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README.md

@@ -1,223 +0,0 @@
----
-annotations_creators:
-- expert-generated
-language_creators:
-- found
-license:
-- cc-by-4.0
-multilinguality:
-- ar
-- de
-- ja
-- hi
-- pt
-- en
-- es
-- it
-- fr
-size_categories:
-- 100K<n<1M
-source_datasets:
-- original
-task_categories:
-- question-answering
-task_ids:
-- open-domain-qa
-paperswithcode_id: mintaka
-pretty_name: Mintaka
-language_bcp47:
-- ar-SA
-- de-DE
-- ja-JP
-- hi-HI
-- pt-PT
-- en-EN
-- es-ES
-- it-IT
-- fr-FR
----
-
-# Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering
-
-## Table of Contents
-- [Dataset Description](#dataset-description)
-  - [Dataset Summary](#dataset-summary)
-  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
-  - [Languages](#languages)
-- [Dataset Structure](#dataset-structure)
-  - [Data Instances](#data-instances)
-  - [Data Fields](#data-fields)
-  - [Data Splits](#data-splits)
-- [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:** https://github.com/amazon-science/mintaka
-- **Repository:** https://github.com/amazon-science/mintaka
-- **Paper:** https://aclanthology.org/2022.coling-1.138/
-- **Point of Contact:** [GitHub](https://github.com/amazon-science/mintaka)
-
-### Dataset Summary
-
-Mintaka is a complex, natural, and multilingual question answering (QA) dataset composed of 20,000 question-answer pairs elicited from MTurk workers and annotated with Wikidata question and answer entities. Full details on the Mintaka dataset can be found in our paper: https://aclanthology.org/2022.coling-1.138/
-
-To build Mintaka, we explicitly collected questions in 8 complexity types, as well as generic questions:
-
-- Count (e.g., Q: How many astronauts have been elected to Congress? A: 4)
-- Comparative (e.g., Q: Is Mont Blanc taller than Mount Rainier? A: Yes)
-- Superlative (e.g., Q: Who was the youngest tribute in the Hunger Games? A: Rue)
-- Ordinal (e.g., Q: Who was the last Ptolemaic ruler of Egypt? A: Cleopatra)
-- Multi-hop (e.g., Q: Who was the quarterback of the team that won Super Bowl 50? A: Peyton Manning)
-- Intersection (e.g., Q: Which movie was directed by Denis Villeneuve and stars Timothee Chalamet? A: Dune)
-- Difference (e.g., Q: Which Mario Kart game did Yoshi not appear in? A: Mario Kart Live: Home Circuit)
-- Yes/No (e.g., Q: Has Lady Gaga ever made a song with Ariana Grande? A: Yes.)
-- Generic (e.g., Q: Where was Michael Phelps born? A: Baltimore, Maryland)
-- We collected questions about 8 categories: Movies, Music, Sports, Books, Geography, Politics, Video Games, and History
-
-Mintaka is one of the first large-scale complex, natural, and multilingual datasets that can be used for end-to-end question-answering models.
-
-### Supported Tasks and Leaderboards
-
-The dataset can be used to train a model for question answering.
-To ensure comparability, please refer to our evaluation script here: https://github.com/amazon-science/mintaka#evaluation
-
-### Languages
-
-All questions were written in English and translated into 8 additional languages: Arabic, French, German, Hindi, Italian, Japanese, Portuguese, and Spanish.
-
-## Dataset Structure
-
-### Data Instances
-
-An example of 'train' looks as follows.
-
-```json
-{
-  "id": "a9011ddf",
-  "lang": "en",
-  "question": "What is the seventh tallest mountain in North America?",
-  "answerText": "Mount Lucania",
-  "category": "geography",
-  "complexityType": "ordinal",
-  "questionEntity":
-  [
-      {
-          "name": "Q49",
-          "entityType": "entity",
-          "label": "North America",
-          "mention": "North America",
-          "span": [40, 53]
-      },
-      {
-          "name": 7,
-          "entityType": "ordinal",
-          "mention": "seventh",
-          "span": [12, 19]
-      }
-  ],
-  "answerEntity":
-  [
-      {
-          "name": "Q1153188",
-          "label": "Mount Lucania",
-      }
-  ],
-}
-```
-
-### Data Fields
-
-The data fields are the same among all splits.
-
-`id`: a unique ID for the given sample.
-
-`lang`: the language of the question. 
-
-`question`: the original question elicited in the corresponding language.
-
-`answerText`: the original answer text elicited in English.
-
-`category`: the category of the question. Options are: geography, movies, history, books, politics, music, videogames, or sports
-
-`complexityType`: the complexity type of the question. Options are: ordinal, intersection, count, superlative, yesno comparative, multihop, difference, or generic
-
-`questionEntity`:  a list of annotated question entities identified by crowd workers.
-```
-{
-     "name": The Wikidata Q-code or numerical value of the entity
-     "entityType": The type of the entity. Options are:
-             entity, cardinal, ordinal, date, time, percent, quantity, or money
-     "label": The label of the Wikidata Q-code
-     "mention": The entity as it appears in the English question text. Will be empty for non-English samples.
-     "span": The start and end characters of the mention in the English question text. Will be empty for non-English samples.
-}
-```
-`answerEntity`:  a list of annotated answer entities identified by crowd workers.
-```
-{
-     "name": The Wikidata Q-code or numerical value of the entity
-     "label": The label of the Wikidata Q-code
-}
-```
-
-### Data Splits
-
-For each language, we split into train (14,000 samples), dev (2,000 samples), and test (4,000 samples) sets.
-
-### Personal and Sensitive Information
-
-The corpora is free of personal or sensitive information.
-
-## Considerations for Using the Data
-### Social Impact of Dataset
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-### Discussion of Biases
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-### Other Known Limitations
-[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
-
-## Additional Information
-
-### Dataset Curators
-
-Amazon Alexa AI.
-
-### Licensing Information
-
-This project is licensed under the CC-BY-4.0 License.
-
-### Citation Information
-
-Please cite the following papers when using this dataset.
-
-```latex
-@inproceedings{sen-etal-2022-mintaka,
-    title = "Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering",
-    author = "Sen, Priyanka  and
-      Aji, Alham Fikri  and
-      Saffari, Amir",
-    booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
-    month = oct,
-    year = "2022",
-    address = "Gyeongju, Republic of Korea",
-    publisher = "International Committee on Computational Linguistics",
-    url = "https://aclanthology.org/2022.coling-1.138",
-    pages = "1604--1619"
-}
-```
-
-### Contributions
-
-Thanks to [@afaji](https://github.com/afaji) for adding this dataset.

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mintaka.py

@@ -1,177 +0,0 @@
-# coding=utf-8
-
-"""Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering"""
-
-import json
-import datasets
-
-logger = datasets.logging.get_logger(__name__)
-
-_DESCRIPTION = """\
-         Mintaka is a complex, natural, and multilingual dataset designed for experimenting with end-to-end
-         question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English,
-         annotated with Wikidata entities, and translated into Arabic, French, German, Hindi, Italian,
-         Japanese, Portuguese, and Spanish for a total of 180,000 samples.
-         Mintaka includes 8 types of complex questions, including superlative, intersection, and multi-hop questions, 
-         which were naturally elicited from crowd workers. 
-"""
-
-_CITATION = """\
-        @inproceedings{sen-etal-2022-mintaka,
-            title = "Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering",
-            author = "Sen, Priyanka and Aji, Alham Fikri and Saffari, Amir",
-            booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
-            month = oct,
-            year = "2022",
-            address = "Gyeongju, Republic of Korea",
-            publisher = "International Committee on Computational Linguistics",
-            url = "https://aclanthology.org/2022.coling-1.138",
-            pages = "1604--1619"
-        }
-"""
-
-_LICENSE = """\
-Copyright Amazon.com Inc. or its affiliates.
-Attribution 4.0 International
-"""
-
-_TRAIN_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_train.json"
-_DEV_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_dev.json"
-_TEST_URL = "https://raw.githubusercontent.com/amazon-science/mintaka/main/data/mintaka_test.json"
-
-
-_LANGUAGES = ['en', 'ar', 'de', 'ja', 'hi', 'pt', 'es', 'it', 'fr']
-
-_ALL = "all"
-
-class Mintaka(datasets.GeneratorBasedBuilder):
-    """Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering"""
-
-    BUILDER_CONFIGS = [
-        datasets.BuilderConfig(
-            name = name,
-            version = datasets.Version("1.0.0"),
-            description = f"Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering for {name}",
-        ) for name in _LANGUAGES
-    ]
-
-    BUILDER_CONFIGS.append(datasets.BuilderConfig(
-        name = _ALL,
-        version = datasets.Version("1.0.0"),
-        description = f"Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering",
-    ))
-
-    DEFAULT_CONFIG_NAME = 'en'
-
-    def _info(self):
-        return datasets.DatasetInfo(
-            description=_DESCRIPTION,
-            features=datasets.Features(
-                {
-                    "id": datasets.Value("string"),
-                    "lang": datasets.Value("string"),
-                    "question": datasets.Value("string"),
-                    "answerText": datasets.Value("string"),
-                    "category": datasets.Value("string"),
-                    "complexityType": datasets.Value("string"),
-                    "questionEntity": [{
-                        "name": datasets.Value("string"),
-                        "entityType": datasets.Value("string"),
-                        "label": datasets.Value("string"),
-                        "mention": datasets.Value("string"),
-                        "span": [datasets.Value("int32")],
-                    }],
-                    "answerEntity": [{
-                        "name": datasets.Value("string"),
-                        "label": datasets.Value("string"),
-                    }]
-                },
-            ),
-            supervised_keys=None,
-            citation=_CITATION,
-            license=_LICENSE,
-        )
-
-    def _split_generators(self, dl_manager):
-        return [
-            datasets.SplitGenerator(
-                name=datasets.Split.TRAIN,
-                gen_kwargs={
-                    "file": dl_manager.download_and_extract(_TRAIN_URL),
-                    "lang": self.config.name,
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.VALIDATION,
-                gen_kwargs={
-                    "file": dl_manager.download_and_extract(_DEV_URL),
-                    "lang": self.config.name,
-                }
-            ),
-            datasets.SplitGenerator(
-                name=datasets.Split.TEST,
-                gen_kwargs={
-                    "file": dl_manager.download_and_extract(_TEST_URL),
-                    "lang": self.config.name,
-                }
-            ),
-        ]
-
-    def _generate_examples(self, file, lang):
-        if lang == _ALL:
-            langs = _LANGUAGES
-        else:
-            langs = [lang]
-
-        key_ = 0
-
-        logger.info("⏳ Generating examples from = %s", ", ".join(lang))
-
-        with open(file, encoding='utf-8') as json_file:
-            data = json.load(json_file)
-            for lang in langs:
-                for sample in data:
-                    questionEntity = [
-                        {
-                            "name": str(qe["name"]),
-                            "entityType": qe["entityType"],
-                            "label": qe["label"] if "label" in qe else "",
-                            # span only applies for English question
-                            "mention": qe["mention"] if lang == "en" else None,
-                            "span": qe["span"] if lang == "en" else [],
-                        } for qe in sample["questionEntity"]
-                    ]
-
-                    answers = []
-                    if sample['answer']["answerType"] == "entity" and sample['answer']['answer'] is not None:
-                        answers = sample['answer']['answer']
-                    elif sample['answer']["answerType"] == "numerical" and "supportingEnt" in sample["answer"]:
-                        answers = sample['answer']['supportingEnt']
-
-                    # helper to get language for the corresponding language
-                    def get_label(labels, lang):
-                        if lang in labels:
-                            return labels[lang]
-                        if 'en' in labels:
-                            return labels['en']
-                        return None
-
-                    answerEntity = [
-                        {
-                            "name": str(ae["name"]),
-                            "label": get_label(ae["label"], lang),
-                        } for ae in answers
-                    ]
-
-                    yield key_, {
-                        "id": sample["id"],
-                        "lang": lang,
-                        "question": sample["question"] if lang == 'en' else sample['translations'][lang],
-                        "answerText": sample["answer"]["mention"],
-                        "category": sample["category"],
-                        "complexityType": sample["complexityType"],
-                        "questionEntity": questionEntity,
-                        "answerEntity": answerEntity,
-                    }
-
-                    key_ += 1

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test_mintaka.py

@@ -1,16 +0,0 @@
-from datasets import load_dataset
-
-source = "AmazonScience/mintaka"
-
-#dataset = load_dataset(source, "all", download_mode="force_redownload")
-dataset = load_dataset(source, "all")
-
-print(dataset)
-print(dataset["train"][0])
-print(dataset["train"][0:10]['question'])
-
-
-dataset = load_dataset(source, "en")
-dataset = load_dataset(source, "ar")
-
-