📄 Dart¶

DART is a large and open-domain structured DAta Record to Text generation corpus with high-quality sentence annotations with each input being a set of entity-relation triples following a tree-structured ontology. It consists of 82191 examples across different domains with each input being a semantic RDF triple set derived from data records in tables and the tree ontology of table schema, annotated with sentence description that covers all facts in the triple set. DART is released in the following paper where you can find more details and baseline results: https://arxiv.org/abs/2007.02871

Tags: annotations_creators:['crowdsourced', 'machine-generated'], arxiv:2007.02871, croissant:True, language:en, language_creators:['crowdsourced', 'machine-generated'], license:mit, multilinguality:monolingual, region:us, size_categories:10K<n<100K, source_datasets:['extended|wikitable_questions', 'extended|wikisql', 'extended|web_nlg', 'extended|cleaned_e2e'], task_categories:tabular-to-text, task_ids:rdf-to-text

Note

ID: cards.dart | Type: TaskCard

{
    "__description__": "DART is a large and open-domain structured DAta Record to Text generation corpus with high-quality\nsentence annotations with each input being a set of entity-relation triples following a tree-structured ontology.\nIt consists of 82191 examples across different domains with each input being a semantic RDF triple set derived\nfrom data records in tables and the tree ontology of table schema, annotated with sentence description that\ncovers all facts in the triple set.\nDART is released in the following paper where you can find more details and baseline results:\nhttps://arxiv.org/abs/2007.02871",
    "__tags__": {
        "annotations_creators": [
            "crowdsourced",
            "machine-generated"
        ],
        "arxiv": "2007.02871",
        "croissant": true,
        "language": "en",
        "language_creators": [
            "crowdsourced",
            "machine-generated"
        ],
        "license": "mit",
        "multilinguality": "monolingual",
        "region": "us",
        "size_categories": "10K<n<100K",
        "source_datasets": [
            "extended|wikitable_questions",
            "extended|wikisql",
            "extended|web_nlg",
            "extended|cleaned_e2e"
        ],
        "task_categories": "tabular-to-text",
        "task_ids": "rdf-to-text"
    },
    "loader": {
        "path": "dart",
        "type": "load_hf"
    },
    "preprocess_steps": [
        "splitters.small_no_test",
        {
            "field_to_field": [
                [
                    "tripleset",
                    "serialized_triples"
                ]
            ],
            "type": "serialize_triples"
        },
        {
            "field_to_field": {
                "serialized_triples": "input"
            },
            "type": "rename_fields"
        },
        {
            "field_to_field": {
                "annotations/text/0": "output"
            },
            "type": "copy_fields"
        },
        {
            "fields": {
                "type_of_input": "Triples",
                "type_of_output": "Text"
            },
            "type": "add_fields"
        }
    ],
    "task": "tasks.generation",
    "templates": "templates.generation.all",
    "type": "task_card"
}

Explanation about TaskCard¶

TaskCard delineates the phases in transforming the source dataset into a model-input, and specifies the metrics for evaluation of model-output.

Attributes:
loader: specifies the source address and the loading operator that can access that source and transform it into a unitxt multistream.

preprocess_steps: list of unitxt operators to process the data source into a model-input.

task: specifies the fields (of the already (pre)processed instance) making the inputs, the fields making the outputs, and the metrics to be used for evaluating the model output.

templates: format strings to be applied on the input fields (specified by the task) and the output fields. The template also carries the instructions and the list of postprocessing steps, to be applied to the model output.

Explanation about CopyFields¶

Copies values from specified fields to specified fields.

Args (of parent class):
field_to_field (Union[List[List], Dict[str, str]]): A list of lists, where each sublist contains the source field and the destination field, or a dictionary mapping source fields to destination fields.

Examples:
An input instance {“a”: 2, “b”: 3}, when processed by CopyField(field_to_field={“a”: “b”} would yield {“a”: 2, “b”: 2}, and when processed by CopyField(field_to_field={“a”: “c”} would yield {“a”: 2, “b”: 3, “c”: 2}

with field names containing / , we can also copy inside the field: CopyFields(field_to_field={“a/0”: “a”}) would process instance {“a”: [1, 3]} into {“a”: 1}

Explanation about AddFields¶

Adds specified fields to each instance in a given stream or all streams (default) If fields exist, updates them.

Args:

fields (Dict[str, object]): The fields to add to each instance.
Use ‘/’ to access inner fields

use_deepcopy (bool) : Deep copy the input value to avoid later modifications

Examples:
# Add a ‘classes’ field with a value of a list “positive” and “negative” to all streams AddFields(fields={“classes”: [“positive”,”negatives”]})

# Add a ‘start’ field under the ‘span’ field with a value of 0 to all streams AddFields(fields={“span/start”: 0}

# Add a ‘classes’ field with a value of a list “positive” and “negative” to ‘train’ stream AddFields(fields={“classes”: [“positive”,”negatives”], apply_to_stream=[“train”]})

# Add a ‘classes’ field on a given list, prevent modification of original list # from changing the instance. AddFields(fields={“classes”: alist}), use_deepcopy=True) # if now alist is modified, still the instances remain intact.

Explanation about RenameFields¶

Renames fields.

Move value from one field to another, potentially, if field name contains a /, from one branch into another. Remove the from field, potentially part of it in case of / in from_field.

Examples:
RenameFields(field_to_field={“b”: “c”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “c”: 2}, {“a”: 2, “c”: 3}]

RenameFields(field_to_field={“b”: “c/d”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “c”: {“d”: 2}}, {“a”: 2, “c”: {“d”: 3}}]

RenameFields(field_to_field={“b”: “b/d”}) will change inputs [{“a”: 1, “b”: 2}, {“a”: 2, “b”: 3}] to [{“a”: 1, “b”: {“d”: 2}}, {“a”: 2, “b”: {“d”: 3}}]

RenameFields(field_to_field={“b/c/e”: “b/d”}) will change inputs [{“a”: 1, “b”: {“c”: {“e”: 2, “f”: 20}}}] to [{“a”: 1, “b”: {“c”: {“f”: 20}, “d”: 2}}]

Explanation about SerializeTriples¶

Serializes triples into a flat sequence.

Sample input in expected format: [[ “First Clearing”, “LOCATION”, “On NYS 52 1 Mi. Youngsville” ], [ “On NYS 52 1 Mi. Youngsville”, “CITY_OR_TOWN”, “Callicoon, New York”]]

Sample output: First Clearing : LOCATION : On NYS 52 1 Mi. Youngsville | On NYS 52 1 Mi. Youngsville : CITY_OR_TOWN : Callicoon, New York

References: splitters.small_no_test, templates.generation.all, tasks.generation