📄 Extractive¶

MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage. We aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents… See the full description on the dataset page: https://huggingface.co/datasets/multidoc2dial

Tags: annotations_creators:crowdsourced, arxiv:2109.12595, language:en, language_creators:['crowdsourced', 'expert-generated'], license:apache-2.0, multilinguality:monolingual, region:us, size_categories:['10K<n<100K', '1K<n<10K', 'n<1K'], source_datasets:extended|doc2dial, task_categories:question-answering, task_ids:open-domain-qa

Note

ID: cards.multidoc2dial.extractive | Type: TaskCard

{
    "__description__": "MultiDoc2Dial is a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents. Most previous works treat document-grounded dialogue modeling as a machine reading comprehension task based on a single given document or passage. We aim to address more realistic scenarios where a goal-oriented information-seeking conversation involves multiple topics, and hence is grounded on different documents… See the full description on the dataset page: https://huggingface.co/datasets/multidoc2dial",
    "__tags__": {
        "annotations_creators": "crowdsourced",
        "arxiv": "2109.12595",
        "language": "en",
        "language_creators": [
            "crowdsourced",
            "expert-generated"
        ],
        "license": "apache-2.0",
        "multilinguality": "monolingual",
        "region": "us",
        "size_categories": [
            "10K<n<100K",
            "1K<n<10K",
            "n<1K"
        ],
        "source_datasets": "extended|doc2dial",
        "task_categories": "question-answering",
        "task_ids": "open-domain-qa"
    },
    "__type__": "task_card",
    "loader": {
        "__type__": "load_hf",
        "path": "multidoc2dial"
    },
    "preprocess_steps": [
        {
            "__type__": "rename_fields",
            "field_to_field": {
                "answers/text/0": "relevant_context"
            }
        },
        {
            "__type__": "list_field_values",
            "fields": [
                "relevant_context"
            ],
            "to_field": "answers"
        },
        {
            "__type__": "execute_expression",
            "expression": "question.split('[SEP]')[0]",
            "to_field": "question"
        },
        {
            "__type__": "set",
            "fields": {
                "context_type": "document"
            }
        }
    ],
    "task": "tasks.qa.with_context.extractive",
    "templates": "templates.qa.with_context.all"
}

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 LoadHF¶

Loads datasets from the Huggingface Hub.

It supports loading with or without streaming, and can filter datasets upon loading.
Args:
path: The path or identifier of the dataset on the Huggingface Hub. name: An optional dataset name. data_dir: Optional directory to store downloaded data. split: Optional specification of which split to load. data_files: Optional specification of particular data files to load. streaming: Bool indicating if streaming should be used. filtering_lambda: A lambda function for filtering the data after loading. num_proc: Optional integer to specify the number of processes to use for parallel dataset loading.

Example:
Loading glue’s mrpc dataset
load_hf = LoadHF(path='glue', name='mrpc')

Explanation about ListFieldValues¶

Concatenates values of multiple fields into a list, and assigns it to a new field.

Explanation about ExecuteExpression¶

Compute an expression, specified as a string to be eval-uated, over the instance’s fields, and store the result in field to_field.

Raises an error if a field mentioned in the query is missing from the instance.

Args:
expression (str): an expression to be evaluated over the fields of the instance to_field (str): the field where the result is to be stored into imports_list (List[str]): names of imports needed for the eval of the query (e.g. ‘re’, ‘json’)

Examples:
When instance {“a”: 2, “b”: 3} is process-ed by operator ExecuteExpression(expression=”a+b”, to_field = “c”) the result is {“a”: 2, “b”: 3, “c”: 5}

When instance {“a”: “hello”, “b”: “world”} is process-ed by operator ExecuteExpression(expression = “a+’ ‘+b”, to_field = “c”) the result is {“a”: “hello”, “b”: “world”, “c”: “hello world”}

Explanation about Set¶

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 Set(fields={“classes”: [“positive”,”negatives”]})

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

# Add a ‘classes’ field with a value of a list “positive” and “negative” to ‘train’ stream Set(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. Set(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}}]

References: templates.qa.with_context.all, tasks.qa.with_context.extractive