This repository has been archived by the owner on Nov 3, 2023. It is now read-only.
-
Notifications
You must be signed in to change notification settings - Fork 2.1k
/
task_list.py
1712 lines (1710 loc) · 62.8 KB
/
task_list.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
#!/usr/bin/env python3
# Copyright (c) Facebook, Inc. and its affiliates.
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
"""
This file contains a list of all the tasks, their id and task name, description and the
tags associated with them.
"""
task_list = [
{
"id": "AmazonQA",
"display_name": "AmazonQA",
"task": "amazon_qa",
"tags": ["QA"],
"links": {"website": "http://jmcauley.ucsd.edu/data/amazon/qa/"},
"description": (
"This dataset contains Question and Answer data from Amazon, "
"totaling around 1.4 million answered questions."
),
},
{
"id": "AQuA",
"display_name": "AQuA",
"task": "aqua",
"tags": ["QA"],
"links": {"arXiv": "https://arxiv.org/abs/1705.04146"},
"description": (
"Dataset containing algebraic word problems with rationales for "
"their answers."
),
},
{
"id": "bAbI-1k",
"display_name": "bAbI 1k",
"task": "babi:All1k",
"tags": ["QA"],
"description": (
"20 synthetic tasks that each test a unique aspect of text and "
"reasoning, and hence test different capabilities of learning "
"models."
),
"links": {"arXiv": "http://arxiv.org/abs/1502.05698"},
"notes": (
"You can access just one of the bAbI tasks with e.g. "
"'babi:Task1k:3' for task 3."
),
},
{
"id": "bAbI-10k",
"display_name": "bAbI 10k",
"task": "babi:All10k",
"tags": ["QA"],
"description": (
"20 synthetic tasks that each test a unique aspect of text and "
"reasoning, and hence test different capabilities of learning "
"models."
),
"links": {"arXiv": "http://arxiv.org/abs/1502.05698"},
"notes": (
"You can access just one of the bAbI tasks with e.g. 'babi:Task10k:3' "
"for task 3."
),
},
{
"id": "BlendedSkillTalk",
"display_name": "Blended Skill Talk",
"task": "blended_skill_talk",
"tags": ["ChitChat"],
"description": (
"A dataset of 7k conversations explicitly designed to exhibit multiple "
"conversation modes: displaying personality, having empathy, and "
"demonstrating knowledge."
),
},
{
"id": "BookTest",
"display_name": "BookTest",
"task": "booktest",
"tags": ["Cloze"],
"description": (
"Sentence completion given a few sentences as context from a book. "
"A larger version of CBT."
),
"links": {"arXiv": "https://arxiv.org/abs/1610.00956"},
},
{
"id": "BotAdversarialDialogue",
"display_name": "Bot Adversarial Dialogue ",
"task": "bot_adversarial_dialogue",
"tags": [],
"description": (
"Datasets described in the paper Recipes for Safety in Open-domain Chatbots. "
"Datasets consist of classification tasks in which the goal is to "
"determine if the utterance is offensive or not given a dialogue context. "
),
"links": {"arXiv": "https://arxiv.org/abs/2010.07079"},
},
{
"id": "SafetyMix",
"display_name": "Safety Mix",
"task": "safety_mix",
"tags": [],
"description": (
"Datasets described in the paper: Learning from data in the mixed adversarial non-adversarial case:"
"Finding the helpers and ignoring the trolls. "
"Datasets based on Bot Adversarial Dialogue and consist of a mixture of different troll users."
"Artificial noise is introduced to the dataset given the troll user type."
),
},
{
"id": "CBT",
"display_name": "Children's Book Test (CBT)",
"task": "cbt",
"tags": ["Cloze"],
"description": (
"Sentence completion given a few sentences as context from a "
"children's book."
),
"links": {"arXiv": "https://arxiv.org/abs/1511.02301"},
},
{
"id": "CCPE",
"display_name": "Coached Conversational Preference Elicitation",
"task": "ccpe",
"tags": ["Goal"],
"description": (
"A dataset consisting of 502 dialogs with 12,000 annotated "
"utterances between a user and an assistant discussing movie "
"preferences in natural language. It was collected using a "
"Wizard-of-Oz methodology between two paid crowd-workers, "
"where one worker plays the role of an 'assistant', while "
"the other plays the role of a 'user'."
),
"links": {
"website": "https://ai.google/tools/datasets/coached-conversational-preference-elicitation"
},
},
{
"id": "CMU_DoG",
"display_name": "CMU Document Grounded Conversations",
"task": "cmu_dog",
"tags": ["ChitChat", "Grounded"],
"description": (
"A document grounded dataset for text conversations, where the "
"documents are Wikipedia articles about popular movies. Consists "
"of 4112 conversations with an average of 21.43 turns per conversation."
),
"links": {
"arXiv": "https://arxiv.org/abs/1809.07358",
"github": "https://github.com/festvox/datasets-CMU_DoG",
},
},
{
"id": "COPA",
"display_name": "Choice of Plausible Alternatives",
"task": "copa",
"tags": ["Reasoning"],
"description": (
"The Choice Of Plausible Alternatives (COPA) evaluation provides "
"researchers with a tool for assessing progress in open-domain "
"commonsense causal reasoning. COPA consists of 1000 questions, "
"split equally into development and test sets of 500 questions each."
),
"links": {"website": "http://people.ict.usc.edu/~gordon/copa.html"},
},
{
"id": "COQA",
"display_name": "Conversational Question Answering Challenge",
"task": "coqa",
"tags": ["QA"],
"description": (
"CoQA is a large-scale dataset for building Conversational "
"Question Answering systems. The goal of the CoQA challenge "
"is to measure the ability of machines to understand a text "
"passage and answer a series of interconnected questions that "
"appear in a conversation. CoQA is pronounced as coca."
),
"links": {"arXiv": "https://arxiv.org/abs/1808.07042"},
},
{
"id": "CornellMovie",
"display_name": "Cornell Movie",
"task": "cornell_movie",
"tags": ["ChitChat", "Dodeca"],
"description": ("Fictional conversations extracted from raw movie scripts."),
"links": {"arXiv": "https://arxiv.org/abs/1106.3077"},
},
{
"id": "DBLL-bAbI",
"display_name": "Dialog Based Language Learning: bAbI Task",
"task": "dbll_babi",
"tags": ["Goal"],
"description": (
"Short dialogs based on the bAbI tasks, but in the form of a "
"question from a teacher, the answer from the student, and finally a "
"comment on the answer from the teacher. The aim is to find learning "
"models that use the comments to improve."
),
"links": {"arXiv": "https://arxiv.org/abs/1604.06045"},
"notes": (
"Tasks can be accessed with a "
"format like: 'parlai display_data -t "
"dbll_babi:task:2_p0.5' which specifies task 2, and policy with 0.5 "
"answers correct, see the paper for more details of the tasks."
),
},
{
"id": "DBLL-Movie",
"display_name": "Dialog Based Language Learning: WikiMovies Task",
"task": "dbll_movie",
"tags": ["Goal"],
"description": (
"Short dialogs based on WikiMovies, but in the form of a question "
"from a teacher, the answer from the student, and finally a comment "
"on the answer from the teacher. The aim is to find learning models "
"that use the comments to improve."
),
"links": {"arXiv": "https://arxiv.org/abs/1604.06045"},
},
{
"id": "dialog-bAbI",
"display_name": "Dialog bAbI",
"task": "dialog_babi",
"tags": ["Goal"],
"description": "Simulated dialogs of restaurant booking",
"links": {"arXiv": "https://arxiv.org/abs/1605.07683"},
},
{
"id": "dialog-bAbI-plus",
"display_name": "Dialog bAbI+",
"task": "dialog_babi_plus",
"tags": ["Goal"],
"description": (
"bAbI+ is an extension of the bAbI Task 1 dialogues with everyday "
"incremental dialogue phenomena (hesitations, restarts, and "
"corrections) which model the disfluencies and communication "
"problems in everyday spoken interaction in real-world environments. "
),
"links": {
"website": (
"https://www.researchgate.net/publication/"
"319128941_Challenging_Neural_Dialogue_Models_with_Natural_"
"Data_Memory_Networks_Fail_on_Incremental_Phenomena"
),
"paper": "http://aclweb.org/anthology/D17-1235",
},
},
{
"id": "dialogue-nli",
"display_name": "Dialogue NLI",
"task": "dialogue_nli",
"tags": ["ChitChat", "NLI"],
"description": (
"Dialogue NLI is a dataset that addresses the issue of consistency in "
"dialogue models."
),
"links": {
"website": "https://wellecks.github.io/dialogue_nli/",
"arXiv": "https://arxiv.org/abs/1811.00671",
},
},
{
"id": "dstc7",
"display_name": "DSTC7 subtrack 1 - ubuntu",
"task": "dstc7",
"tags": ["ChitChat"],
"description": (
"DSTC7 is a competition which provided a dataset of dialogs very "
"similar to the ubuntu dataset. In particular, the subtrack 1 "
"consists in predicting the next utterance."
),
"links": {"arXiv": "https://arxiv.org/abs/1901.03461"},
},
{
"id": "FVQA",
"display_name": "FVQA",
"task": "fvqa",
"tags": ["Visual"],
"description": (
"The FVQA, a VQA dataset which requires, and supports, much deeper "
"reasoning. We extend a conventional visual question answering "
"dataset, which contains image-question-answer triplets, through "
"additional image-question-answer-supporting fact tuples. The "
"supporting fact is represented as a structural triplet, such as "
"<Cat,CapableOf,ClimbingTrees>."
),
"links": {"arXiv": "https://arxiv.org/abs/1606.05433"},
},
{
"id": "DealNoDeal",
"display_name": "Deal or No Deal",
"task": "dealnodeal",
"tags": ["Negotiation"],
"description": (
"End-to-end negotiation task which requires two agents to agree on "
"how to divide a set of items, with each agent assigning different "
"values to each item."
),
"links": {"arXiv": "https://arxiv.org/abs/1706.05125"},
},
{
"id": "Friends",
"display_name": "Friends",
"task": "friends",
"tags": ["MultiPartyConvo"],
"description": (
"Multi-party conversation dataset modified from the 10 seasons "
"of the popular American sitcom that ran in the 90s, Friends."
),
"links": {"website": "https://convokit.cornell.edu/documentation/friends.html"},
},
{
"id": "Glue",
"display_name": "Glue",
"task": "glue",
"tags": [],
"description": (
"GLUE, the General Language Understanding Evaluation benchmark is "
"a collection of resources for training, evaluating, and analyzing "
"natural language understanding systems."
),
"links": {
"website": "https://gluebenchmark.com/",
"website2": "https://huggingface.co/datasets/glue",
},
},
{
"id": "HotpotQA",
"display_name": "HotpotQA",
"task": "hotpotqa",
"tags": ["QA"],
"description": (
"HotpotQA is a dataset for multi-hop question answering. "
"The overall setting is that given some context paragraphs"
"(e.g., a few paragraphs, or the entire Web) and a question,"
"a QA system answers the question by extracting a span of text"
"from the context. It is necessary to perform multi-hop reasoning"
"to correctly answer the question."
),
"links": {"arXiv": "https://arxiv.org/abs/1809.09600"},
},
{
"id": "HuggingFace",
"display_name": "HuggingFace",
"task": "huggingface",
"tags": [],
"description": ("HuggingFace datasets"),
"links": {"website": "https://huggingface.co/"},
},
{
"id": "LIGHT-Dialogue",
"display_name": "LIGHT-Dialogue",
"task": "light_dialog",
"tags": ["Grounded", "Dodeca"],
"description": (
"LIGHT is a text adventure game with actions and dialogue collected. "
"The source data is collected between crowdworkers playing the game."
),
"links": {
"website": "http://parl.ai/projects/light",
"arXiv": "https://arxiv.org/abs/1903.03094",
},
},
{
"id": "LIGHT-Dialogue-Wild",
"display_name": "LIGHT-Dialogue-Wild",
"task": "light_dialog_wild",
"tags": ["Grounded", "LIGHT"],
"description": (
" LIGHT is a text adventure game with actions and dialogue. "
"The WILD dataset here features 41,131+ training episodes of dialogue "
"collected from deploying a game as described in "
),
"links": {
"arXiv": "https://arxiv.org/abs/2008.08076",
"website": "http://parl.ai/projects/light",
},
},
{
"id": "MutualFriends",
"display_name": "MutualFriends",
"task": "mutualfriends",
"tags": ["Goal"],
"description": (
"Task where two agents must discover which friend of theirs is "
"mutual based on the friends's attributes."
),
"links": {"website": "https://stanfordnlp.github.io/cocoa/"},
},
{
"id": "MCTest",
"display_name": "MCTest",
"task": "mctest",
"tags": ["QA"],
"description": ("Questions about short children's stories."),
"links": {
"website": (
"https://www.microsoft.com/en-us/research/publication/"
"mctest-challenge-dataset-open-domain-machine-comprehension-text/"
)
},
},
{
"id": "MovieDD-QA",
"display_name": "Movie Dialog QA",
"task": "moviedialog:Task:1",
"tags": ["QA", "MovieDD"],
"description": (
"Closed-domain QA dataset asking templated questions about movies, "
"answerable from Wikipedia, similar to WikiMovies."
),
"links": {"arXiv": "https://arxiv.org/abs/1511.06931"},
},
{
"id": "MovieDD-QARecs",
"display_name": "Movie Dialog QA Recommendations",
"task": "moviedialog:Task:3",
"tags": ["Goal", "MovieDD"],
"description": (
"Dialogs discussing questions about movies as well as recommendations."
),
"links": {"arXiv": "https://arxiv.org/abs/1511.06931"},
},
{
"id": "MovieDD-Recs",
"display_name": "Movie Dialog Recommendations",
"task": "moviedialog:Task:2",
"tags": ["QA", "MovieDD"],
"description": ("Questions asking for movie recommendations."),
"links": {"arXiv": "https://arxiv.org/abs/1511.06931"},
},
{
"id": "MovieDD-Reddit",
"display_name": "Movie Dialog Reddit",
"task": "moviedialog:Task:4",
"tags": ["ChitChat", "MovieDD"],
"description": (
"Dialogs discussing Movies from Reddit (the Movies SubReddit)."
),
"links": {"arXiv": "https://arxiv.org/abs/1511.06931"},
},
{
"id": "MTurkWikiMovies",
"display_name": "MTurk WikiMovies",
"task": "mturkwikimovies",
"tags": ["QA"],
"description": (
"Closed-domain QA dataset asking MTurk-derived questions about "
"movies, answerable from Wikipedia."
),
"links": {"arXiv": "https://arxiv.org/abs/1611.09823"},
},
{
"id": "MultiNLI",
"display_name": "MultiNLI",
"task": "multinli",
"tags": ["Entailment", "decanlp"],
"description": (
"A dataset designed for use in the development and evaluation of "
"machine learning models for sentence understanding. Each example "
"contains a premise and hypothesis. Model has to predict whether "
"premise and hypothesis entail, contradict or are neutral to each "
"other."
),
"links": {"arXiv": "https://arxiv.org/abs/1704.05426"},
},
{
"id": "NarrativeQA",
"display_name": "NarrativeQA",
"task": "narrative_qa",
"tags": ["QA"],
"description": (
"A dataset and set of tasks in which the reader must answer "
"questions about stories by reading entire books or movie scripts. "
),
"links": {"arXiv": "https://arxiv.org/abs/1712.07040"},
"notes": (
"You can access summaries only task for NarrativeQA by using task "
"'narrative_qa:summaries'. By default, only stories are provided."
),
},
{
"id": "NaturalQuestions",
"display_name": "Natural Questions",
"task": "natural_questions",
"tags": ["QA"],
"description": (
"An open domain question answering dataset. "
"Each example contains real questions that people searched "
"for in Google and the content of the a Wikipedia article that "
"was amongst the top 5 search results for that query, "
"and its annotations. The annotations have the options of a long "
"answer that is seleced from span of major content entities in "
"the Wikipedia article (e.g., paragraphs, tables), a short answer "
"that is selected from one or more short span of words in the "
"article, or 'yes/no'. The existence of any of these answer "
"formats depends on whether the main question can be answered, "
"given the article; if not they are left empty."
),
"links": {
"paper": "https://research.google/pubs/pub47761/",
"website": "https://ai.google.com/research/NaturalQuestions",
},
"notes": (
"Since this task uses ChunkTeacher, it should be used with streaming."
),
},
{
"id": "OpenSubtitles",
"display_name": "Open Subtitles",
"task": "opensubtitles",
"tags": ["ChitChat"],
"description": "Dataset of dialogs from movie scripts.",
"links": {
"version 2018 website": "http://opus.lingfil.uu.se/OpenSubtitles2018.php",
"version 2009 website": "http://opus.lingfil.uu.se/OpenSubtitles.php",
"related work (arXiv)": "https://arxiv.org/abs/1506.05869",
},
},
{
"id": "personalized-dialog-full",
"display_name": "Personalized Dialog Full Set",
"task": "personalized_dialog:AllFull",
"tags": ["Goal", "Personalization"],
"description": (
"Simulated dataset of restaurant booking focused on personalization "
"based on user profiles."
),
"links": {"arXiv": "https://arxiv.org/abs/1706.07503"},
},
{
"id": "personalized-dialog-small",
"display_name": "Personalized Dialog Small Set",
"task": "personalized_dialog:AllSmall",
"tags": ["Goal", "Personalization"],
"description": (
"Simulated dataset of restaurant booking focused on personalization "
"based on user profiles."
),
"links": {"arXiv": "https://arxiv.org/abs/1706.07503"},
},
{
"id": "prosocial_dialog",
"display_name": "Prosocial Dialog",
"task": "prosocial_dialog",
"tags": [],
"description": (
"Prosocial Dialog dataset of 58K dialogues between a speaker showing "
"potentially unsafe behavior and a speaker giving constructive feedback "
"for more socially acceptable behavior."
),
"links": {"arXiv": "https://arxiv.org/abs/2205.12688"},
},
{
"id": "QACNN",
"display_name": "QA CNN",
"task": "qacnn",
"tags": ["Cloze"],
"description": (
"Cloze dataset based on a missing (anonymized) entity phrase from a "
"CNN article"
),
"links": {"arXiv": "https://arxiv.org/abs/1506.03340"},
},
{
"id": "QADailyMail",
"display_name": "QA Daily Mail",
"task": "qadailymail",
"tags": ["Cloze"],
"description": (
"Cloze dataset based on a missing (anonymized) entity phrase from a "
"Daily Mail article."
),
"links": {"arXiv": "https://arxiv.org/abs/1506.03340"},
},
{
"id": "QuAC",
"display_name": "Question Answering in Context",
"task": "quac",
"tags": ["QA"],
"description": (
"Question Answering in Context is a dataset for modeling, "
"understanding, and participating in information seeking dialog. Data "
"instances consist of an interactive dialog between two crowd workers: "
"(1) a student who poses a sequence of freeform questions to learn as "
"much as possible about a hidden Wikipedia text, and (2) a teacher who "
"answers the questions by providing short excerpts (spans) from the text. "
"QuAC introduces challenges not found in existing machine comprehension "
"datasets: its questions are often more open-ended, unanswerable, "
"or only meaningful within the dialog context."
),
"links": {"arXiv": "https://arxiv.org/abs/1808.07036"},
},
{
"id": "SimpleQuestions",
"display_name": "Simple Questions",
"task": "simplequestions",
"tags": ["QA"],
"description": ("Open-domain QA dataset based on Freebase triples."),
"links": {"arXiv": "https://arxiv.org/abs/1506.02075"},
},
{
"id": "ReframeUnhelpfulThoughts",
"display_name": "Reframe Unhelpful Thoughts",
"task": "reframe_thoughts",
"tags": [],
"description": (
"Dataset of about 10k examples of thoughts containing unhelpful "
"thought patterns conditioned on a given persona, accompanied by about "
"27k positive reframes."
),
"links": {},
},
{
"id": "SNLI",
"display_name": "The Stanford Natural Language Inference (SNLI) Corpus",
"task": "snli",
"tags": ["Entailment"],
"description": (
"The SNLI corpus (version 1.0) is a collection of 570k "
"human-written English sentence pairs manually labeled for balanced "
"classification with the labels entailment, contradiction, and "
"neutral, supporting the task of natural language inference (NLI), "
"also known as recognizing textual entailment (RTE)"
),
"links": {"website": "https://nlp.stanford.edu/projects/snli/"},
},
{
"id": "SQuAD2",
"display_name": "SQuAD2",
"task": "squad2",
"tags": ["QA"],
"description": (
"Open-domain QA dataset answerable from a given paragraph from "
"Wikipedia."
),
"links": {"arXiv": "http://arxiv.org/abs/1806.03822"},
},
{
"id": "SQuAD",
"display_name": "SQuAD",
"task": "squad",
"tags": ["QA"],
"description": (
"Open-domain QA dataset answerable from a given paragraph from "
"Wikipedia."
),
"links": {"arXiv": "https://arxiv.org/abs/1606.05250"},
},
{
"id": "SuperGLUE",
"display_name": "SuperGLUE",
"task": "superglue",
"tags": [],
"description": (
"SuperGLUE (https://super.gluebenchmark.com/) is a new benchmark "
"styled after GLUE with a new set of more difficult language "
"understanding tasks, improved resources, and a new public "
"leaderboard."
),
"links": {
"website": "https://super.gluebenchmark.com/",
"website2": "https://huggingface.co/datasets/super_glue",
},
},
{
"id": "TriviaQA",
"display_name": "TriviaQA",
"task": "triviaqa",
"tags": ["QA"],
"description": (
"Open-domain QA dataset with question-answer-evidence triples."
),
"links": {"arXiv": "https://arxiv.org/abs/1705.03551"},
},
{
"id": "TaskNTalk",
"display_name": "Task N' Talk",
"task": "taskntalk",
"tags": ["Goal"],
"description": (
"Dataset of synthetic shapes described by attributes, for agents to "
"play a cooperative QA game."
),
"links": {"arXiv": "https://arxiv.org/abs/1706.08502"},
},
{
"id": "Ubuntu",
"display_name": "Ubuntu",
"task": "ubuntu",
"tags": ["ChitChat", "Dodeca"],
"description": (
"Dialogs between an Ubuntu user and an expert trying to fix issue, "
"we use the V2 version, which cleaned the data to some extent. "
),
"links": {"arXiv": "https://arxiv.org/abs/1506.08909"},
},
{
"id": "WebQuestions",
"display_name": "Web Questions",
"task": "webquestions",
"tags": ["QA"],
"description": ("Open-domain QA dataset from Web queries."),
"links": {"paper": "http://www.aclweb.org/anthology/D13-1160"},
},
{
"id": "WikiMovies",
"display_name": "WikiMovies",
"task": "wikimovies",
"tags": ["QA"],
"description": (
"Closed-domain QA dataset asking templated questions about movies, "
"answerable from Wikipedia."
),
"links": {"arXiv": "https://arxiv.org/abs/1606.03126"},
},
{
"id": "WikiQA",
"display_name": "WikiQA",
"task": "wikiqa",
"tags": ["QA"],
"description": ("Open domain QA from Wikipedia dataset"),
"links": {
"website": (
"https://www.microsoft.com/en-us/research/publication/wikiqa-a-"
"challenge-dataset-for-open-domain-question-answering/"
)
},
},
{
"id": "VQAv1",
"display_name": "VQAv1",
"task": "vqa_v1",
"tags": ["Visual"],
"description": ("Open-ended question answering about visual content."),
"links": {"arXiv": "https://arxiv.org/abs/1505.00468"},
},
{
"id": "VQAv2",
"display_name": "VQAv2",
"task": "vqa_v2",
"tags": ["Visual"],
"description": ("Bigger, more balanced version of the original VQA dataset."),
"links": {"arXiv": "https://arxiv.org/abs/1612.00837"},
},
{
"id": "VisDial",
"display_name": "VisDial",
"task": "visdial",
"tags": ["Visual"],
"description": (
"Task which requires agents to hold a meaningful dialog about "
"visual content."
),
"links": {"arXiv": "https://arxiv.org/abs/1611.08669"},
},
{
"id": "MNIST_QA",
"display_name": "MNIST_QA",
"task": "mnist_qa",
"tags": ["Visual"],
"description": (
"Task which requires agents to identify which number they are "
"seeing. From the MNIST dataset."
),
},
{
"id": "InsuranceQA",
"display_name": "InsuranceQA",
"task": "insuranceqa",
"tags": ["QA"],
"description": (
"Task which requires agents to identify high quality answers "
"composed by professionals with deep domain knowledge."
),
"links": {"arXiv": "https://arxiv.org/abs/1508.01585"},
},
{
"id": "MS_MARCO",
"display_name": "MS_MARCO",
"task": "ms_marco",
"tags": ["QA"],
"description": (
"A large scale Machine Reading Comprehension Dataset with questions "
"sampled from real anonymized user queries and contexts from web "
"documents."
),
"links": {"arXiv": "https://arxiv.org/abs/1611.09268"},
},
{
"id": "CLEVR",
"display_name": "CLEVR",
"task": "clevr",
"tags": ["Visual"],
"description": (
"A visual reasoning dataset that tests abilities such as attribute "
"identification, counting, comparison, spatial relationships, and "
"logical operations."
),
"links": {"arXiv": "https://arxiv.org/abs/1612.06890"},
},
{
"id": "nlvr",
"display_name": "nlvr",
"task": "nlvr",
"tags": ["Visual"],
"description": (
"Cornell Natural Language Visual Reasoning (NLVR) is a language "
"grounding dataset based on pairs of natural language statements "
"grounded in synthetic images."
),
"links": {"website": "http://lic.nlp.cornell.edu/nlvr/"},
},
{
"id": "WMT",
"display_name": "WMT",
"task": "wmt",
"tags": ["MT"],
"description": (
"Workshop on Machine Translation task, currently only includes en_de."
),
},
{
"id": "IWSLT14",
"display_name": "IWSLT14",
"task": "iwslt14",
"tags": ["MT", "decanlp"],
"description": (
"2014 International Workshop on Spoken Language task, currently "
"only includes en_de and de_en."
),
"links": {"website": "https://wit3.fbk.eu"},
},
{
"id": "ConvAI2",
"display_name": "ConvAI2",
"task": "convai2",
"tags": ["ChitChat", "Dodeca"],
"description": (
"A chit-chat dataset based on PersonaChat for a NIPS 2018 competition. "
),
"links": {
"arXiv": "https://arxiv.org/abs/1801.07243",
"website": "http://convai.io/",
},
},
{
"id": "ConvAI_ChitChat",
"display_name": "ConvAI_ChitChat",
"task": "convai_chitchat",
"tags": ["ChitChat", "decanlp"],
"description": (
"Human-bot dialogues containing free discussions of randomly chosen "
"paragraphs from SQuAD."
),
"links": {"website": "http://convai.io/data/"},
},
{
"id": "Dialogue_QE",
"display_name": "Dialogue_QE",
"task": "dialogue_qe",
"tags": [],
"description": (
"Human-bot dialogues labelled for quality at the level of "
"dialogues. Can be used to train dialogue-level metric for dialogue "
"systems."
),
},
{
"id": "QAngaroo",
"display_name": "QAngaroo",
"task": "qangaroo",
"tags": ["QA"],
"description": (
"Reading Comprehension with Multiple Hop. Including two datasets: "
"WIKIHOP built on on wikipedia, MEDHOP built on paper abstracts from "
"PubMed."
),
"links": {"website": "http://qangaroo.cs.ucl.ac.uk/"},
},
{
"id": "SCAN",
"display_name": "SCAN",
"task": "scan",
"tags": ["Goal"],
"description": (
"SCAN is a set of simple language-driven navigation tasks for "
"studying compositional learning and zero-shot generalization. The "
"SCAN tasks were inspired by the CommAI environment, which is the "
"origin of the acronym (Simplified versions of the CommAI Navigation "
"tasks)."
),
"links": {
"arXiv": "https://arxiv.org/abs/1711.00350",
"website": "https://github.com/brendenlake/SCAN",
},
},
{
"id": "Persona-Chat",
"display_name": "Persona-Chat",
"task": "personachat",
"tags": ["ChitChat"],
"description": (
"A chit-chat dataset where paired Turkers are given assigned "
"personas and chat to try to get to know each other."
),
"links": {"arXiv": "https://arxiv.org/abs/1801.07243"},
},
{
"id": "TaskMaster",
"display_name": "TaskMaster-1-2019",
"task": "taskmaster",
"tags": ["ChitChat"],
"description": (
"A chit-chat dataset by GoogleAI providing high quality goal-oriented conversations"
"The dataset hopes to provoke interest in written vs spoken language"
"Both the datasets consists of two-person dialogs:"
"Spoken: Created using Wizard of Oz methodology. "
"Written: Created by crowdsourced workers who were asked to write the "
"full conversation themselves playing roles of both the user and assistant."
),
"links": {"website": "https://ai.google/tools/datasets/taskmaster-1"},
},
{
"id": "MSR-E2E",
"display_name": "MSR End-to-End",
"task": "msr_e2e",
"tags": ["ChitChat"],
"description": (
"MSR-E2E is a dataset of human-human conversations in which one "
"human plays the role of an Agent and the other one plays the role"
"of a User. Data is collected from Amazon Mechanical Turk. "
),
"links": {"website": "https://github.com/xiul-msr/e2e_dialog_challenge"},
},
{
"id": "Twitter",
"display_name": "Twitter",
"task": "twitter",
"tags": ["ChitChat", "Dodeca"],
"description": (
"Twitter data found on GitHub. No "
"train/valid/test split was provided so 10k for valid and 10k for "
"test was chosen at random."
),
"links": {"website": "https://github.com/Marsan-Ma/chat_corpus/"},
},
{
"id": "Wikipedia",
"display_name": "Wikipedia",
"task": 'wikipedia',
"tags": [],
"description": ("Dump of Wikipedia articles from 2/3/18"),
"notes": (
"Specify ':full' for the full articles to be returned, otherwise "
"defaults to ':summary', which provides the first paragraphs. To put "
"the article in the labels and the title in the text, specify "
"':key-value' at the end (for a title/content key-value "
"association)"
),
},
{
"id": "Flickr30k",
"display_name": "Flickr30k",
"task": "flickr30k",
"tags": ["Visual"],
"description": ("30k captioned images pulled from Flickr compiled by UIUC. "),
"links": {
"website": "http://web.engr.illinois.edu/~bplumme2/Flickr30kEntities/",
"paper1": "https://arxiv.org/abs/1505.04870v2",
"paper2": "http://aclweb.org/anthology/Q14-1006",
},
},
{
"id": "COCO_Captions",
"display_name": "COCO_Captions",
"task": "coco_caption",
"tags": ["Visual"],
"description": (
"COCO annotations derived from the 2015 COCO Caption Competition. "
),
"links": {"website": "http://cocodataset.org/"},
},
{
"id": "integration_tests",
"display_name": "Integration Tests",
"task": "integration_tests",
"tags": ["Debug"],
"description": ("Artificial tasks for ensuring models perform as expected"),
},
{
"id": "ConvAI2_wild_evaluation",
"display_name": "ConvAI2_wild_evaluation",
"task": "convai2_wild_evaluation",
"tags": ["ChitChat"],
"description": (
"Dataset collected during the wild evaluation of ConvaAI2 participants "
"bots. 60% train, 20% valid and 20% test is chosen at "