mrqa

  • Description:

The MRQA 2019 Shared Task focuses on generalization in question answering. An effective question answering system should do more than merely interpolate from the training set to answer test examples drawn from the same distribution: it should also be able to extrapolate to out-of-distribution examples — a significantly harder challenge.

MRQA adapts and unifies multiple distinct question answering datasets (carefully selected subsets of existing datasets) into the same format (SQuAD format). Among them, six datasets were made available for training, and six datasets were made available for testing. Small portions of the training datasets were held-out as in-domain data that may be used for development. The testing datasets only contain out-of-domain data. This benchmark is released as part of the MRQA 2019 Shared Task.

More information can be found at: <a href="https://mrqa.github.io/2019/shared.html">https://mrqa.github.io/2019/shared.html</a>.

FeaturesDict({
    'answers': Sequence(tf.string),
    'context': tf.string,
    'context_tokens': Sequence({
        'offsets': tf.int32,
        'tokens': tf.string,
    }),
    'detected_answers': Sequence({
        'char_spans': Sequence({
            'end': tf.int32,
            'start': tf.int32,
        }),
        'text': tf.string,
        'token_spans': Sequence({
            'end': tf.int32,
            'start': tf.int32,
        }),
    }),
    'qid': tf.string,
    'question': tf.string,
    'question_tokens': Sequence({
        'offsets': tf.int32,
        'tokens': tf.string,
    }),
    'subset': tf.string,
})
  • Feature documentation:
Feature Class Shape Dtype Description
FeaturesDict
answers Sequence(Tensor) (None,) tf.string
context Tensor tf.string
context_tokens Sequence
context_tokens/offsets Tensor tf.int32
context_tokens/tokens Tensor tf.string
detected_answers Sequence
detected_answers/char_spans Sequence
detected_answers/char_spans/end Tensor tf.int32
detected_answers/char_spans/start Tensor tf.int32
detected_answers/text Tensor tf.string
detected_answers/token_spans Sequence
detected_answers/token_spans/end Tensor tf.int32
detected_answers/token_spans/start Tensor tf.int32
qid Tensor tf.string
question Tensor tf.string
question_tokens Sequence
question_tokens/offsets Tensor tf.int32
question_tokens/tokens Tensor tf.string
subset Tensor tf.string

mrqa/squad (default config)

  • Config description: The SQuAD (Stanford Question Answering Dataset) dataset is used as the basis for the shared task format. Crowdworkers are shown paragraphs from Wikipedia and are asked to write questions with extractive answers.

  • Download size: 29.66 MiB

  • Dataset size: 271.43 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 86,588
'validation' 10,507
  • Citation:
@inproceedings{rajpurkar-etal-2016-squad,
    title = "{SQ}u{AD}: 100,000+ Questions for Machine Comprehension of Text",
    author = "Rajpurkar, Pranav  and
      Zhang, Jian  and
      Lopyrev, Konstantin  and
      Liang, Percy",
    booktitle = "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2016",
    address = "Austin, Texas",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D16-1264",
    doi = "10.18653/v1/D16-1264",
    pages = "2383--2392",
}

@inproceedings{fisch-etal-2019-mrqa,
    title = "{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension",
    author = "Fisch, Adam  and
      Talmor, Alon  and
      Jia, Robin  and
      Seo, Minjoon  and
      Choi, Eunsol  and
      Chen, Danqi",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5801",
    doi = "10.18653/v1/D19-5801",
    pages = "1--13",
}

Note that each MRQA dataset has its own citation. Please see the source to see
the correct citation for each contained dataset."

mrqa/news_qa

  • Config description: Two sets of crowdworkers ask and answer questions based on CNN news articles. The “questioners” see only the article’s headline and summary while the “answerers” see the full article. Questions that have no answer or are flagged in the dataset to be without annotator agreement are discarded.

  • Download size: 56.83 MiB

  • Dataset size: 654.25 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 74,160
'validation' 4,212
  • Citation:
@inproceedings{trischler-etal-2017-newsqa,
        title = "{N}ews{QA}: A Machine Comprehension Dataset",
        author = "Trischler, Adam  and
          Wang, Tong  and
          Yuan, Xingdi  and
          Harris, Justin  and
          Sordoni, Alessandro  and
          Bachman, Philip  and
          Suleman, Kaheer",
        booktitle = "Proceedings of the 2nd Workshop on Representation Learning for {NLP}",
        month = aug,
        year = "2017",
        address = "Vancouver, Canada",
        publisher = "Association for Computational Linguistics",
        url = "https://aclanthology.org/W17-2623",
        doi = "10.18653/v1/W17-2623",
        pages = "191--200",
    }
#
@inproceedings{fisch-etal-2019-mrqa,
    title = "{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension",
    author = "Fisch, Adam  and
      Talmor, Alon  and
      Jia, Robin  and
      Seo, Minjoon  and
      Choi, Eunsol  and
      Chen, Danqi",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5801",
    doi = "10.18653/v1/D19-5801",
    pages = "1--13",
}

Note that each MRQA dataset has its own citation. Please see the source to see
the correct citation for each contained dataset."

mrqa/trivia_qa

  • Config description: Question and answer pairs are sourced from trivia and quiz-league websites. The web version of TriviaQA, where the contexts are retrieved from the results of a Bing search query, is used.

  • Download size: 383.14 MiB

  • Dataset size: 772.75 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 61,688
'validation' 7,785
  • Citation:
@inproceedings{joshi-etal-2017-triviaqa,
    title = "{T}rivia{QA}: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension",
    author = "Joshi, Mandar  and
      Choi, Eunsol  and
      Weld, Daniel  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P17-1147",
    doi = "10.18653/v1/P17-1147",
    pages = "1601--1611",
}

@inproceedings{fisch-etal-2019-mrqa,
    title = "{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension",
    author = "Fisch, Adam  and
      Talmor, Alon  and
      Jia, Robin  and
      Seo, Minjoon  and
      Choi, Eunsol  and
      Chen, Danqi",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5801",
    doi = "10.18653/v1/D19-5801",
    pages = "1--13",
}

Note that each MRQA dataset has its own citation. Please see the source to see
the correct citation for each contained dataset."

mrqa/search_qa

  • Config description: Question and answer pairs are sourced from the Jeopardy! TV show. The contexts are composed of retrieved snippets from a Google search query.

  • Download size: 699.86 MiB

  • Dataset size: 1.38 GiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 117,384
'validation' 16,980
  • Citation:
@article{dunn2017searchqa,
    title={Searchqa: A new q\&a dataset augmented with context from a search engine},
    author={Dunn, Matthew and Sagun, Levent and Higgins, Mike and Guney, V Ugur and Cirik, Volkan and Cho, Kyunghyun},
    journal={arXiv preprint arXiv:1704.05179},
    year={2017}
}

@inproceedings{fisch-etal-2019-mrqa,
    title = "{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension",
    author = "Fisch, Adam  and
      Talmor, Alon  and
      Jia, Robin  and
      Seo, Minjoon  and
      Choi, Eunsol  and
      Chen, Danqi",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5801",
    doi = "10.18653/v1/D19-5801",
    pages = "1--13",
}

Note that each MRQA dataset has its own citation. Please see the source to see
the correct citation for each contained dataset."

mrqa/hotpot_qa

  • Config description: Crowdworkers are shown two entity-linked paragraphs from Wikipedia and are asked to write and answer questions that require multi-hop reasoning to solve. In the original setting, these paragraphs are mixed with additional distractor paragraphs to make inference harder. Here, the distractor paragraphs are not included.

  • Download size: 111.98 MiB

  • Dataset size: 272.87 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 72,928
'validation' 5,901
  • Citation:
@inproceedings{yang-etal-2018-hotpotqa,
    title = "{H}otpot{QA}: A Dataset for Diverse, Explainable Multi-hop Question Answering",
    author = "Yang, Zhilin  and
      Qi, Peng  and
      Zhang, Saizheng  and
      Bengio, Yoshua  and
      Cohen, William  and
      Salakhutdinov, Ruslan  and
      Manning, Christopher D.",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D18-1259",
    doi = "10.18653/v1/D18-1259",
    pages = "2369--2380",
}

@inproceedings{fisch-etal-2019-mrqa,
    title = "{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension",
    author = "Fisch, Adam  and
      Talmor, Alon  and
      Jia, Robin  and
      Seo, Minjoon  and
      Choi, Eunsol  and
      Chen, Danqi",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5801",
    doi = "10.18653/v1/D19-5801",
    pages = "1--13",
}

Note that each MRQA dataset has its own citation. Please see the source to see
the correct citation for each contained dataset."

mrqa/natural_questions

  • Config description: Questions are collected from information-seeking queries to the Google search engine by real users under natural conditions. Answers to the questions are annotated in a retrieved Wikipedia page by crowdworkers. Two types of annotations are collected: 1) the HTML bounding box containing enough information to completely infer the answer to the question (Long Answer), and 2) the subspan or sub-spans within the bounding box that comprise the actual answer (Short Answer). Only the examples that have short answers are used, and the long answer is used as the context.

  • Download size: 121.15 MiB

  • Dataset size: 339.03 MiB

  • Auto-cached (documentation): No

  • Splits:

Split Examples
'train' 104,071
'validation' 12,836
  • Citation:
@article{kwiatkowski-etal-2019-natural,
    title = "Natural Questions: A Benchmark for Question Answering Research",
    author = "Kwiatkowski, Tom  and
      Palomaki, Jennimaria  and
      Redfield, Olivia  and
      Collins, Michael  and
      Parikh, Ankur  and
      Alberti, Chris  and
      Epstein, Danielle  and
      Polosukhin, Illia  and
      Devlin, Jacob  and
      Lee, Kenton  and
      Toutanova, Kristina  and
      Jones, Llion  and
      Kelcey, Matthew  and
      Chang, Ming-Wei  and
      Dai, Andrew M.  and
      Uszkoreit, Jakob  and
      Le, Quoc  and
      Petrov, Slav",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    year = "2019",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://aclanthology.org/Q19-1026",
    doi = "10.1162/tacl_a_00276",
    pages = "452--466",
}

@inproceedings{fisch-etal-2019-mrqa,
    title = "{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension",
    author = "Fisch, Adam  and
      Talmor, Alon  and
      Jia, Robin  and
      Seo, Minjoon  and
      Choi, Eunsol  and
      Chen, Danqi",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5801",
    doi = "10.18653/v1/D19-5801",
    pages = "1--13",
}

Note that each MRQA dataset has its own citation. Please see the source to see
the correct citation for each contained dataset."

mrqa/bio_asq

  • Config description: BioASQ, a challenge on large-scale biomedical semantic indexing and question answering, contains question and answer pairs that are created by domain experts. They are then manually linked to multiple related science (PubMed) articles. The full abstract of each of the linked articles is downloaded and used as individual contexts (e.g., a single question can be linked to multiple, independent articles to create multiple QA-context pairs). Abstracts that do not exactly contain the answer are discarded.

  • Download size: 2.54 MiB

  • Dataset size: 6.70 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'test' 1,504
  • Citation:
@article{tsatsaronis2015overview,
    title={An overview of the BIOASQ large-scale biomedical semantic indexing and question answering competition},
    author={Tsatsaronis, George and Balikas, Georgios and Malakasiotis, Prodromos and Partalas, Ioannis and Zschunke, Matthias and Alvers, Michael R and Weissenborn, Dirk and Krithara, Anastasia and Petridis, Sergios and Polychronopoulos, Dimitris and others},
    journal={BMC bioinformatics},
    volume={16},
    number={1},
    pages={1--28},
    year={2015},
    publisher={Springer}
}

@inproceedings{fisch-etal-2019-mrqa,
    title = "{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension",
    author = "Fisch, Adam  and
      Talmor, Alon  and
      Jia, Robin  and
      Seo, Minjoon  and
      Choi, Eunsol  and
      Chen, Danqi",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5801",
    doi = "10.18653/v1/D19-5801",
    pages = "1--13",
}

Note that each MRQA dataset has its own citation. Please see the source to see
the correct citation for each contained dataset."

mrqa/drop

  • Config description: DROP (Discrete Reasoning Over the content of Paragraphs) examples were collected similarly to SQuAD, where crowdworkers are asked to create question-answer pairs from Wikipedia paragraphs. The questions focus on quantitative reasoning, and the original dataset contains non-extractive numeric answers as well as extractive text answers. The set of questions that are extractive is used.

  • Download size: 578.25 KiB

  • Dataset size: 5.41 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'test' 1,503
  • Citation:
@inproceedings{dua-etal-2019-drop,
    title = "{DROP}: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs",
    author = "Dua, Dheeru  and
      Wang, Yizhong  and
      Dasigi, Pradeep  and
      Stanovsky, Gabriel  and
      Singh, Sameer  and
      Gardner, Matt",
    booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
    month = jun,
    year = "2019",
    address = "Minneapolis, Minnesota",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/N19-1246",
    doi = "10.18653/v1/N19-1246",
    pages = "2368--2378",
}

@inproceedings{fisch-etal-2019-mrqa,
    title = "{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension",
    author = "Fisch, Adam  and
      Talmor, Alon  and
      Jia, Robin  and
      Seo, Minjoon  and
      Choi, Eunsol  and
      Chen, Danqi",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5801",
    doi = "10.18653/v1/D19-5801",
    pages = "1--13",
}

Note that each MRQA dataset has its own citation. Please see the source to see
the correct citation for each contained dataset."

mrqa/duo_rc

  • Config description: The ParaphraseRC split of the DuoRC dataset is used. In this setting, two different plot summaries of the same movie are collected—one from Wikipedia and the other from IMDb. Two different sets of crowdworkers ask and answer questions about the movie plot, where the “questioners” are shown only the Wikipedia page, and the “answerers” are shown only the IMDb page. Questions that are marked as unanswerable are discarded.

  • Download size: 1.14 MiB

  • Dataset size: 15.04 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'test' 1,501
  • Citation:
@inproceedings{saha-etal-2018-duorc,
    title = "{D}uo{RC}: Towards Complex Language Understanding with Paraphrased Reading Comprehension",
    author = "Saha, Amrita  and
      Aralikatte, Rahul  and
      Khapra, Mitesh M.  and
      Sankaranarayanan, Karthik",
    booktitle = "Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/P18-1156",
    doi = "10.18653/v1/P18-1156",
    pages = "1683--1693",
}

@inproceedings{fisch-etal-2019-mrqa,
    title = "{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension",
    author = "Fisch, Adam  and
      Talmor, Alon  and
      Jia, Robin  and
      Seo, Minjoon  and
      Choi, Eunsol  and
      Chen, Danqi",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5801",
    doi = "10.18653/v1/D19-5801",
    pages = "1--13",
}

Note that each MRQA dataset has its own citation. Please see the source to see
the correct citation for each contained dataset."

mrqa/race

  • Config description: ReAding Comprehension Dataset From Examinations (RACE) is collected from English reading comprehension exams for middle and high school Chinese students. The high school split (which is more challenging) is used and also the implicit “fill in the blank” style questions (which are unnatural for this task) are filtered out.

  • Download size: 1.49 MiB

  • Dataset size: 3.53 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'test' 674
  • Citation:
@inproceedings{lai-etal-2017-race,
    title = "{RACE}: Large-scale {R}e{A}ding Comprehension Dataset From Examinations",
    author = "Lai, Guokun  and
      Xie, Qizhe  and
      Liu, Hanxiao  and
      Yang, Yiming  and
      Hovy, Eduard",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D17-1082",
    doi = "10.18653/v1/D17-1082",
    pages = "785--794",
}

@inproceedings{fisch-etal-2019-mrqa,
    title = "{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension",
    author = "Fisch, Adam  and
      Talmor, Alon  and
      Jia, Robin  and
      Seo, Minjoon  and
      Choi, Eunsol  and
      Chen, Danqi",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5801",
    doi = "10.18653/v1/D19-5801",
    pages = "1--13",
}

Note that each MRQA dataset has its own citation. Please see the source to see
the correct citation for each contained dataset."

mrqa/relation_extraction

  • Config description: Given a slot-filling dataset, relations among entities are systematically transformed into questionanswer pairs using templates. For example, the educated_at(x, y) relationship between two entities x and y appearing in a sentence can be expressed as “Where was x educated at?” with answer y. Multiple templates for each type of relation are collected. The dataset’s zeroshot benchmark split (generalization to unseen relations) is used, and only the positive examples are kept.

  • Download size: 830.88 KiB

  • Dataset size: 3.71 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'test' 2,948
  • Citation:
@inproceedings{levy-etal-2017-zero,
    title = "Zero-Shot Relation Extraction via Reading Comprehension",
    author = "Levy, Omer  and
      Seo, Minjoon  and
      Choi, Eunsol  and
      Zettlemoyer, Luke",
    booktitle = "Proceedings of the 21st Conference on Computational Natural Language Learning ({C}o{NLL} 2017)",
    month = aug,
    year = "2017",
    address = "Vancouver, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/K17-1034",
    doi = "10.18653/v1/K17-1034",
    pages = "333--342",
}

@inproceedings{fisch-etal-2019-mrqa,
    title = "{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension",
    author = "Fisch, Adam  and
      Talmor, Alon  and
      Jia, Robin  and
      Seo, Minjoon  and
      Choi, Eunsol  and
      Chen, Danqi",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5801",
    doi = "10.18653/v1/D19-5801",
    pages = "1--13",
}

Note that each MRQA dataset has its own citation. Please see the source to see
the correct citation for each contained dataset."

mrqa/textbook_qa

  • Config description: TextbookQA is collected from lessons from middle school Life Science, Earth Science, and Physical Science textbooks. Questions that are accompanied with a diagram, or that are “True or False” questions are not included.

  • Download size: 1.79 MiB

  • Dataset size: 14.04 MiB

  • Auto-cached (documentation): Yes

  • Splits:

Split Examples
'test' 1,503
  • Citation:
@inproceedings{kembhavi2017you,
    title={Are you smarter than a sixth grader? textbook question answering for multimodal machine comprehension},
    author={Kembhavi, Aniruddha and Seo, Minjoon and Schwenk, Dustin and Choi, Jonghyun and Farhadi, Ali and Hajishirzi, Hannaneh},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern recognition},
    pages={4999--5007},
    year={2017}
}

@inproceedings{fisch-etal-2019-mrqa,
    title = "{MRQA} 2019 Shared Task: Evaluating Generalization in Reading Comprehension",
    author = "Fisch, Adam  and
      Talmor, Alon  and
      Jia, Robin  and
      Seo, Minjoon  and
      Choi, Eunsol  and
      Chen, Danqi",
    booktitle = "Proceedings of the 2nd Workshop on Machine Reading for Question Answering",
    month = nov,
    year = "2019",
    address = "Hong Kong, China",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/D19-5801",
    doi = "10.18653/v1/D19-5801",
    pages = "1--13",
}

Note that each MRQA dataset has its own citation. Please see the source to see
the correct citation for each contained dataset."