datacard

turku_paraphrase_corpus.json

@@ -34,7 +34,7 @@
       "gem-added-by": "Jenna Kanerva, Filip Ginter (TurkuNLP / University of Turku)"
     },
     "structure": {
-      "data-fields": "The dataset consist of pairs of text passages, where a typical passage is about a sentence long, however, a passage may also be longer or shorter than a sentence. Thus, each example include two text passages (string), a manually annotated label to indicate the paraphrase type (string), and additional metadata.\n\nThe dataset include three different `modes`, plain, classification, and generation. The `plain` mode loads the original data without any additional preprocessing or transformations, while the `classification` mode directly builds the data in a form suitable for training a paraphrase classifier, where each example is doubled in the data with different directions (text1, text2, label) --> (text2, text1, label) taking care of the label flipping as well if needed (paraphrases with directionality flag < or >). In the `generation` mode, the examples are preprocessed to be directly suitable for paraphrase generation task. In here, paraphrases not suitable for generation are discarded (negative, and highly context-dependent paraphrases), and directional paraphrases are provided so that the generation goes from more detailed passage to the more general one in order to prevent model hallucination (i.e. model learning to introduce new information). The rest of the paraphrases are provided in both directions (text1, text2, label) --> (text2, text1, label).\n\n\nEach pair in `plain` and `classification` mode will include fields:\n\n`gem_id`: Identifier of the paraphrase pair (string)\n`goeswith`: Identifier of the document from which the paraphrase was extracted, can be `not available` in case the source of the paraphrase is not from document-structured data (string)\n`fold`: 0-99, data split into 100 parts respecting document boundaries, you can use this e.g. to implement crossvalidation safely as all paraphrases from one document are in one fold (int)\n`text1`: First paraphrase passage (string)\n`text2`: Second paraphrase passage (string)\n`label`: Manually annotated labels (string)\n`binary_label`: Label turned into binary with values `positive` (paraphrase) and `negative` (not-paraphrase) (string)\n`is_rewrite`: Indicator whether the example is human produced rewrite or naturally occurring paraphrase (bool)\n\nEach pair in `generation` mode will include the same fields expect `text1` and `text2` are renamed to `input` and `output` in order to indicate the generation direction. Thus the fields are:\n\n`gem_id`: Identifier of the paraphrase pair (string)\n`goeswith`: Identifier of the document from which the paraphrase was extracted, can be `not available` in case the source of the paraphrase is not from document-structured data (string)\n`fold`: 0-99, data split into 100 parts respecting document boundaries, you can use this e.g. to implement crossvalidation safely as all paraphrases from one document are in one fold (int)\n`input`: The input paraphrase passage for generation (string)\n`output`: The output paraphrase passage for generation (string)\n`label`: Manually annotated labels (string)\n`binary_label`: Label turned into binary with values `positive` (paraphrase) and `negative` (not-paraphrase) (string)\n`is_rewrite`: Indicator whether the example is human produced rewrite or naturally occurring paraphrase (bool)",
+      "data-fields": "The dataset consist of pairs of text passages, where a typical passage is about a sentence long, however, a passage may also be longer or shorter than a sentence. Thus, each example include two text passages (string), a manually annotated label to indicate the paraphrase type (string), and additional metadata.\n\nThe dataset include three different `modes`, plain, classification, and generation. The `plain` mode loads the original data without any additional preprocessing or transformations, while the `classification` mode directly builds the data in a form suitable for training a paraphrase classifier, where each example is doubled in the data with different directions (text1, text2, label) --> (text2, text1, label) taking care of the label flipping as well if needed (paraphrases with directionality flag < or >). In the `generation` mode, the examples are preprocessed to be directly suitable for paraphrase generation task. In here, paraphrases not suitable for generation are discarded (negative, and highly context-dependent paraphrases), and directional paraphrases are provided so that the generation goes from more detailed passage to the more general one in order to prevent model hallucination (i.e. model learning to introduce new information). The rest of the paraphrases are provided in both directions (text1, text2, label) --> (text2, text1, label).\n\nUsage:\n\n```\nfrom datasets import load_dataset\ndataset = load_dataset('turku_paraphrase_corpus.py', 'plain')\ndataset = load_dataset('turku_paraphrase_corpus.py', 'classification')\ndataset = load_dataset('turku_paraphrase_corpus.py', 'generation')\n```\n\n\nEach pair in `plain` and `classification` mode will include fields:\n\n`gem_id`: Identifier of the paraphrase pair (string)\n\n`goeswith`: Identifier of the document from which the paraphrase was extracted, can be `not available` in case the source of the paraphrase is not from document-structured data (string)\n\n`fold`: 0-99, data split into 100 parts respecting document boundaries, you can use this e.g. to implement crossvalidation safely as all paraphrases from one document are in one fold (int)\n\n`text1`: First paraphrase passage (string)\n\n`text2`: Second paraphrase passage (string)\n\n`target`: Classification target, same as label (string)\n\n`references`: References, not available in classification (list)\n\n`label`: Manually annotated label, same as target (string)\n\n`binary_label`: Label turned into binary with values `positive` (paraphrase) and `negative` (not-paraphrase) (string)\n\n`is_rewrite`: Indicator whether the example is human produced rewrite or naturally occurring paraphrase (bool)\n\n\n\n\nEach pair in `generation` mode will include the fields:\n\n\n`gem_id`: Identifier of the paraphrase pair (string)\n\n`goeswith`: Identifier of the document from which the paraphrase was extracted, can be `not available` in case the source of the paraphrase is not from document-structured data (string)\n\n`fold`: 0-99, data split into 100 parts respecting document boundaries, you can use this e.g. to implement crossvalidation safely as all paraphrases from one document are in one fold (int)\n\n`input`: The input paraphrase passage for generation (string)\n\n`target`: The generation target (string)\n\n`references`: The list of generation references (list)\n\n`label`: Manually annotated label (string)\n\n`binary_label`: Label turned into binary with values `positive` (paraphrase) and `negative` (not-paraphrase) (string)\n\n`is_rewrite`: Indicator whether the example is human produced rewrite or naturally occurring paraphrase (bool)\n",
       "structure-example": "{\n  'gem_id':  'gem-turku_paraphrase_corpus-train-15',\n  'goeswith': 'episode-02243',\n  'fold': 0,\n  'text1': 'Mit\u00e4 merkityst\u00e4 sill\u00e4 on?',\n  'text2': 'Mit\u00e4 v\u00e4li\u00e4 sill\u00e4 edes on?',\n  'label': '4',\n  'binary_label': 'positive',\n  'is_rewrite': False\n}",
       "structure-splits": "The corpus include 3 splits: train, validation, and test.",
       "structure-splits-criteria": "The data is split randomly into the three section with a restriction of all paraphrases from the same document (movie, TV episode, news article, student translation, or exam question) being in the same section. All splits are manually annotated."
@@ -164,4 +164,4 @@
       "bias-analyses": "N/A"
     }
   }
-}
+}