---
title: "Microsoft — works of 2019: 28 papers, the most cited first"
description: "28 works of Microsoft from 2019 in our library, the most cited first. Among them: UNITER: UNiversal Image-TExt Representation Learning…"
canonical: "https://research.elamur.ai/free/en/companies/microsoft/year/2019/"
---
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# Works of Microsoft from 2019

28 works · the most cited first · [← Microsoft](https://md.research.elamur.ai/free/en/companies/microsoft/)

1. **[UNITER: UNiversal Image-TExt Representation Learning](https://md.research.elamur.ai/free/en/papers/chen-2019-uniter-universal-image-text-representation/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Yen-Chun Chen, Linjie Li, Licheng Yu, et al.*2019 · 2,356 citations*
2. **[Multi-Task Deep Neural Networks for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/liu-2019-multi-task-deep-neural-networks/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaodong Liu, Pengcheng He, Weizhu Chen, et al.*2019 · 1,511 citations*
3. [**Unified Language Model Pre-training for Natural Language Understanding and Generation arXiv:1905.03197**Li Dong, Nan Yang, Wenhui Wang, et al.*2019 · 1,406 citations*](https://md.research.elamur.ai/free/en/papers/dong-2019-unified-language-model-pre-training/)
4. **[COMET: Commonsense Transformers for Automatic Knowledge Graph Construction](https://md.research.elamur.ai/free/en/papers/bosselut-2019-comet-commonsense-transformers-automatic-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Antoine Bosselut, Hannah Rashkin, Maarten Sap, et al.*2019 · 1,287 citations*
5. [**Publicly Available Clinical BERT Embeddings arXiv:1904.03323**Emily Alsentzer, John R. Murphy, Willie Boag, et al.*2019 · 1,226 citations*](https://md.research.elamur.ai/free/en/papers/alsentzer-2019-publicly-available-clinical-bert-embeddings/)
6. **[PIQA: Reasoning about Physical Commonsense in Natural Language](https://md.research.elamur.ai/free/en/papers/bisk-2019-piqa-reasoning-about-physical-commonsense/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yonatan Bisk, Rowan Zellers, Ronan Le Bras, et al.*2019 · 1,220 citations*
7. [**VL-BERT: Pre-training of Generic Visual-Linguistic Representations arXiv:1908.08530**Weijie Su, Xizhou Zhu, Yue Cao, et al.*2019 · 1,025 citations*](https://md.research.elamur.ai/free/en/papers/su-2019-vl-bert-pre-training-generic/)
8. [**Patient Knowledge Distillation for BERT Model Compression arXiv:1908.09355**Siqi Sun, Yu Cheng, Zhe Gan, et al.*2019 · 943 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-patient-knowledge-distillation-bert-model/)
9. **[Findings of the 2019 Conference on Machine Translation (WMT19)](https://md.research.elamur.ai/free/en/papers/barrault-2019-findings-2019-conference-machine-translation/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Loïc Barrault, Ondřej Bojar, Marta R. Costa-jussà, et al.*2019 · 841 citations*
10. [**DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation arXiv:1911.00536**Yizhe Zhang, Siqi Sun, Michel Galley, et al.*2019 · 690 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-dialogpt-large-scale-generative-pre/)
11. [**The Second Conversational Intelligence Challenge (ConvAI2) arXiv:1902.00098**Emily Dinan, Varvara Logacheva, Valentin Malykh, et al.*2019 · 527 citations*](https://md.research.elamur.ai/free/en/papers/dinan-2019-second-conversational-intelligence-challenge-convai2/)
12. [**FreeLB: Enhanced Adversarial Training for Natural Language Understanding arXiv:1909.11764**Chen Zhu, Yu Cheng, Zhe Gan, et al.*2019 · 446 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2019-freelb-enhanced-adversarial-training-natural/)
13. **[Discourse-Aware Neural Extractive Text Summarization](https://md.research.elamur.ai/free/en/papers/xu-2019-discourse-aware-neural-extractive-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiacheng Xu, Zhe Gan, Yu Cheng, et al.*2019 · 385 citations*
14. **[Counterfactual Data Augmentation for Mitigating Gender Stereotypes in Languages with Rich Morphology](https://md.research.elamur.ai/free/en/papers/zmigrod-2019-counterfactual-data-augmentation-mitigating-gender/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ran Zmigrod, Sabrina J. Mielke, Hanna Wallach, et al.*2019 · 372 citations*
15. **[Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings](https://md.research.elamur.ai/free/en/papers/manzini-2019-black-is-criminal-as-caucasian/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Thomas Manzini, Lim Yao Chong, Alan W Black, et al.*2019 · 332 citations*
16. **[Unicoder: A Universal Language Encoder by Pre-training with Multiple Cross-lingual Tasks](https://md.research.elamur.ai/free/en/papers/huang-2019-unicoder-universal-language-encoder-pre/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Haoyang Huang, Yaobo Liang, Nan Duan, et al.*2019 · 264 citations*
17. **[Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction](https://md.research.elamur.ai/free/en/papers/zheng-2019-doc2edag-end-end-document-level/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shun Zheng, Wei Cao, Wei Xu, et al.*2019 · 246 citations*
18. **[Microsoft Translator at WMT 2019: Towards Large-Scale Document-Level Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/dowmunt-2019-microsoft-translator-wmt-2019-large/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Marcin Junczys-Dowmunt*2019 · 222 citations*
19. **[Visualizing and Understanding the Effectiveness of BERT](https://md.research.elamur.ai/free/en/papers/hao-2019-visualizing-understanding-effectiveness-bert/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yaru Hao, Li Dong, Furu Wei, et al.*2019 · 221 citations*
20. **[Sentence Mover's Similarity: Automatic Evaluation for Multi-Sentence Texts](https://md.research.elamur.ai/free/en/papers/clark-2019-sentence-mover-s-similarity-automatic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Elizabeth Clark, Asli Celikyilmaz, Noah A. Smith*2019 · 208 citations*
21. [**Representation Degeneration Problem in Training Natural Language Generation Models arXiv:1907.12009**Jun Gao, Di He, Xu Tan, et al.*2019 · 180 citations*](https://md.research.elamur.ai/free/en/papers/gao-2019-representation-degeneration-problem-training-natural/)
22. **[Findings of the WMT 2019 Shared Tasks on Quality Estimation](https://md.research.elamur.ai/free/en/papers/fonseca-2019-findings-wmt-2019-shared-tasks/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Erick Fonseca, Lisa Yankovskaya, André F. T. Martins, et al.*2019 · 173 citations*
23. **[Multilingual Neural Machine Translation with Language Clustering](https://md.research.elamur.ai/free/en/papers/tan-2019-multilingual-neural-machine-translation-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xu Tan, Jiale Chen, Di He, et al.*2019 · 172 citations*
24. **[Target-Guided Open-Domain Conversation](https://md.research.elamur.ai/free/en/papers/tang-2019-target-guided-open-domain-conversation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jianheng Tang, Tiancheng Zhao, Chenyan Xiong, et al.*2019 · 166 citations*
25. **[Multi-Source Cross-Lingual Model Transfer: Learning What to Share](https://md.research.elamur.ai/free/en/papers/chen-2019-multi-source-cross-lingual-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xilun Chen, Ahmed Hassan Awadallah, Hany Hassan, et al.*2019 · 160 citations*
26. **[Question Answering as an Automatic Evaluation Metric for News Article Summarization](https://md.research.elamur.ai/free/en/papers/eyal-2019-question-answering-as-automatic-evaluation/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Matan Eyal, Tal Baumel, Michael Elhadad*2019 · 156 citations*
27. **[Exploiting Monolingual Data at Scale for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/wu-2019-exploiting-monolingual-data-scale-neural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Lijun Wu, Yiren Wang, Yingce Xia, et al.*2019 · 102 citations*
28. **[Findings of the WMT 2019 Shared Task on Automatic Post-Editing](https://md.research.elamur.ai/free/en/papers/chatterjee-2019-findings-wmt-2019-shared-task/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Rajen Chatterjee, Christian Federmann, Matteo Negri, et al.*2019 · 102 citations*
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