.

RotoWire_English-German.json

@@ -4,10 +4,10 @@
       "has-leaderboard": "no",
       "leaderboard-url": "N/A",
       "leaderboard-description": "N/A",
-      "website": "https://sites.google.com/view/wngt19/dgt-task",
-      "data-url": "https://github.com/neulab/dgt",
-      "paper-url": "https://www.aclweb.org/anthology/D19-5601/",
-      "paper-bibtext": "@inproceedings{hayashi-etal-2019-findings,\n    title = \"Findings of the Third Workshop on Neural Generation and Translation\",\n    author = \"Hayashi, Hiroaki  and\n      Oda, Yusuke  and\n      Birch, Alexandra  and\n      Konstas, Ioannis  and\n      Finch, Andrew  and\n      Luong, Minh-Thang  and\n      Neubig, Graham  and\n      Sudoh, Katsuhito\",\n    booktitle = \"Proceedings of the 3rd Workshop on Neural Generation and Translation\",\n    month = nov,\n    year = \"2019\",\n    address = \"Hong Kong\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/D19-5601\",\n    doi = \"10.18653/v1/D19-5601\",\n    pages = \"1--14\",\n    abstract = \"This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the two shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language.\",\n}",
+      "website": "[Website](https://sites.google.com/view/wngt19/dgt-task)",
+      "data-url": "[Github](https://github.com/neulab/dgt)",
+      "paper-url": "[ACL Anthology](https://www.aclweb.org/anthology/D19-5601/)",
+      "paper-bibtext": "```\n@inproceedings{hayashi-etal-2019-findings,\n    title = \"Findings of the Third Workshop on Neural Generation and Translation\",\n    author = \"Hayashi, Hiroaki  and\n      Oda, Yusuke  and\n      Birch, Alexandra  and\n      Konstas, Ioannis  and\n      Finch, Andrew  and\n      Luong, Minh-Thang  and\n      Neubig, Graham  and\n      Sudoh, Katsuhito\",\n    booktitle = \"Proceedings of the 3rd Workshop on Neural Generation and Translation\",\n    month = nov,\n    year = \"2019\",\n    address = \"Hong Kong\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/D19-5601\",\n    doi = \"10.18653/v1/D19-5601\",\n    pages = \"1--14\",\n    abstract = \"This document describes the findings of the Third Workshop on Neural Generation and Translation, held in concert with the annual conference of the Empirical Methods in Natural Language Processing (EMNLP 2019). First, we summarize the research trends of papers presented in the proceedings. Second, we describe the results of the two shared tasks 1) efficient neural machine translation (NMT) where participants were tasked with creating NMT systems that are both accurate and efficient, and 2) document generation and translation (DGT) where participants were tasked with developing systems that generate summaries from structured data, potentially with assistance from text in another language.\",\n}\n```",
       "contact-name": "Hiroaki Hayashi",
       "contact-email": "[email protected]"
     },
@@ -38,9 +38,12 @@
       "structure-description": "- Structured data are directly imported from the original RotoWire dataset.\n- Textual data (English, German) are associated to each sample.",
       "structure-labels": "N/A",
       "structure-splits": "- Train\n- Validation\n- Test",
-      "structure-example": "{\n  'id': '11_02_16-Jazz-Mavericks-TheUtahJazzdefeatedthe',\n  'gem_id': 'GEM-RotoWire_English-German-train-0'\n  'day': '11_02_16',\n  'home_city': 'Utah',\n  'home_name': 'Jazz',\n  'vis_city': 'Dallas',\n  'vis_name': 'Mavericks',\n  'home_line': {\n    'TEAM-FT_PCT': '58', ...\n  },\n  'vis_line': {\n    'TEAM-FT_PCT': '80', ...\n  },\n  'box_score': {\n    'PLAYER_NAME': {\n      '0': 'Harrison Barnes', ...\n  }, ...\n  'summary_en': ['The', 'Utah', 'Jazz', 'defeated', 'the', 'Dallas', 'Mavericks', ...],\n  'sentence_end_index_en': [16, 52, 100, 137, 177, 215, 241, 256, 288],\n  'summary_de': ['Die', 'Utah', 'Jazz', 'besiegten', 'am', 'Mittwoch', 'in', 'der', ...],\n  'sentence_end_index_de': [19, 57, 107, 134, 170, 203, 229, 239, 266],\n  'detok_summary_org': \"The Utah Jazz defeated the Dallas Mavericks 97 - 81 ...\",\n  'detok_summary': \"The Utah Jazz defeated the Dallas Mavericks 97-81 ...\",\n  'summary': ['The', 'Utah', 'Jazz', 'defeated', 'the', 'Dallas', 'Mavericks', ...],\n}",
+      "structure-example": "```\n{\n  'id': '11_02_16-Jazz-Mavericks-TheUtahJazzdefeatedthe',\n  'gem_id': 'GEM-RotoWire_English-German-train-0'\n  'day': '11_02_16',\n  'home_city': 'Utah',\n  'home_name': 'Jazz',\n  'vis_city': 'Dallas',\n  'vis_name': 'Mavericks',\n  'home_line': {\n    'TEAM-FT_PCT': '58', ...\n  },\n  'vis_line': {\n    'TEAM-FT_PCT': '80', ...\n  },\n  'box_score': {\n    'PLAYER_NAME': {\n      '0': 'Harrison Barnes', ...\n  }, ...\n  'summary_en': ['The', 'Utah', 'Jazz', 'defeated', 'the', 'Dallas', 'Mavericks', ...],\n  'sentence_end_index_en': [16, 52, 100, 137, 177, 215, 241, 256, 288],\n  'summary_de': ['Die', 'Utah', 'Jazz', 'besiegten', 'am', 'Mittwoch', 'in', 'der', ...],\n  'sentence_end_index_de': [19, 57, 107, 134, 170, 203, 229, 239, 266],\n  'detok_summary_org': \"The Utah Jazz defeated the Dallas Mavericks 97 - 81 ...\",\n  'detok_summary': \"The Utah Jazz defeated the Dallas Mavericks 97-81 ...\",\n  'summary': ['The', 'Utah', 'Jazz', 'defeated', 'the', 'Dallas', 'Mavericks', ...],\n}\n```",
       "structure-splits-criteria": "- English summaries are provided sentence-by-sentence to professional German translators with basketball knowledge to obtain sentence-level German translations.\n- Split criteria follows the original RotoWire dataset.",
       "structure-outlier": "- The (English) summary length in the training set varies from 145 to 650 words, with an average of 323 words."
+    },
+    "what": {
+      "dataset": "This dataset is a data-to-text dataset in the basketball domain. The input are tables in a fixed format with statistics about a game (in English) and the target is a German translation of the originally English description. The translations were done by professional translators with basketball experience. The dataset can be used to evaluate the cross-lingual data-to-text capabilities of a model with complex inputs. "
     }
   },
   "curation": {