text: #!/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": "AQuA",
          "display_name": "AQuA",
          "task": "aqua",
          "tags": ["All", "QA"],
          "description": (
              "Dataset containing algebraic word problems with rationales for "
              "their answers. From Ling et. al. 2017, Link: "
              "https://arxiv.org/pdf/1705.04146.pdf"
          )
      },
      {
          "id": "bAbI-1k",
          "display_name": "bAbI 1k",
          "task": "babi:All1k",
          "tags": ["All", "QA"],
          "description": (
              "20 synthetic tasks that each test a unique aspect of text and "
              "reasoning, and hence test different capabilities of learning "
              "models. From Weston et al. '16. Link: "
              "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": ["All", "QA"],
          "description": (
              "20 synthetic tasks that each test a unique aspect of text and "
              "reasoning, and hence test different capabilities of learning "
              "models. From Weston et al. '16. Link: "
              "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": "BookTest",
          "display_name": "BookTest",
          "task": "booktest",
          "tags": ["All", "Cloze"],
          "description": (
              "Sentence completion given a few sentences as context from a book. "
              "A larger version of CBT. From Bajgar et al., 16. Link: "
              "https://arxiv.org/abs/1610.00956"
          ),
      },
      {
          "id": "CBT",
          "display_name": "Children's Book Test (CBT)",
          "task": "cbt",
          "tags": ["All", "Cloze"],
          "description": (
              "Sentence completion given a few sentences as context from a "
              "children's book. From Hill et al., '16. Link: "
              "https://arxiv.org/abs/1511.02301"
          ),
      },
      {
          "id": "COPA",
          "display_name": "Choice of Plausible Alternatives",
          "task": "copa",
          "tags": ["All", "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. See http://people.ict.usc.edu/~gordon/copa.html for more "
              "information"
          ),
      },
      {
          "id": "CornellMovie",
          "display_name": "Cornell Movie",
          "task": "cornell_movie",
          "tags": ["All", "ChitChat"],
          "description": (
              "Fictional conversations extracted from raw movie scripts. "
              "Danescu-Niculescu-Mizil & Lee, '11. Link: "
              "https://arxiv.org/abs/1106.3077"
          ),
      },
      {
          "id": "DBLL-bAbI",
          "display_name": "Dialog Based Language Learning: bAbI Task",
          "task": "dbll_babi",
          "tags": ["All", "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. From Weston '16. Link: "
              "https://arxiv.org/abs/1604.06045. Tasks can be accessed with a "
              "format like: 'python examples/display_data.py -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": ["All", "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. From Weston '16. Link: "
              "https://arxiv.org/abs/1604.06045"
          ),
      },
      {
          "id": "dialog-bAbI",
          "display_name": "Dialog bAbI",
          "task": "dialog_babi",
          "tags": ["All", "Goal"],
          "description": (
              "Simulated dialogs of restaurant booking, from Bordes et al. '16. "
              "Link: https://arxiv.org/abs/1605.07683"
          ),
      },
      {
          "id": "dialog-bAbI-plus",
          "display_name": "Dialog bAbI+",
          "task": "dialog_babi_plus",
          "tags": ["All", "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. "
              "See https://www.researchgate.net/publication/319128941_Challenging_Neural_"
              "Dialogue_Models_with_Natural_Data_Memory_Networks_Fail_on_"
              "Incremental_Phenomena,http://aclweb.org/anthology/D17-1235"
          ),
      },
      {
          "id": "FVQA",
          "display_name": "FVQA",
          "task": "fvqa",
          "tags": ["All", "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>.  Link: "
              "https://arxiv.org/abs/1606.05433"
  
          ),
      },
      {
          "id": "DealNoDeal",
          "display_name": "Deal or No Deal",
          "task": "dealnodeal",
          "tags": ["All", "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. From Lewis et al. '17. Link: "
              "https://arxiv.org/abs/1706.05125"
          ),
      },
      {
          "id": "MutualFriends",
          "display_name": "MutualFriends",
          "task": "mutualfriends",
          "tags": ["All", "Goal"],
          "description": (
              "Task where two agents must discover which friend of theirs is "
              "mutual based on the friends's attributes. From He He et al. '17. "
              "Link: https://stanfordnlp.github.io/cocoa/"
          ),
      },
      {
          "id": "MCTest",
          "display_name": "MCTest",
          "task": "mctest",
          "tags": ["All", "QA"],
          "description": (
              "Questions about short children's stories, from Richardson et al. "
              "'13. Link: 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": ["All", "QA", "MovieDD"],
          "description": (
              "Closed-domain QA dataset asking templated questions about movies, "
              "answerable from Wikipedia, similar to WikiMovies. From Dodge et al. "
              "'15. Link: https://arxiv.org/abs/1511.06931"
          ),
      },
      {
          "id": "MovieDD-QARecs",
          "display_name": "Movie Dialog QA Recommendations",
          "task": "moviedialog:Task:3",
          "tags": ["All", "Goal", "MovieDD"],
          "description": (
              "Dialogs discussing questions about movies as well as "
              "recommendations. From Dodge et al. '15. Link: "
              "https://arxiv.org/abs/1511.06931"
          ),
      },
      {
          "id": "MovieDD-Recs",
          "display_name": "Movie Dialog Recommendations",
          "task": "moviedialog:Task:2",
          "tags": ["All", "QA", "MovieDD"],
          "description": (
              "Questions asking for movie recommendations. From Dodge et al. '15. "
              "Link: https://arxiv.org/abs/1511.06931"
          ),
      },
      {
          "id": "MovieDD-Reddit",
          "display_name": "Movie Dialog Reddit",
          "task": "moviedialog:Task:4",
          "tags": ["All", "ChitChat", "MovieDD"],
          "description": (
              "Dialogs discussing Movies from Reddit (the Movies SubReddit). From "
              "Dodge et al. '15. Link: https://arxiv.org/abs/1511.06931"
          ),
      },
      {
          "id": "MTurkWikiMovies",
          "display_name": "MTurk WikiMovies",
          "task": "mturkwikimovies",
          "tags": ["All", "QA"],
          "description": (
              "Closed-domain QA dataset asking MTurk-derived questions about "
              "movies, answerable from Wikipedia. From Li et al. '16. Link: "
              "https://arxiv.org/abs/1611.09823"
          ),
      },
      {
          "id": "MultiNLI",
          "display_name": "MultiNLI",
          "task": "multinli",
          "tags": ["All", "Entailment"],
          "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. From Williams et al. '17. Link: "
              "https://arxiv.org/abs/1704.05426"
          ),
      },
      {
          "id": "NarrativeQA",
          "display_name": "NarrativeQA",
          "task": "narrative_qa",
          "tags": ["All", "QA"],
          "description": (
              "A dataset and set of tasks in which the reader must answer "
              "questions about stories by reading entire books or movie scripts. "
              "From Kočiský et. al. '17. Link: 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": "OpenSubtitles",
          "display_name": "Open Subtitles",
          "task": "opensubtitles",
          "tags": ["All", "ChitChat"],
          "description": (
              "Dataset of dialogs from movie scripts. Version 2018: "
              "http://opus.lingfil.uu.se/OpenSubtitles2018.php, version 2009: "
              "http://opus.lingfil.uu.se/OpenSubtitles.php. A variant of the "
              "dataset used in Vinyals & Le '15, "
              "https://arxiv.org/abs/1506.05869."
          ),
      },
      {
          "id": "personalized-dialog-full",
          "display_name": "Personalized Dialog Full Set",
          "task": "personalized_dialog:AllFull",
          "tags": ["All", "Goal", "Personalization"],
          "description": (
              "Simulated dataset of restaurant booking focused on personalization "
              "based on user profiles. From Joshi et al. '17. Link: "
              "https://arxiv.org/abs/1706.07503"
          ),
      },
      {
          "id": "personalized-dialog-small",
          "display_name": "Personalized Dialog Small Set",
          "task": "personalized_dialog:AllSmall",
          "tags": ["All", "Goal", "Personalization"],
          "description": (
              "Simulated dataset of restaurant booking focused on personalization "
              "based on user profiles. From Joshi et al. '17. Link: "
              "https://arxiv.org/abs/1706.07503"
          ),
      },
      {
          "id": "QACNN",
          "display_name": "QA CNN",
          "task": "qacnn",
          "tags": ["All", "Cloze"],
          "description": (
              "Cloze dataset based on a missing (anonymized) entity phrase from a "
              "CNN article, Hermann et al. '15. Link: "
              "https://arxiv.org/abs/1506.03340"
          ),
      },
      {
          "id": "QADailyMail",
          "display_name": "QA Daily Mail",
          "task": "qadailymail",
          "tags": ["All", "Cloze"],
          "description": (
              "Cloze dataset based on a missing (anonymized) entity phrase from a "
              "Daily Mail article, Hermann et al. '15. Link: "
              "https://arxiv.org/abs/1506.03340"
          ),
      },
      {
          "id": "SimpleQuestions",
          "display_name": "Simple Questions",
          "task": "simplequestions",
          "tags": ["All", "QA"],
          "description": (
              "Open-domain QA dataset based on Freebase triples from Bordes et "
              "al. '15. Link: https://arxiv.org/abs/1506.02075"
          ),
      },
      {
          "id": "SNLI",
          "display_name": "The Stanford Natural Language Inference (SNLI) Corpus",
          "task": "snli",
          "tags": ["All", "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). See "
              "https://nlp.stanford.edu/projects/snli/"
          ),
      },
      {
          "id": "SQuAD2",
          "display_name": "SQuAD2",
          "task": "squad2",
          "tags": ["All", "QA"],
          "description": (
              "Open-domain QA dataset answerable from a given paragraph from "
              "Wikipedia, from Rajpurkar & Jia et al. '18. Link: "
              "http://arxiv.org/abs/1806.03822"
          ),
      },
      {
          "id": "SQuAD",
          "display_name": "SQuAD",
          "task": "squad",
          "tags": ["All", "QA"],
          "description": (
              "Open-domain QA dataset answerable from a given paragraph from "
              "Wikipedia, from Rajpurkar et al. '16. Link: "
              "https://arxiv.org/abs/1606.05250"
          ),
      },
      {
          "id": "TriviaQA",
          "display_name": "TriviaQA",
          "task": "triviaqa",
          "tags": ["All", "QA"],
          "description": (
              "Open-domain QA dataset with question-answer-evidence triples, from "
              "Joshi et al. '17. Link: https://arxiv.org/abs/1705.03551"
          ),
      },
      {
          "id": "TaskNTalk",
          "display_name": "Task N' Talk",
          "task": "taskntalk",
          "tags": ["All", "Goal"],
          "description": (
              "Dataset of synthetic shapes described by attributes, for agents to "
              "play a cooperative QA game, from Kottur et al. '17. Link: "
              "https://arxiv.org/abs/1706.08502"
          ),
      },
      {
          "id": "Ubuntu",
          "display_name": "Ubuntu",
          "task": "ubuntu",
          "tags": ["All", "ChitChat"],
          "description": (
              "Dialogs between an Ubuntu user and an expert trying to fix issue, "
              "from Lowe et al. '15. Link: https://arxiv.org/abs/1506.08909"
          ),
      },
      {
          "id": "WebQuestions",
          "display_name": "Web Questions",
          "task": "webquestions",
          "tags": ["All", "QA"],
          "description": (
              "Open-domain QA dataset from Web queries from Berant et al. '13. "
              "Link: http://www.aclweb.org/anthology/D13-1160"
          ),
      },
      {
          "id": "WikiMovies",
          "display_name": "WikiMovies",
          "task": "wikimovies",
          "tags": ["All", "QA"],
          "description": (
              "Closed-domain QA dataset asking templated questions about movies, "
              "answerable from Wikipedia. From Miller et al. '16. Link: "
              "https://arxiv.org/abs/1606.03126"
          ),
      },
      {
          "id": "WikiQA",
          "display_name": "WikiQA",
          "task": "wikiqa",
          "tags": ["All", "QA"],
          "description": (
              "Open domain QA from Wikipedia dataset from Yang et al. '15. Link: "
              "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": ["All", "Visual"],
          "description": (
              "Open-ended question answering about visual content. From Agrawal "
              "et al. '15. Link: https://arxiv.org/abs/1505.00468"
          ),
      },
      {
          "id": "VQAv2",
          "display_name": "VQAv2",
          "task": "vqa_v2",
          "tags": ["All", "Visual"],
          "description": (
              "Bigger, more balanced version of the original VQA dataset. From "
              "Goyal et al. '16. Link: https://arxiv.org/abs/1612.00837"
          ),
      },
      {
          "id": "VisDial",
          "display_name": "VisDial",
          "task": "visdial",
          "tags": ["All", "Visual"],
          "description": (
              "Task which requires agents to hold a meaningful dialog about "
              "visual content. From Das et al. '16. Link: "
              "https://arxiv.org/abs/1611.08669"
          ),
      },
      {
          "id": "MNIST_QA",
          "display_name": "MNIST_QA",
          "task": "mnist_qa",
          "tags": ["All", "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": ["All", "QA"],
          "description": (
              "Task which requires agents to identify high quality answers "
              "composed by professionals with deep domain knowledge. From Feng et "
              "al. '15. Link: https://arxiv.org/abs/1508.01585"
          ),
      },
      {
          "id": "MS_MARCO",
          "display_name": "MS_MARCO",
          "task": "ms_marco",
          "tags": ["All", "QA"],
          "description": (
              "A large scale Machine Reading Comprehension Dataset with questions "
              "sampled from real anonymized user queries and contexts from web "
              "documents. From Nguyen et al. '16. Link: "
              "https://arxiv.org/abs/1611.09268"
          ),
      },
      {
          "id": "CLEVR",
          "display_name": "CLEVR",
          "task": "clevr",
          "tags": ["All", "Visual"],
          "description": (
              "A visual reasoning dataset that tests abilities such as attribute "
              "identification, counting, comparison, spatial relationships, and "
              "logical operations. From Johnson et al. '16. Link: "
              "https://arxiv.org/abs/1612.06890"
          ),
      },
      {
          "id": "nlvr",
          "display_name": "nlvr",
          "task": "nlvr",
          "tags": ["All", "Visual"],
          "description": (
              "Cornell Natural Language Visual Reasoning (NLVR) is a language "
              "grounding dataset based on  pairs of natural language statements "
              "grounded in synthetic images. From Suhr et al. '17. Link: "
              "http://lic.nlp.cornell.edu/nlvr/"
          ),
      },
      {
          "id": "WMT",
          "display_name": "WMT",
          "task": "wmt",
          "tags": ["All", "MT"],
          "description": (
              "Workshop on Machine Translation task, currently only includes en_de."
          ),
      },
      {
          "id": "IWSLT14",
          "display_name": "IWSLT14",
          "task": "iwslt14",
          "tags": ["All", "MT"],
          "description": (
              "2014 International Workshop on Spoken Language task, currently "
              "only includes en_de and de_en. From Cettolo et al. '12. Link: "
              "wit3.fbk.eu"
          ),
      },
      {
          "id": "ConvAI2",
          "display_name": "ConvAI2",
          "task": "convai2",
          "tags": ["All", "ChitChat"],
          "description": (
              "A chit-chat dataset based on PersonaChat "
              "(https://arxiv.org/abs/1801.07243) for a NIPS 2018 competition. "
              "Link: http://convai.io/."
          ),
      },
      {
          "id": "ConvAI_ChitChat",
          "display_name": "ConvAI_ChitChat",
          "task": "convai_chitchat",
          "tags": ["All", "ChitChat"],
          "description": (
              "Human-bot dialogues containing free discussions of randomly chosen "
              "paragraphs from SQuAD. Link to dataset: http://convai.io/data/"
          ),
      },
      {
          "id": "Dialogue_QE",
          "display_name": "Dialogue_QE",
          "task": "dialogue_qe",
          "tags": ["All"],
          "description": (
              "Human-bot dialogues labelled for quality at the level of "
              "dialogues. Can be used to train dialogue-level metric for dialogue "
              "systems. Link to dataset: http://convai.io/data/"
          ),
      },
      {
          "id": "QAngaroo",
          "display_name": "QAngaroo",
          "task": "qangaroo",
          "tags": ["All", "QA"],
          "description": (
              "Reading Comprehension with Multiple Hop. Including two datasets: "
              "WIKIHOP built on on wikipedia, MEDHOP built on paper abstracts from "
              "PubMed. Link to dataset: http://qangaroo.cs.ucl.ac.uk/"
          ),
      },
      {
          "id": "SCAN",
          "display_name": "SCAN",
          "task": "scan",
          "tags": ["Goal", "All"],
          "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). See the paper: https://arxiv.org/abs/1711.00350 or data: "
              "https://github.com/brendenlake/SCAN"
          ),
      },
      {
          "id": "Persona-Chat",
          "display_name": "Persona-Chat",
          "task": "personachat",
          "tags": ["ChitChat", "All"],
          "description": (
              "A chit-chat dataset where paired Turkers are given assigned "
              "personas and chat to try to get to know each other. See the paper: "
              "https://arxiv.org/abs/1801.07243"
          ),
      },
      {
          "id": "Twitter",
          "display_name": "Twitter",
          "task": "twitter",
          "tags": ["All", "ChitChat"],
          "description": (
              "Twitter data from: https://github.com/Marsan-Ma/chat_corpus/. No "
              "train/valid/test split was provided so 10k for valid and 10k for "
              "test was chosen at random."
          ),
      },
      {
          "id": "Wikipedia",
          "display_name": "Wikipedia",
          "task": 'wikipedia',
          "tags": ["All"],
          "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": ["All", "Visual"],
          "description": (
              "30k captioned images pulled from Flickr compiled by UIUC: "
              "http://web.engr.illinois.edu/~bplumme2/Flickr30kEntities/. Based "
              "off of these papers: https://arxiv.org/abs/1505.04870v2, "
              "http://aclweb.org/anthology/Q14-1006"
          ),
      },
      {
          "id": "COCO_Captions",
          "display_name": "COCO_Captions",
          "task": "coco_caption",
          "tags": ["All", "Visual"],
          "description": (
              "COCO annotations derived from the 2015 COCO Caption Competition. "
              "Link to dataset: http://cocodataset.org/#download"
          ),
      },
      {
          "id": "integration_tests",
          "display_name": "Integration Tests",
          "task": "integration_tests",
          "tags": ["All", "Debug"],
          "description": (
              "Artificial tasks for ensuring models perform as expected"
          ),
      },
      {
          "id": "ConvAI2_wild_evaluation",
          "display_name": "ConvAI2_wild_evaluation",
          "task": "convai2_wild_evaluation",
          "tags": ["All", "ChitChat"],
          "description": (
              "Dataset collected during the wild evaluation of ConvaAI2 participants "
              "bots (http://convai.io). 60% train, 20% valid and 20% test is chosen at "
              "random from the whole dataset."
          ),
      },
      {
          "id": "Personality_Captions",
          "display_name": "Personality_Captions",
          "task": "personality_captions",
          "tags": ["All", "Visual"],
          "description": (
              "200k images from the YFCC100m dataset "
              "(https://multimediacommons.wordpress.com/yfcc100m-core-dataset/), "
              "with captions conditioned on one of 215 personalities. See "
              "https://arxiv.org/abs/1810.10665 for more information."
          ),
          "notes": (
              "If you have already downloaded the images, please specify with "
              "the `--yfcc-path` flag, as the image download script takes a "
              "very long time to run"
          ),
      },
      {
          "id": "Image_Chat",
          "display_name": "Image_Chat",
          "task": "image_chat",
          "tags": ["All", "Visual", "ChitChat"],
          "description": (
              "202k dialogues and 401k utterances over 202k images from "
              "the YFCC100m dataset"
              "(https://multimediacommons.wordpress.com/yfcc100m-core-dataset/)"
              "using 215 possible personality traits"
              "see https://klshuster.github.io/image_chat/ for more information."
          ),
          "notes": (
              "If you have already downloaded the images, please specify with "
              "the `--yfcc-path` flag, as the image download script takes a "
              "very long time to run"
          ),
      },
      {
          "id": "TalkTheWalk",
          "display_name": "Talk the Walk",
          "task": "talkthewalk",
          "tags": ["All", "Visual"],
          "description": (
              "Talk the walk dataset."
              "See https://arxiv.org/abs/1807.03367 for more information."
          ),
      },
      {
          "id": "Wizard_of_Wikipedia",
          "display_name": "Wizard_of_Wikipedia",
          "task": "wizard_of_wikipedia",
          "tags": ["All", "ChitChat"],
          "description": (
              "A dataset with conversations directly grounded with knowledge "
              "retrieved from Wikipedia. Contains 201k utterances from 22k "
              "dialogues spanning over 1300 diverse topics, split into train, "
              "test, and valid sets. The test and valid sets are split "
              "into two sets each: one with overlapping topics with the train "
              "set, and one with unseen topics."
              "See https://arxiv.org/abs/1811.01241 for more information."
          ),
          "notes": (
              "To access the different valid/test splits (unseen/seen), specify "
              "the corresponding split (`random_split` for seen, `topic_split` "
              "for unseen) after the last colon in the task. "
              "E.g. `wizard_of_wikipedia:WizardDialogKnowledgeTeacher:random_split`"
          ),
      },
      {
          "id": "DailyDialog",
          "display_name": "Daily Dialog",
          "task": "dailydialog",
          "tags": ["All", "ChitChat"],
          "description": (
              "A dataset of chitchat dialogues with strong annotations for "
              "topic, emotion and utterance act. This version contains both sides "
              "of every conversation, and uses the official train/valid/test splits "
              "from the original authors. See https://arxiv.org/abs/1710.03957 "
              "for more information."
          )
      },
      {
          "id": "EmpatheticDialogues",
          "display_name": "Empathetic Dialogues",
          "task": "empathetic_dialogues",
          "tags": ["All", "ChitChat"],
          "description": (
              "A dataset of 25k conversations grounded in emotional situations "
              "to facilitate training and evaluating dialogue systems. See "
              "https://arxiv.org/abs/1811.00207 for more information."
          ),
      },
  ]
  