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Update files from the datasets library (from 1.2.0)

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Release notes: https://github.com/huggingface/datasets/releases/tag/1.2.0

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README.md ADDED
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+ ---
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+ annotations_creators:
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+ - crowdsourced
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+ language_creators:
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+ - expert-generated
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+ - found
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+ languages:
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+ - ar
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+ - bn
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+ - fi
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+ - ja
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+ - ko
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+ - ru
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+ - te
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+ licenses:
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+ - mit
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+ multilinguality:
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+ - multilingual
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+ size_categories:
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+ - 10K<n<100K
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+ source_datasets:
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+ - original
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+ - extended|tydiqa
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+ task_categories:
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+ - question-answering
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+ task_ids:
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+ - open-domain-qa
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+ ---
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+
30
+ # Dataset Card for XOR QA
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+
32
+ ## Table of Contents
33
+ - [Dataset Description](#dataset-description)
34
+ - [Dataset Summary](#dataset-summary)
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+ - [Supported Tasks](#supported-tasks-and-leaderboards)
36
+ - [Languages](#languages)
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+ - [Dataset Structure](#dataset-structure)
38
+ - [Data Instances](#data-instances)
39
+ - [Data Fields](#data-instances)
40
+ - [Data Splits](#data-instances)
41
+ - [Dataset Creation](#dataset-creation)
42
+ - [Curation Rationale](#curation-rationale)
43
+ - [Source Data](#source-data)
44
+ - [Annotations](#annotations)
45
+ - [Personal and Sensitive Information](#personal-and-sensitive-information)
46
+ - [Considerations for Using the Data](#considerations-for-using-the-data)
47
+ - [Social Impact of Dataset](#social-impact-of-dataset)
48
+ - [Discussion of Biases](#discussion-of-biases)
49
+ - [Other Known Limitations](#other-known-limitations)
50
+ - [Additional Information](#additional-information)
51
+ - [Dataset Curators](#dataset-curators)
52
+ - [Licensing Information](#licensing-information)
53
+ - [Citation Information](#citation-information)
54
+
55
+ ## Dataset Description
56
+
57
+ - **Homepage:** [XOR QA Homepage](https://nlp.cs.washington.edu/xorqa/)
58
+ - **Repository:** [XOR QA Repository](https://github.com/AkariAsai/XORQA)
59
+ - **Paper:** [XOR QA Paper](https://arxiv.org/abs/2010.11856)
60
+ - **Leaderboard:** [XOR QA Leaderboard](https://nlp.cs.washington.edu/xorqa/)
61
+ - **Point of Contact:** [Akari Asai](akari@cs.washington.edu)
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+
63
+ ### Dataset Summary
64
+
65
+ XOR-TyDi QA brings together for the first time information-seeking questions, open-retrieval QA, and multilingual QA to create a multilingual open-retrieval QA dataset that enables cross-lingual answer retrieval. It consists of questions written by information-seeking native speakers in 7 typologically diverse languages and answer annotations that are retrieved from multilingual document collections.
66
+
67
+ ### Supported Tasks and Leaderboards
68
+
69
+ There are three sub-tasks: XOR-Retrieve, XOR-EnglishSpan, and XOR-Full.
70
+
71
+ - `XOR-retrieve`: XOR-Retrieve is a cross-lingual retrieval task where a question is written in a target language (e.g., Japanese) and a system is required to retrieve English paragraphs that answer the question. The dataset can be used to train a model for cross-lingual retrieval. Success on this task is typically measured by R@5kt, R@2kt (the recall by computing the fraction of the questions for which the minimal answer is contained in the top 5,000 / 2,000 tokens selected). This task has an active leaderboard which can be found at [leaderboard url](https://nlp.cs.washington.edu/xorqa/)
72
+
73
+ - `XOR-English Span`: XOR-English Span is a cross-lingual retrieval task where a question is written in a target language (e.g., Japanese) and a system is required to output a short answer in English. The dataset can be used to train a model for cross-lingual retrieval. Success on this task is typically measured by F1, EM. This task has an active leaderboard which can be found at [leaderboard url](https://nlp.cs.washington.edu/xorqa/)
74
+
75
+ - `XOR-Full`: XOR-Full is a cross-lingual retrieval task where a question is written in the target language (e.g., Japanese) and a system is required to output a short answer in a target language. Success on this task is typically measured by F1, EM, BLEU This task has an active leaderboard which can be found at [leaderboard url](https://nlp.cs.washington.edu/xorqa/)
76
+ ### Languages
77
+
78
+ The text in the dataset is available in 7 languages: Arabic `ar`, Bengali `bn`, Finnish `fi`, Japanese `ja`, Korean `ko`, Russian `ru`, Telugu `te`
79
+
80
+ ## Dataset Structure
81
+
82
+ ### Data Instances
83
+
84
+ A typical data point comprises a `question`, it's `answer` the `language` of the question text and the split to which it belongs.
85
+
86
+ ```
87
+ {
88
+ "id": "-3979399588609321314",
89
+ "question": "Сколько детей было у Наполео́на I Бонапа́рта?",
90
+ "answers": ["сын"],
91
+ "lang": "ru",
92
+ "split": "train"
93
+ }
94
+ ```
95
+
96
+ ### Data Fields
97
+
98
+ - `id`: An identifier for each example in the dataset
99
+ - `question`: Open domain question
100
+ - `answers`: The corresponding answer to the question posed
101
+ - `lang`: BCP-47 language tag
102
+ - `split`: identifier to differentiate train, validation and test splits
103
+
104
+ ### Data Splits
105
+
106
+ The data is split into a training, validation and test set for each of the two configurations.
107
+
108
+ | | Tain | Valid | Test |
109
+ | ----- | ------ | ----- | ---- |
110
+ | XOR Retrieve | 15250| 2113| 2501|
111
+ | XOR Full | 61360| 3179| 8177|
112
+
113
+ ## Dataset Creation
114
+
115
+ ### Curation Rationale
116
+
117
+ This task framework reflects well real-world scenarios where a QA system uses multilingual document collections and answers questions asked by users with diverse linguistic and cultural backgrounds. Despite the common assumption that we can find answers in the target language, web re- sources in non-English languages are largely lim- ited compared to English (information scarcity), or the contents are biased towards their own cul- tures (information asymmetry). To solve these issues, XOR-TYDI QA (Asai et al., 2020) provides a benchmark for developing a multilingual QA system that finds answers in multiple languages.
118
+
119
+ ### Source Data
120
+
121
+ annotation pipeline consists of four steps: 1) collection of realistic questions that require cross-lingual ref- erences by annotating questions from TYDI QA without a same-language answer; 2) question translation from a target language to the pivot language of English where the missing informa- tion may exist; 3) answer span selection in the pivot language given a set of candidate documents; 4) answer verification and translation from the pivot language back to the original language.
122
+
123
+ #### Initial Data Collection and Normalization
124
+
125
+ [More Information Needed]
126
+
127
+ #### Who are the source language producers?
128
+
129
+ The Dataset is created by extending TyDiQA dataset and translating the questions into other languages. The answers are obtained by crowdsourcing the questions to Mechanical Turk workders
130
+
131
+ ### Annotations
132
+
133
+ #### Annotation process
134
+
135
+ The English questions from TyDiQA are translated into other languages. The languages are chosen based on the availability of wikipedia data and the availability of tranlators.
136
+
137
+ #### Who are the annotators?
138
+
139
+ The translations are carried out using the professionla tranlation service (Gengo)[https://gengo.com] and the answers are annotated by MechanicalTurk workers
140
+
141
+ ### Personal and Sensitive Information
142
+
143
+ The dataset is created from wikipedia content and the QA task requires preserving the named entities, there by all the Wikipedia Named Entities are preserved in the data. Not much information has been provided about masking sensitive information.
144
+
145
+ ## Considerations for Using the Data
146
+
147
+ ### Social Impact of Dataset
148
+
149
+ [More Information Needed]
150
+
151
+ ### Discussion of Biases
152
+
153
+ [More Information Needed]
154
+
155
+ ### Other Known Limitations
156
+
157
+ [More Information Needed]
158
+
159
+ ## Additional Information
160
+
161
+ ### Dataset Curators
162
+
163
+ The people associated with the creation of the dataset are Akari Asai, Jungo Kasai, Jonathan H. Clark, Kenton Lee, Eunsol Choi, Hannaneh Hajishirzi
164
+
165
+ ### Licensing Information
166
+
167
+ XOR-TyDi QA is distributed under the [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/legalcode) license
168
+
169
+ ### Citation Information
170
+
171
+ ```
172
+ @article{xorqa,
173
+ title = {XOR QA: Cross-lingual Open-Retrieval Question Answering},
174
+ author = {Akari Asai and Jungo Kasai and Jonathan H. Clark and Kenton Lee and Eunsol Choi and Hannaneh Hajishirzi}
175
+ year = {2020}
176
+ }
177
+ ```
dataset_infos.json ADDED
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+ {"xor-retrieve": {"description": " XOR-TyDi QA brings together for the first time information-seeking questions,\n open-retrieval QA, and multilingual QA to create a multilingual open-retrieval\n QA dataset that enables cross-lingual answer retrieval. It consists of questions\n written by information-seeking native speakers in 7 typologically diverse languages\n and answer annotations that are retrieved from multilingual document collections.\n There are three sub-tasks: XOR-Retrieve, XOR-EnglishSpan, and XOR-Full.\n\nXOR-Retrieve is a cross-lingual retrieval task where a question is written in the target\nlanguage (e.g., Japanese) and a system is required to retrieve English document that answers the question.\n", "citation": " @misc{asai2020xor,\n title={XOR QA: Cross-lingual Open-Retrieval Question Answering}, \n author={Akari Asai and Jungo Kasai and Jonathan H. Clark and Kenton Lee and Eunsol Choi and Hannaneh Hajishirzi},\n year={2020},\n eprint={2010.11856},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://nlp.cs.washington.edu/xorqa/", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "lang": {"num_classes": 7, "names": ["ar", "bn", "fi", "ja", "ko", "ru", "te"], "names_file": null, "id": null, "_type": "ClassLabel"}, "answers": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "xor_ty_di", "config_name": "xor-retrieve", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1698662, "num_examples": 15250, "dataset_name": "xor_ty_di"}, "validation": {"name": "validation", "num_bytes": 259887, "num_examples": 2113, "dataset_name": "xor_ty_di"}, "test": {"name": "test", "num_bytes": 219284, "num_examples": 2501, "dataset_name": "xor_ty_di"}}, "download_checksums": {"https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_train_retrieve_eng_span.jsonl": {"num_bytes": 2912848, "checksum": "f0c8202eebcef9fc129c9bb8e7a12fae1a7648163b249dc0a06df4318923f70b"}, "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_dev_retrieve_eng_span.jsonl": {"num_bytes": 427653, "checksum": "4e6118cd647fd90c7c810dd3041d9c2f0ee1e3816a6a09a0568c93e23f0c938d"}, "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_test_retrieve_eng_span_q_only.jsonl": {"num_bytes": 362804, "checksum": "7aaa11939a3c08c113c61c7e6fe663018cb6ad46f516bc8aba1f0bc378a28624"}}, "download_size": 3703305, "post_processing_size": null, "dataset_size": 2177833, "size_in_bytes": 5881138}, "xor-full": {"description": " XOR-TyDi QA brings together for the first time information-seeking questions,\n open-retrieval QA, and multilingual QA to create a multilingual open-retrieval\n QA dataset that enables cross-lingual answer retrieval. It consists of questions\n written by information-seeking native speakers in 7 typologically diverse languages\n and answer annotations that are retrieved from multilingual document collections.\n There are three sub-tasks: XOR-Retrieve, XOR-EnglishSpan, and XOR-Full.\n\nXOR-Full is a cross-lingual retrieval task where a question is written in the target\nlanguage (e.g., Japanese) and a system is required to output a short answer in the target language.", "citation": " @misc{asai2020xor,\n title={XOR QA: Cross-lingual Open-Retrieval Question Answering}, \n author={Akari Asai and Jungo Kasai and Jonathan H. Clark and Kenton Lee and Eunsol Choi and Hannaneh Hajishirzi},\n year={2020},\n eprint={2010.11856},\n archivePrefix={arXiv},\n primaryClass={cs.CL}\n}\n", "homepage": "https://nlp.cs.washington.edu/xorqa/", "license": "", "features": {"question": {"dtype": "string", "id": null, "_type": "Value"}, "lang": {"num_classes": 7, "names": ["ar", "bn", "fi", "ja", "ko", "ru", "te"], "names_file": null, "id": null, "_type": "ClassLabel"}, "answers": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": null, "builder_name": "xor_ty_di", "config_name": "xor-full", "version": {"version_str": "1.0.0", "description": "", "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7250913, "num_examples": 61360, "dataset_name": "xor_ty_di"}, "validation": {"name": "validation", "num_bytes": 394531, "num_examples": 3179, "dataset_name": "xor_ty_di"}, "test": {"name": "test", "num_bytes": 706832, "num_examples": 8177, "dataset_name": "xor_ty_di"}}, "download_checksums": {"https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_train_full.jsonl": {"num_bytes": 12126903, "checksum": "7bb3b97f0ff57a39df21289102cc4e278406074c460310f4a22ff50ed578f315"}, "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_dev_full.jsonl": {"num_bytes": 640875, "checksum": "eb53a0ce73c5c7b7d76a99aa8a3548f01ff7788f8a969c27390f1a3938689185"}, "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_test_full_q_only.jsonl": {"num_bytes": 1176124, "checksum": "eac4ecb847b16037c6410c5bbcf64c10be3500d64082eef532852a98c5593cbf"}}, "download_size": 13943902, "post_processing_size": null, "dataset_size": 8352276, "size_in_bytes": 22296178}}
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xor_tydi_qa.py ADDED
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1
+ """XOR QA: Cross-lingual Open-Retrieval Question Answering"""
2
+
3
+ from __future__ import absolute_import, division, print_function
4
+
5
+ import json
6
+ import textwrap
7
+
8
+ import datasets
9
+
10
+
11
+ _XOR_TYDI_QA_CITATION = """\
12
+ @misc{asai2020xor,
13
+ title={XOR QA: Cross-lingual Open-Retrieval Question Answering},
14
+ author={Akari Asai and Jungo Kasai and Jonathan H. Clark and Kenton Lee and Eunsol Choi and Hannaneh Hajishirzi},
15
+ year={2020},
16
+ eprint={2010.11856},
17
+ archivePrefix={arXiv},
18
+ primaryClass={cs.CL}
19
+ }
20
+ """
21
+
22
+ _XOR_TYDI_QA_DESCRIPTION = """\
23
+ XOR-TyDi QA brings together for the first time information-seeking questions,
24
+ open-retrieval QA, and multilingual QA to create a multilingual open-retrieval
25
+ QA dataset that enables cross-lingual answer retrieval. It consists of questions
26
+ written by information-seeking native speakers in 7 typologically diverse languages
27
+ and answer annotations that are retrieved from multilingual document collections.
28
+ There are three sub-tasks: XOR-Retrieve, XOR-EnglishSpan, and XOR-Full.
29
+ """
30
+
31
+ _DESCRIPTIONS = {
32
+ "xor-retrieve": textwrap.dedent(
33
+ """\
34
+ XOR-Retrieve is a cross-lingual retrieval task where a question is written in the target
35
+ language (e.g., Japanese) and a system is required to retrieve English document that answers the question.
36
+ """
37
+ ),
38
+ "xor-full": textwrap.dedent(
39
+ """\
40
+ XOR-Full is a cross-lingual retrieval task where a question is written in the target
41
+ language (e.g., Japanese) and a system is required to output a short answer in the target language."""
42
+ ),
43
+ }
44
+
45
+ _DATA_URLS = {
46
+ "xor-retrieve": {
47
+ "train": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_train_retrieve_eng_span.jsonl",
48
+ "dev": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_dev_retrieve_eng_span.jsonl",
49
+ "test": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_test_retrieve_eng_span_q_only.jsonl",
50
+ },
51
+ "xor-full": {
52
+ "train": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_train_full.jsonl",
53
+ "dev": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_dev_full.jsonl",
54
+ "test": "https://nlp.cs.washington.edu/xorqa/XORQA_site/data/xor_test_full_q_only.jsonl",
55
+ },
56
+ }
57
+
58
+ _XOR_TYDI_QA_URL = "https://nlp.cs.washington.edu/xorqa/"
59
+
60
+
61
+ class XORTyDiConfig(datasets.BuilderConfig):
62
+ "BuilderConfig for XOR-TyDi Dataset"
63
+
64
+ def __init__(self, data_url, citation, url, **kwargs):
65
+ """
66
+ Args:
67
+
68
+ data_url: `dictionary`, dict with url for each split of data.
69
+ citation: `string`, citation for the dataset.
70
+ url: `string`, url for information about the dataset.
71
+ **kwargs: keyword arguments forwarded to super.
72
+ """
73
+ super(XORTyDiConfig, self).__init__(version=datasets.Version("1.0.0", ""), **kwargs)
74
+ self.data_url = data_url
75
+ self.citation = citation
76
+ self.url = url
77
+
78
+
79
+ class XORTyDi(datasets.GeneratorBasedBuilder):
80
+
81
+ BUILDER_CONFIGS = [
82
+ XORTyDiConfig(
83
+ name=name,
84
+ description=_DESCRIPTIONS[name],
85
+ data_url=_DATA_URLS[name],
86
+ citation=_XOR_TYDI_QA_CITATION,
87
+ url=_XOR_TYDI_QA_URL,
88
+ )
89
+ for name in ["xor-retrieve", "xor-full"]
90
+ ]
91
+
92
+ def _info(self):
93
+ features = {}
94
+ features["question"] = datasets.Value("string")
95
+ features["lang"] = datasets.features.ClassLabel(names=["ar", "bn", "fi", "ja", "ko", "ru", "te"])
96
+ features["answers"] = datasets.Value("string")
97
+
98
+ return datasets.DatasetInfo(
99
+ description=_XOR_TYDI_QA_DESCRIPTION + "\n" + self.config.description,
100
+ features=datasets.Features(features),
101
+ homepage=self.config.url,
102
+ citation=_XOR_TYDI_QA_CITATION,
103
+ )
104
+
105
+ def _split_generators(self, dl_manager):
106
+ train = dl_manager.download_and_extract(self.config.data_url["train"])
107
+ dev = dl_manager.download_and_extract(self.config.data_url["dev"])
108
+ test = dl_manager.download_and_extract(self.config.data_url["test"])
109
+
110
+ return [
111
+ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": train, "split": "train"}),
112
+ datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": dev, "split": "dev"}),
113
+ datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": test, "split": "test"}),
114
+ ]
115
+
116
+ def _generate_examples(self, filepath, split):
117
+ with open(filepath, encoding="utf-8") as f:
118
+ jlines = f.read()
119
+ result = [json.loads(jline) for jline in jlines.splitlines()]
120
+ if split == "test":
121
+ for id_, row in enumerate(result):
122
+ yield id_, {"question": row["question"], "answers": "None", "lang": row["lang"].strip()}
123
+ else:
124
+ for id_, row in enumerate(result):
125
+ yield id_, {
126
+ "question": row["question"],
127
+ "answers": " ".join(row["answers"]),
128
+ "lang": row["lang"].strip(),
129
+ }