turku_hockey_data2text.json

{
  "overview": {
    "where": {
      "has-leaderboard": "no",
      "leaderboard-url": "N/A",
      "leaderboard-description": "N/A",
      "website": "https://turkunlp.org/hockey_data2text.html",
      "data-url": "https://github.com/TurkuNLP/Turku-hockey-data2text",
      "paper-url": "https://aclanthology.org/W19-6125/",
      "paper-bibtext": "@inproceedings{kanerva2019newsgen,\n  Title = {Template-free Data-to-Text Generation of Finnish Sports News},\n  Author = {Jenna Kanerva and Samuel R{\\\"o}nnqvist and Riina Kekki and Tapio Salakoski and Filip Ginter},\n  booktitle = {Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa\u201919)},\n  year={2019}\n  }",
      "contact-name": "Jenna Kanerva, Filip Ginter",
      "contact-email": "jmnybl@utu.fi, figint@utu.fi"
    },
    "languages": {
      "is-multilingual": "no",
      "license": "cc-by-nc-sa-4.0: Creative Commons Attribution Non Commercial Share Alike 4.0 International",
      "task-other": "N/A",
      "language-names": [
        "Finnish"
      ],
      "language-dialects": "written standard language",
      "intended-use": "This dataset was developed as a benchmark for evaluating template-free, machine learning methods on Finnish news generation in the area of ice hockey reporting.",
      "license-other": "N/A",
      "task": "Data-to-Text",
      "communicative": "Describe an event from an ice hockey game based on the given structural data.",
      "language-speakers": "The original news articles are written by professional journalists. The text passages extracted in the annotation may be slightly edited compared to the original language during the corpus annotation."
    },
    "credit": {
      "organization-type": [
        "academic"
      ],
      "organization-names": "University of Turku",
      "creators": "Jenna Kanerva, Samuel R\u00f6nnqvist, Riina Kekki, Tapio Salakoski, Filip Ginter (TurkuNLP / University of Turku)",
      "funding": "The project was supported by the Google Digital News Innovation Fund.",
      "gem-added-by": "Jenna Kanerva, Filip Ginter (TurkuNLP / University of Turku)"
    },
    "structure": {
      "data-fields": "The dataset is constructed of games, where each game is a list of events. If the event was annotated (corresponding sentence was found from the news article), it includes `text` field with value other than empty string (\"\").\n\nFor each game (dict), there are keys `gem_id` (string), `id` (string), `news_article` (string), and `events` (list).\n\nFor each event (dict), there are different, relevant keys available with non empty values depending on the event type (e.g. goal or penalty). The mandatory keys for each event are `event_id` (string), `event_type` (string), `text` (string, empty string if not annotated), and `multi_reference` (bool). The keys not relevant for the specific event type are left empty.\n\nThe relevant keys in the event dictionary are:\n\nFor each event type, the following keys are relevant:\n  `event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)\n  `event_type`: Type of the event, possible values are `game result`, `goal`, `penalty`, or `saves` (string)\n  `text`: Natural language description of the event, or empty string if not available (string)\n  `multi_reference`: Does this event refer to a text passage describing multiple events? (bool)\n\n\nThe rest of the fields are specific to the event type. The relevant fields for each event type are:\n\ngame result:\n  `event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)\n  `event_type`: Type of the event (string)\n  `home_team`: Name of the home team (string)\n  `guest_team`: Name of the guest team (string)\n  `score`: Final score of the game, in the form of home\u2013guest (string)\n  `periods`: Scores for individual periods, each in the form of home\u2013guest score in that period (list of strings)\n  `features`: Additional features, such as overtime win or shoot out (list of strings)\n  `text`: Natural language description of the event, or empty string if not available (string)\n  `multi_reference`: Does this event refer to a text passage describing multiple events? (bool)\n\ngoal:\n  `event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)\n  `event_type`: Type of the event (string)\n  `player`: Name of the player scoring (string)\n  `assist`: Names of the players assisting, at most two players (list of strings)\n  `team`: Team scoring with possible values of `home` or `guest` (string)\n  `team_name`: Name of the team scoring (string)\n  `score`: Score after the goal, in the form of home\u2013guest (string)\n  `time`: Time of the goal, minutes and seconds from the beginning (string)\n  `features`: Additional features, such as power play or short-handed goal (list of strings)\n  `text`: Natural language description of the event, or empty string if not available (string)\n  `multi_reference`: Does this event refer to a text passage describing multiple events? (bool)\n\npenalty:\n  `event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)\n  `event_type`: Type of the event (string)\n  `player`: Name of the player getting the penalty (string)\n  `team`: Team getting the penalty with possible values of `home` or `guest` (string)\n  `team_name`: Name of the team getting the penalty (string)\n  `penalty_minutes`: Penalty minutes (string)\n  `time`: Time of the penalty, minutes and seconds from the beginning (string)\n  `text`: Natural language description of the event, or empty string if not available (string)\n  `multi_reference`: Does this event refer to a text passage describing multiple events? (bool)\n\nsaves:\n `event_id`: Identifier of the event, unique to the game but not globally, in chronological order (string)\n  `event_type`: Type of the event (string)\n  `player`: Name of the goalkeeper (string)\n  `team`: Team of the goalkeeper with possible values of `home` or `guest` (string)\n  `team_name`: Name of the team (string)\n  `saves`: Number of saves in the game (string)\n  `text`: Natural language description of the event, or empty string if not available (string)\n  `multi_reference`: Does this event refer to a text passage describing multiple events? (bool)\n\n\nText passages describing multiple events (multi_reference):\n\nSome text passages refer to multiple events in such way that separating them to individual statements is not adequate (e.g. \"The home team received two penalties towards the end of the first period.\"). In these cases, multiple events are aligned to the same text passage so that the first event (in chronological order) include the annotated text passage, while the rest of the events referring to the same text passage include the identifier of the first event in the annotated text field (e.g. `text`: \"E4\").",
      "structure-example": "{\n  'gem_id': 'gem-turku_hockey_data2text-train-0',\n  'id': '20061031-TPS-HPK',\n  'news_article': 'HPK:n hyv\u00e4 syysvire jatkuu j\u00e4\u00e4kiekon SM-liigassa. Tiistaina HPK kukisti mainiolla liikkeell\u00e4 ja tehokkaalla ylivoimapelill\u00e4 TPS:n vieraissa 1\u20130 (1\u20130, 0\u20130, 0\u20130).\\nHPK hy\u00f6dynsi ylivoimaa mennen jo ensimm\u00e4isess\u00e4 er\u00e4ss\u00e4 Mikko M\u00e4enp\u00e4\u00e4n maalilla 1\u20130 -johtoon.\\nToisessa ja kolmannessa er\u00e4ss\u00e4 HPK tarjosi edelleen TPS:lle runsaasti tilanteita, mutta maalia eiv\u00e4t turkulaiset mill\u00e4\u00e4n ilveell\u00e4 saaneet. Pahin este oli loistavan pelin H\u00e4meenlinnan maalilla pelannut Mika Oksa.\\nTPS:n maalissa Jani Hurme ei osumille mit\u00e4\u00e4n mahtanut. Joukkueen suuri yksin\u00e4inen kentt\u00e4pelaaja oli Kai Nurminen, mutta h\u00e4nell\u00e4k\u00e4\u00e4n ei ollut onnea maalitilanteissa.',\n  'events':\n    {\n      'event_id': ['E1', 'E2', 'E3'],\n      'event_type': ['game result', 'penalty', 'goal'],\n      'text': ['HPK kukisti TPS:n vieraissa 1\u20130 (1\u20130, 0\u20130, 0\u20130).', '', 'HPK hy\u00f6dynsi ylivoimaa mennen jo ensimm\u00e4isess\u00e4 er\u00e4ss\u00e4 Mikko M\u00e4enp\u00e4\u00e4n maalilla 1\u20130 -johtoon.'],\n      'home_team': ['TPS', '', ''],\n      'guest_team': ['HPK', '', ''],\n      'score': ['0\u20131', '', '0\u20131'],\n      'periods': [['0\u20131', '0\u20130', '0\u20130'], [], []],\n      'features': [[], [], ['power play']],\n      'player': ['', 'Fredrik Svensson', 'Mikko M\u00e4enp\u00e4\u00e4'],\n      'assist': [[], [], ['Jani Kein\u00e4nen', 'Toni M\u00e4kiaho']],\n      'team': ['', 'guest', 'guest'],\n      'team_name': ['', 'HPK', 'HPK'],\n      'time': ['', '9.28', '14.57'],\n      'penalty_minutes': ['', '2', ''],\n      'saves': ['', '', ''],\n      'multi_reference': [false, false, false]\n    }\n}",
      "structure-splits": "The corpus include 3 splits: train, validation, and test.",
      "structure-description": ""
    }
  },
  "curation": {
    "original": {
      "is-aggregated": "no",
      "aggregated-sources": "N/A",
      "rationale": "The dataset is designed for text generation (data2text), where the original source of natural language descriptions is news articles written by journalists. While the link between structural data (ice hockey game statistics) and the news articles describing the game  was quite weak (news articles including a lot of information not derivable from the statistics, while leaving many events unmentioned), the corpus includes full manual annotation aligning the events extracted from game statistics and the corresponding natural language passages extracted from the news articles.\n\nEach event is manually aligned into a sentence-like passage, and in case a suitable passage was not found, the annotation is left empty (with value `None`). The extracted passages were manually modified not to include additional information not derivable from the game statistics, or not considered as world knowledge. The manual curation of passages is designed to prevent model hallucination, i.e. model learning to generate facts not derivable from the input data.",
      "communicative": "Describing the given events (structural data) in natural language, and therefore generating ice hockey game reports."
    },
    "language": {
      "found": [],
      "crowdsourced": [],
      "created": "N/A",
      "machine-generated": "N/A",
      "validated": "not validated",
      "is-filtered": "algorithmically",
      "filtered-criteria": "Include only games, where both game statistics and a news article describing the game were available (based on timestamps and team names).",
      "obtained": [
        "Other"
      ],
      "producers-description": "The initial data, both game statistics and news articles, were obtained from the Finnish News Agency STT news archives released for academic use (http://urn.fi/urn:nbn:fi:lb-2019041501). The original news articles are written by professional journalists.\n\nWe (TurkuNLP) gratefully acknowledge the collaboration of Maija Paikkala, Salla Salmela and Pihla Lehmusjoki from the Finnish News Agency STT while creating the corpus.",
      "topics": "Ice hockey, news",
      "pre-processed": "N/A"
    },
    "annotations": {
      "origin": "expert created",
      "rater-number": "1",
      "rater-qualifications": "Members of the TurkuNLP research group, native speakers of Finnish.",
      "rater-training-num": "1",
      "rater-test-num": "1",
      "rater-annotation-service-bool": "no",
      "rater-annotation-service": [],
      "values": "Manual alignment of events and their natural language descriptions. Removing information not derivable from the input data or world knowledge in order to prevent the model 'hallucination'.",
      "quality-control": "validated by data curators",
      "quality-control-details": "Manual inspection of examples during the initial annotation training phrase."
    },
    "consent": {
      "has-consent": "yes",
      "consent-policy": "The corpus license was agreed with the providers of the source material.",
      "consent-other": "",
      "no-consent-justification": "N/A"
    },
    "pii": {
      "has-pii": "yes/very likely",
      "no-pii-justification": "N/A",
      "is-pii-identified": "no identification",
      "pii-identified-method": "N/A",
      "is-pii-replaced": "N/A",
      "pii-replaced-method": "N/A",
      "pii-categories": [
        "generic PII"
      ]
    },
    "maintenance": {
      "has-maintenance": "no",
      "description": "N/A",
      "contact": "N/A",
      "contestation-mechanism": "N/A",
      "contestation-link": "N/A",
      "contestation-description": "N/A"
    }
  },
  "gem": {
    "rationale": {
      "sole-task-dataset": "yes",
      "distinction-description": "This is the only data2text corpus for Finnish in GEM.",
      "sole-language-task-dataset": "yes",
      "contribution": "The dataset was created to develop machine learned text generation models for Finnish ice hockey news, where the generation would reflect the natural language variation found from the game reports written by professional journalists. While the original game reports often include additional information not derivable from the game statistics, the corpus was fully manually curated to remove all such  information from the natural language descriptions. The rationale of such curation was to prevent model 'hallucinating' additional facts.",
      "model-ability": "morphological inflection, language variation"
    },
    "curation": {
      "has-additional-curation": "yes",
      "modification-types": [
        "data points modified"
      ],
      "modification-description": "Structural data was translated into English.",
      "has-additional-splits": "no",
      "additional-splits-description": "N/A",
      "additional-splits-capacicites": "N/A"
    },
    "starting": {}
  },
  "results": {
    "results": {
      "other-metrics-definitions": "N/A",
      "has-previous-results": "yes",
      "current-evaluation": "N/A",
      "previous-results": "N/A",
      "original-evaluation": "Automatic evaluation: BLEU, NIST, METEOR, ROUGE-L, CIDEr\nManual evaluation: factual mistakes, grammatical errors, minimum edit distance to an acceptable game report (using WER)",
      "metrics": [
        "BLEU",
        "METEOR",
        "ROUGE",
        "WER"
      ]
    }
  },
  "considerations": {
    "pii": {
      "risks-description": "None"
    },
    "licenses": {
      "dataset-restrictions-other": "N/A",
      "data-copyright-other": "N/A",
      "dataset-restrictions": [
        "non-commercial use only"
      ],
      "data-copyright": [
        "non-commercial use only"
      ]
    },
    "limitations": {}
  },
  "context": {
    "previous": {
      "is-deployed": "no",
      "described-risks": "N/A",
      "changes-from-observation": "N/A"
    },
    "underserved": {
      "helps-underserved": "no",
      "underserved-description": "N/A"
    },
    "biases": {
      "has-biases": "no",
      "bias-analyses": "N/A",
      "speaker-distibution": "The dataset represents only written standard language. "
    }
  }
}