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1. Time-Aware Language Models as Temporal Knowledge Bases

2. https://github.com/salesforce/ctrl

3. Attention Is All You Need

4. https://github.com/chiphuyen/lazynlp

5. https://github.com/jcpeterson/openwebtext

6. Teaching Machines to Read and Comprehend

7. https://aclanthology.org/2020.wmt-1.1.pdf

8. 2008-sandhaus.pdf

9. Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies

10. Amazon Reviews: Image-based Recommendations on Styles and Substitutes

11. ELI5: Long Form Question Answering

12. https://github.com/mrqa/MRQA-Shared-Task-2019

13. SQuAD: 100,000+ Questions for Machine Comprehension of Text

14. NewsQA: A Machine Comprehension Dataset

15. TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension

16. SearchQA: A New Q&A Dataset Augmented with Context from a Search Engine

17. HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering

18. Natural Questions: A Benchmark for Question Answering Research

19. 2019-keskar-table7-datasetsandcontrolcodesmetadata.png

20. https://web.archive.org/web/20110109074405/http://www.urlesque.com/2011/01/06/whats-a-subreddit-how-reddit-works/

21. https://www.reddit.com/r/keto/

22. https://www.reddit.com/r/politics/

23. Wikipedia Bibliography:

    1. https://en.wikipedia.org/wiki/Wikipedia  :

       https://en.wikipedia.org/wiki/Wikipedia

    2. Project Gutenberg

    3. English Wikipedia

    4. German Wikipedia

    5. https://en.wikipedia.org/wiki/Spanish_Wikipedia  :

       https://en.wikipedia.org/wiki/Spanish_Wikipedia

    6. French Wikipedia

    7. Europarl Corpus

    8. https://en.wikipedia.org/wiki/Entertainment_Tonight  :

       https://en.wikipedia.org/wiki/Entertainment_Tonight

    9. https://en.wikipedia.org/wiki/George_Clooney  :

       https://en.wikipedia.org/wiki/George_Clooney

    10. Reddit

    11. https://en.wikipedia.org/wiki/Conditioning_(probability)  :

        https://en.wikipedia.org/wiki/Conditioning_(probability)