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candidate_id (string)response_start (int)response_end (int)
SearchQA_000077f3912049dfb4511db271697bad/_0_0
0
242
SearchQA_000077f3912049dfb4511db271697bad/_0_1
243
306
SearchQA_000077f3912049dfb4511db271697bad/_0_2
307
377
SearchQA_000077f3912049dfb4511db271697bad/_1_0
378
793
SearchQA_000077f3912049dfb4511db271697bad/_2_0
794
1,047
SearchQA_000077f3912049dfb4511db271697bad/_2_1
1,048
1,210
SearchQA_000077f3912049dfb4511db271697bad/_3_0
1,211
1,228
SearchQA_000077f3912049dfb4511db271697bad/_3_1
1,229
1,617
SearchQA_000077f3912049dfb4511db271697bad/_4_0
1,618
2,026
SearchQA_000077f3912049dfb4511db271697bad/_5_0
2,027
2,165
SearchQA_000077f3912049dfb4511db271697bad/_5_1
2,166
2,278
SearchQA_000077f3912049dfb4511db271697bad/_5_2
2,279
2,336
SearchQA_000077f3912049dfb4511db271697bad/_5_3
2,337
2,356
SearchQA_000077f3912049dfb4511db271697bad/_5_4
2,357
2,428
SearchQA_000077f3912049dfb4511db271697bad/_6_0
2,429
2,580
SearchQA_000077f3912049dfb4511db271697bad/_6_1
2,581
2,840
SearchQA_000077f3912049dfb4511db271697bad/_7_0
2,841
3,217
SearchQA_000077f3912049dfb4511db271697bad/_8_0
3,218
3,429
SearchQA_000077f3912049dfb4511db271697bad/_8_1
3,430
3,550
SearchQA_000077f3912049dfb4511db271697bad/_8_2
3,551
3,630
SearchQA_000077f3912049dfb4511db271697bad/_9_0
3,631
3,792
SearchQA_000077f3912049dfb4511db271697bad/_9_1
3,793
3,796
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_0_0
0
147
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_0_1
148
202
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_0_2
204
293
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_10_0
2,404
2,494
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_10_1
2,495
2,535
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_10_2
2,536
2,583
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_10_3
2,584
2,620
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_11_0
2,621
2,753
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_11_1
2,754
2,769
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_11_2
2,770
2,848
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_12_0
2,849
3,083
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_13_0
3,084
3,179
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_14_0
3,180
3,405
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_15_0
3,406
3,447
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_15_1
3,448
3,620
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_16_0
3,621
3,668
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_16_1
3,669
3,781
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_16_2
3,782
3,855
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_17_0
3,856
3,890
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_1_0
294
441
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_1_1
442
456
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_1_2
457
497
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_1_3
498
592
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_2_0
593
691
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_2_1
692
811
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_3_0
812
977
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_3_1
978
1,049
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_4_0
1,050
1,291
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_5_0
1,292
1,527
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_6_0
1,528
1,761
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_7_0
1,762
1,997
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_8_0
1,998
2,045
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_8_1
2,046
2,194
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_8_2
2,195
2,225
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_9_0
2,226
2,393
SearchQA_00008daa3a1544a38a5656d8ca37ae86/_9_1
2,394
2,403
SearchQA_0001101df4dc4099be5e46bab79aae78/_0_0
0
119
SearchQA_0001101df4dc4099be5e46bab79aae78/_0_1
120
184
SearchQA_0001101df4dc4099be5e46bab79aae78/_10_0
1,997
2,223
SearchQA_0001101df4dc4099be5e46bab79aae78/_11_0
2,224
2,455
SearchQA_0001101df4dc4099be5e46bab79aae78/_11_1
2,456
2,460
SearchQA_0001101df4dc4099be5e46bab79aae78/_12_0
2,461
2,631
SearchQA_0001101df4dc4099be5e46bab79aae78/_12_1
2,632
2,695
SearchQA_0001101df4dc4099be5e46bab79aae78/_13_0
2,696
2,934
SearchQA_0001101df4dc4099be5e46bab79aae78/_13_1
2,935
2,947
SearchQA_0001101df4dc4099be5e46bab79aae78/_14_0
2,948
3,056
SearchQA_0001101df4dc4099be5e46bab79aae78/_14_1
3,057
3,103
SearchQA_0001101df4dc4099be5e46bab79aae78/_14_2
3,104
3,156
SearchQA_0001101df4dc4099be5e46bab79aae78/_15_0
3,157
3,304
SearchQA_0001101df4dc4099be5e46bab79aae78/_15_1
3,305
3,402
SearchQA_0001101df4dc4099be5e46bab79aae78/_16_0
3,403
3,578
SearchQA_0001101df4dc4099be5e46bab79aae78/_16_1
3,580
3,638
SearchQA_0001101df4dc4099be5e46bab79aae78/_17_0
3,639
3,673
SearchQA_0001101df4dc4099be5e46bab79aae78/_17_1
3,674
3,802
SearchQA_0001101df4dc4099be5e46bab79aae78/_17_2
3,804
3,870
SearchQA_0001101df4dc4099be5e46bab79aae78/_18_0
3,871
3,977
SearchQA_0001101df4dc4099be5e46bab79aae78/_1_0
185
291
SearchQA_0001101df4dc4099be5e46bab79aae78/_1_1
293
350
SearchQA_0001101df4dc4099be5e46bab79aae78/_2_0
351
483
SearchQA_0001101df4dc4099be5e46bab79aae78/_2_1
484
552
SearchQA_0001101df4dc4099be5e46bab79aae78/_3_0
553
709
SearchQA_0001101df4dc4099be5e46bab79aae78/_3_1
711
746
SearchQA_0001101df4dc4099be5e46bab79aae78/_3_2
747
790
SearchQA_0001101df4dc4099be5e46bab79aae78/_4_0
791
1,034
SearchQA_0001101df4dc4099be5e46bab79aae78/_5_0
1,035
1,223
SearchQA_0001101df4dc4099be5e46bab79aae78/_5_1
1,224
1,248
SearchQA_0001101df4dc4099be5e46bab79aae78/_5_2
1,250
1,262
SearchQA_0001101df4dc4099be5e46bab79aae78/_6_0
1,263
1,421
SearchQA_0001101df4dc4099be5e46bab79aae78/_7_0
1,422
1,609
SearchQA_0001101df4dc4099be5e46bab79aae78/_7_1
1,610
1,667
SearchQA_0001101df4dc4099be5e46bab79aae78/_8_0
1,668
1,766
SearchQA_0001101df4dc4099be5e46bab79aae78/_8_1
1,767
1,806
SearchQA_0001101df4dc4099be5e46bab79aae78/_8_2
1,807
1,879
SearchQA_0001101df4dc4099be5e46bab79aae78/_9_0
1,880
1,996
SearchQA_00017abb592942ee84b64f4c149f3213/_0_0
0
208
SearchQA_00017abb592942ee84b64f4c149f3213/_0_1
209
222
SearchQA_00017abb592942ee84b64f4c149f3213/_0_2
223
230
SearchQA_00017abb592942ee84b64f4c149f3213/_10_0
3,646
3,696
End of preview (truncated to 100 rows)

Dataset Card for MultiReQA

Dataset Summary

MultiReQA contains the sentence boundary annotation from eight publicly available QA datasets including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, and TextbookQA. Five of these datasets, including SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, contain both training and test data, and three, in cluding BioASQ, RelationExtraction, TextbookQA, contain only the test data (also includes DuoRC but not specified in the official documentation)

Supported Tasks and Leaderboards

  • Question answering (QA)
  • Retrieval question answering (ReQA)

    Languages

Sentence boundary annotation for SearchQA, TriviaQA, HotpotQA, NaturalQuestions, SQuAD, BioASQ, RelationExtraction, TextbookQA and DuoRC

Dataset Structure

Data Instances

The general format is: { "candidate_id": <candidate_id>, "response_start": <response_start>, "response_end": <response_end> } ...

An example from SearchQA: {'candidate_id': 'SearchQA_000077f3912049dfb4511db271697bad/_0_1', 'response_end': 306, 'response_start': 243}

Data Fields

{ "candidate_id": <STRING>, "response_start": <INT>, "response_end": <INT> } ...

  • candidate_id: The candidate id of the candidate sentence. It consists of the original qid from the MRQA shared task.
  • response_start: The start index of the sentence with respect to its original context.
  • response_end: The end index of the sentence with respect to its original context

Data Splits

Train and Dev splits are available only for the following datasets,

  • SearchQA
  • TriviaQA
  • HotpotQA
  • SQuAD
  • NaturalQuestions

Test splits are available only for the following datasets,

  • BioASQ
  • RelationExtraction
  • TextbookQA

The number of candidate sentences for each dataset in the table below.

MultiReQA
train test
SearchQA 629,160 454,836
TriviaQA 335,659 238,339
HotpotQA 104,973 52,191
SQuAD 87,133 10,642
NaturalQuestions 106,521 22,118
BioASQ - 14,158
RelationExtraction - 3,301
TextbookQA - 3,701

Dataset Creation

Curation Rationale

MultiReQA is a new multi-domain ReQA evaluation suite composed of eight retrieval QA tasks drawn from publicly available QA datasets from the MRQA shared task. The dataset was curated by converting existing QA datasets from MRQA shared task to the format of MultiReQA benchmark.

Source Data

Initial Data Collection and Normalization

The Initial data collection was performed by converting existing QA datasets from MRQA shared task to the format of MultiReQA benchmark.

Who are the source language producers?

[More Information Needed]

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

The annotators/curators of the dataset are mandyguo-xyguo and mwurts4google, the contributors of the official MultiReQA github repository

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

The annotators/curators of the dataset are mandyguo-xyguo and mwurts4google, the contributors of the official MultiReQA github repository

Licensing Information

[More Information Needed]

Citation Information

@misc{m2020multireqa,
    title={MultiReQA: A Cross-Domain Evaluation for Retrieval Question Answering Models},
    author={Mandy Guo and Yinfei Yang and Daniel Cer and Qinlan Shen and Noah Constant},
    year={2020},
    eprint={2005.02507},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

Contributions

Thanks to @Karthik-Bhaskar for adding this dataset.

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