Datasets:
Tasks:
Question Answering
Sub-tasks:
extractive-qa
Languages:
Basque
Size:
1K<n<10K
Tags:
dialogue-qa
License:
| # 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. | |
| """ElkarHizketak: Conversational Question Answering dataset in Basque""" | |
| import json | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _CITATION = """\ | |
| @inproceedings{otegi-etal-2020-conversational, | |
| title = "{Conversational Question Answering in Low Resource Scenarios: A Dataset and Case Study for {B}asque}", | |
| author = "Otegi, Arantxa and | |
| Agirre, Aitor and | |
| Campos, Jon Ander and | |
| Soroa, Aitor and | |
| Agirre, Eneko", | |
| booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference", | |
| year = "2020", | |
| publisher = "European Language Resources Association", | |
| url = "https://aclanthology.org/2020.lrec-1.55", | |
| pages = "436--442", | |
| ISBN = "979-10-95546-34-4", | |
| } | |
| """ | |
| _DESCRIPTION = """ | |
| ElkarHizketak is a low resource conversational Question Answering | |
| (QA) dataset in Basque created by Basque speaker volunteers. The | |
| dataset contains close to 400 dialogues and more than 1600 question | |
| and answers, and its small size presents a realistic low-resource | |
| scenario for conversational QA systems. The dataset is built on top of | |
| Wikipedia sections about popular people and organizations. The | |
| dialogues involve two crowd workers: (1) a student ask questions after | |
| reading a small introduction about the person, but without seeing the | |
| section text; and (2) a teacher answers the questions selecting a span | |
| of text of the section. """ | |
| _HOMEPAGE = "http://ixa.si.ehu.es/node/12934" | |
| _LICENSE = "Creative Commons Attribution-ShareAlike 4.0 International Public License (CC BY-SA 4.0)" | |
| _URLs = { | |
| "train": "http://ixa2.si.ehu.es/convai/elkarhizketak-v1.0/elkarhizketak-train-v1.0.json", | |
| "validation": "http://ixa2.si.ehu.es/convai/elkarhizketak-v1.0/elkarhizketak-dev-v1.0.json", | |
| "test": "http://ixa2.si.ehu.es/convai/elkarhizketak-v1.0/elkarhizketak-test-v1.0.json", | |
| } | |
| class Elkarhizketak(datasets.GeneratorBasedBuilder): | |
| """ElkarHizketak: Conversational Question Answering dataset in Basque. Version 1.0.""" | |
| VERSION = datasets.Version("1.0.0") | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig( | |
| name="plain_text", | |
| description="Plain text", | |
| version=VERSION, | |
| ), | |
| ] | |
| def _info(self): | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=datasets.Features( | |
| { | |
| "dialogue_id": datasets.Value("string"), | |
| "wikipedia_page_title": datasets.Value("string"), | |
| "background": datasets.Value("string"), | |
| "section_title": datasets.Value("string"), | |
| "context": datasets.Value("string"), | |
| "turn_id": datasets.Value("string"), | |
| "question": datasets.Value("string"), | |
| "yesno": datasets.ClassLabel(names=["y", "n", "x"]), | |
| "answers": datasets.Sequence( | |
| { | |
| "text": datasets.Value("string"), | |
| "answer_start": datasets.Value("int32"), | |
| "input_text": datasets.Value("string"), | |
| } | |
| ), | |
| "orig_answer": { | |
| "text": datasets.Value("string"), | |
| "answer_start": datasets.Value("int32"), | |
| }, | |
| } | |
| ), | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| license=_LICENSE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| """Returns SplitGenerators.""" | |
| data_dir = dl_manager.download_and_extract(_URLs) | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": data_dir["train"], | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "filepath": data_dir["validation"], | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "filepath": data_dir["test"], | |
| }, | |
| ), | |
| ] | |
| def _generate_examples(self, filepath): | |
| """Yields examples.""" | |
| logger.info("generating examples from = %s", filepath) | |
| key = 0 | |
| with open(filepath, encoding="utf-8") as f: | |
| elkarhizketak = json.load(f) | |
| for section in elkarhizketak["data"]: | |
| wiki_page_title = section.get("title", "").strip() | |
| background = section.get("background", "").strip() | |
| section_title = section.get("section_title", "").strip() | |
| for dialogue in section["paragraphs"]: | |
| context = dialogue["context"].strip() | |
| dialogue_id = dialogue["id"] | |
| for qa in dialogue["qas"]: | |
| answer_starts = [answer["answer_start"] for answer in qa["answers"]] | |
| answers = [answer["text"].strip() for answer in qa["answers"]] | |
| input_texts = [answer["input_text"].strip() for answer in qa["answers"]] | |
| yield key, { | |
| "wikipedia_page_title": wiki_page_title, | |
| "background": background, | |
| "section_title": section_title, | |
| "context": context, | |
| "dialogue_id": dialogue_id, | |
| "question": qa["question"], | |
| "turn_id": qa["id"], | |
| "yesno": qa["yesno"], | |
| "answers": { | |
| "answer_start": answer_starts, | |
| "text": answers, | |
| "input_text": input_texts, | |
| }, | |
| "orig_answer": { | |
| "answer_start": qa["orig_answer"]["answer_start"], | |
| "text": qa["orig_answer"]["text"], | |
| }, | |
| } | |
| key += 1 | |