Dataset Preview
Go to dataset viewer
candidate_id (string)response_start (int32)response_end (int32)
"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.