Datasets:

pchristm
/

conv_questions

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README.md DELETED Viewed

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----
-annotations_creators:
-- crowdsourced
-language_creators:
-- crowdsourced
-language:
-- en
-language_bcp47:
-- en-US
-license:
-- cc-by-4.0
-multilinguality:
-- monolingual
-size_categories:
-- 10K<n<100K
-source_datasets:
-- original
-task_categories:
-- question-answering
-- text-generation
-- fill-mask
-task_ids:
-- open-domain-qa
-- dialogue-modeling
-pretty_name: ConvQuestions
-dataset_info:
-  features:
-  - name: domain
-    dtype: string
-  - name: seed_entity
-    dtype: string
-  - name: seed_entity_text
-    dtype: string
-  - name: questions
-    sequence: string
-  - name: answers
-    sequence:
-      sequence: string
-  - name: answer_texts
-    sequence: string
-  splits:
-  - name: train
-    num_bytes: 3589880
-    num_examples: 6720
-  - name: validation
-    num_bytes: 1241778
-    num_examples: 2240
-  - name: test
-    num_bytes: 1175656
-    num_examples: 2240
-  download_size: 3276017
-  dataset_size: 6007314
----
-# Dataset Card for ConvQuestions
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-instances)
-  - [Data Splits](#data-instances)
-- [Dataset Creation](#dataset-creation)
-  - [Curation Rationale](#curation-rationale)
-  - [Source Data](#source-data)
-  - [Annotations](#annotations)
-  - [Personal and Sensitive Information](#personal-and-sensitive-information)
-- [Considerations for Using the Data](#considerations-for-using-the-data)
-  - [Social Impact of Dataset](#social-impact-of-dataset)
-  - [Discussion of Biases](#discussion-of-biases)
-  - [Other Known Limitations](#other-known-limitations)
-- [Additional Information](#additional-information)
-  - [Dataset Curators](#dataset-curators)
-  - [Licensing Information](#licensing-information)
-  - [Citation Information](#citation-information)
-  - [Contributions](#contributions)
-## Dataset Description
-- **Homepage:** [ConvQuestions page](https://convex.mpi-inf.mpg.de)
-- **Repository:** [GitHub](https://github.com/PhilippChr/CONVEX)
-- **Paper:** [Look before you hop: Conversational question answering over knowledge graphs using judicious context expansion](https://arxiv.org/abs/1910.03262)
-- **Leaderboard:** [Needs More Information]
-- **Point of Contact:** [Philipp Christmann](mailto:pchristm@mpi-inf.mpg.de)
-### Dataset Summary
-ConvQuestions is the first realistic benchmark for conversational question answering over
-knowledge graphs. It contains 11,200 conversations which can be evaluated over Wikidata.
-They are compiled from the inputs of 70 Master crowdworkers on Amazon Mechanical Turk,
-with conversations from five domains: Books, Movies, Soccer, Music, and TV Series.
-The questions feature a variety of complex question phenomena like comparisons, aggregations,
-compositionality, and temporal reasoning. Answers are grounded in Wikidata entities to enable
-fair comparison across diverse methods. The data gathering setup was kept as natural as
-possible, with the annotators selecting entities of their choice from each of the five domains,
-and formulating the entire conversation in one session. All questions in a conversation are
-from the same Turker, who also provided gold answers to the questions. For suitability to knowledge
-graphs, questions were constrained to be objective or factoid in nature, but no other restrictive
-guidelines were set. A notable property of ConvQuestions is that several questions are not
-answerable by Wikidata alone (as of September 2019), but the required facts can, for example,
-be found in the open Web or in Wikipedia. For details, please refer to the CIKM 2019 full paper
-(https://dl.acm.org/citation.cfm?id=3358016).
-### Supported Tasks and Leaderboards
-[Needs More Information]
-### Languages
-en
-## Dataset Structure
-### Data Instances
-An example of 'train' looks as follows.
-```
-{
-  'domain': 'music',
-  'seed_entity': 'https://www.wikidata.org/wiki/Q223495',
-  'seed_entity_text': 'The Carpenters',
-  'questions': [
-    'When did The Carpenters sign with A&M Records?',
-    'What song was their first hit?',
-    'When did Karen die?',
-    'Karen had what eating problem?',
-    'and how did she die?'
-  ],
-  'answers': [
-    [
-      '1969'
-    ],
-    [
-      'https://www.wikidata.org/wiki/Q928282'
-    ],
-    [
-      '1983'
-    ],
-    [
-      'https://www.wikidata.org/wiki/Q131749'
-    ],
-    [
-      'https://www.wikidata.org/wiki/Q181754'
-    ]
-  ],
-  'answer_texts': [
-    '1969',
-    '(They Long to Be) Close to You',
-    '1983',
-    'anorexia nervosa',
-    'heart failure'
-  ]
-}
-```
-### Data Fields
-- `domain`: a `string` feature. Any of: ['books', 'movies', 'music', 'soccer', 'tv_series']
-- `seed_entity`: a `string` feature. Wikidata ID of the topic entity.
-- `seed_entity_text`: a `string` feature. Surface form of the topic entity.
-- `questions`: a `list` of `string` features. List of questions (initial question and follow-up questions).
-- `answers`: a `list` of `lists` of `string` features. List of answers, given as Wikidata IDs or literals (e.g. timestamps or names).
-- `answer_texts`: a `list` of `string` features. List of surface forms of the answers.
-### Data Splits
-|train|validation|tests|
-|----:|---------:|----:|
-| 6720|      2240| 2240|
-## Dataset Creation
-### Curation Rationale
-[Needs More Information]
-### Source Data
-#### Initial Data Collection and Normalization
-[Needs More Information]
-#### Who are the source language producers?
-[Needs More Information]
-### Annotations
-#### Annotation process
-With insights from a meticulous in-house pilot study with ten students over two weeks, the authors posed the conversation generation task on Amazon Mechanical Turk (AMT) in the most natural setup: Each crowdworker was asked to build a conversation by asking five sequential questions starting from any seed entity of his/her choice, as this is an intuitive mental model that humans may have when satisfying their real information needs via their search assistants.
-#### Who are the annotators?
-Local students (Saarland Informatics Campus) and AMT Master Workers.
-### Personal and Sensitive Information
-[Needs More Information]
-## Considerations for Using the Data
-### Social Impact of Dataset
-[Needs More Information]
-### Discussion of Biases
-[Needs More Information]
-### Other Known Limitations
-[Needs More Information]
-## Additional Information
-### Dataset Curators
-[Needs More Information]
-### Licensing Information
-The ConvQuestions benchmark is licensed under a Creative Commons Attribution 4.0 International License.
-### Citation Information
-```
-@InProceedings{christmann2019look,
-  title={Look before you hop: Conversational question answering over knowledge graphs using judicious context expansion},
-  author={Christmann, Philipp and Saha Roy, Rishiraj and Abujabal, Abdalghani and Singh, Jyotsna and Weikum, Gerhard},
-  booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
-  pages={729--738},
-  year={2019}
-}
-```
-### Contributions
-Thanks to [@PhilippChr](https://github.com/PhilippChr) for adding this dataset.

conv_questions.py DELETED Viewed

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-# coding=utf-8
-# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""
-ConvQuestions is the first realistic benchmark for conversational question answering over
-knowledge graphs. It contains 11,200 conversations which can be evaluated over Wikidata.
-They are compiled from the inputs of 70 Master crowdworkers on Amazon Mechanical Turk,
-with conversations from five domains: Books, Movies, Soccer, Music, and TV Series.
-The questions feature a variety of complex question phenomena like comparisons, aggregations,
-compositionality, and temporal reasoning. Answers are grounded in Wikidata entities to enable
-fair comparison across diverse methods. The data gathering setup was kept as natural as
-possible, with the annotators selecting entities of their choice from each of the five domains,
-and formulating the entire conversation in one session. All questions in a conversation are
-from the same Turker, who also provided gold answers to the questions. For suitability to knowledge
-graphs, questions were constrained to be objective or factoid in nature, but no other restrictive
-guidelines were set. A notable property of ConvQuestions is that several questions are not
-answerable by Wikidata alone (as of September 2019), but the required facts can, for example,
-be found in the open Web or in Wikipedia. For details, please refer to our CIKM 2019 full paper
-(https://dl.acm.org/citation.cfm?id=3358016).
-"""
-import json
-import os
-import datasets
-# Find for instance the citation on arxiv or on the dataset repo/website
-_CITATION = """\
-@InProceedings{christmann2019look,
-  title={Look before you hop: Conversational question answering over knowledge graphs using judicious context expansion},
-  author={Christmann, Philipp and Saha Roy, Rishiraj and Abujabal, Abdalghani and Singh, Jyotsna and Weikum, Gerhard},
-  booktitle={Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
-  pages={729--738},
-  year={2019}
-}
-"""
-# You can copy an official description
-_DESCRIPTION = """\
-ConvQuestions is the first realistic benchmark for conversational question answering over knowledge graphs.
-It contains 11,200 conversations which can be evaluated over Wikidata. The questions feature a variety of complex
-question phenomena like comparisons, aggregations, compositionality, and temporal reasoning."""
-_HOMEPAGE = "https://convex.mpi-inf.mpg.de"
-_LICENSE = "CC BY 4.0"
-# The HuggingFace dataset library don't host the datasets but only point to the original files
-# This can be an arbitrary nested dict/list of URLs (see below in `_split_generators` method)
-_URL = "http://qa.mpi-inf.mpg.de/convex/"
-_URLs = {
-    "train": _URL + "ConvQuestions_train.zip",
-    "dev": _URL + "ConvQuestions_dev.zip",
-    "test": _URL + "ConvQuestions_test.zip",
-}
-class ConvQuestions(datasets.GeneratorBasedBuilder):
-    """ConvQuestions is a realistic benchmark for conversational question answering over knowledge graphs."""
-    VERSION = datasets.Version("1.0.0")
-    def _info(self):
-        # This method specifies the datasets.DatasetInfo object which contains informations and typings for the dataset
-        features = datasets.Features(
-            {
-                "domain": datasets.Value("string"),
-                "seed_entity": datasets.Value("string"),
-                "seed_entity_text": datasets.Value("string"),
-                "questions": datasets.features.Sequence(datasets.Value("string")),
-                "answers": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
-                "answer_texts": datasets.features.Sequence(datasets.Value("string")),
-            }
-        )
-        return datasets.DatasetInfo(
-            # This is the description that will appear on the datasets page.
-            description=_DESCRIPTION,
-            # This defines the different columns of the dataset and their types
-            features=features,  # Here we define them above because they are different between the two configurations
-            # If there's a common (input, target) tuple from the features,
-            # specify them here. They'll be used if as_supervised=True in
-            # builder.as_dataset.
-            supervised_keys=None,
-            # Homepage of the dataset for documentation
-            homepage=_HOMEPAGE,
-            # License for the dataset if available
-            license=_LICENSE,
-            # Citation for the dataset
-            citation=_CITATION,
-        )
-    def _split_generators(self, dl_manager):
-        """Returns SplitGenerators."""
-        # This method is tasked with downloading/extracting the data and defining the splits depending on the configuration
-        # If several configurations are possible (listed in BUILDER_CONFIGS), the configuration selected by the user is in self.config.name
-        # dl_manager is a datasets.download.DownloadManager that can be used to download and extract URLs
-        # It can accept any type or nested list/dict and will give back the same structure with the url replaced with path to local files.
-        # By default the archives will be extracted and a path to a cached folder where they are extracted is returned instead of the archive
-        data_dir = dl_manager.download_and_extract(_URLs)
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir["train"], "train_set/train_set_ALL.json"),
-                    "split": "train",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={
-                    "filepath": os.path.join(data_dir["dev"], "dev_set/dev_set_ALL.json"),
-                    "split": "dev",
-                },
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                # These kwargs will be passed to _generate_examples
-                gen_kwargs={"filepath": os.path.join(data_dir["test"], "test_set/test_set_ALL.json"), "split": "test"},
-            ),
-        ]
-    def _generate_examples(
-        self, filepath, split  # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    ):
-        """Yields examples as (key, example) tuples."""
-        # This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
-        # The `key` is here for legacy reason (tfds) and is not important in itself.
-        with open(filepath, encoding="utf-8") as f:
-            data = json.load(f)
-            for id_, instance in enumerate(data):
-                yield id_, {
-                    "domain": instance["domain"],
-                    "seed_entity": instance["seed_entity"],
-                    "seed_entity_text": instance["seed_entity_text"],
-                    "questions": [turn["question"] for turn in instance["questions"]],
-                    "answers": [turn["answer"].split(";") for turn in instance["questions"]],
-                    "answer_texts": [turn["answer_text"] for turn in instance["questions"]],
-                }

dataset_infos.json DELETED Viewed

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