Host: GitHub
- URL: https://github.com/shizhediao/awesome-domain-adaptation-NLP
- Owner: shizhediao
- Created: 2020-08-22T04:55:41.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2021-09-15T07:53:52.000Z (about 3 years ago)
- Last Synced: 2024-10-28T04:01:51.812Z (about 2 months ago)
- Size: 16.6 KB
- Stars: 53
- Watchers: 6
- Forks: 5
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-interesting-topics-in-nlp - NLP
- ultimate-awesome - awesome-domain-adaptation-NLP - Domain adaptation in NLP. (Other Lists / PowerShell Lists)
README
# 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