Datasets:
Tasks:
Question Answering
Sub-tasks:
open-domain-qa
Languages:
English
Multilinguality:
monolingual
Annotations Creators:
crowdsourced
Source Datasets:
extended|doc2dial
ArXiv:
Tags:
License:
| # coding=utf-8 | |
| # Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors. | |
| # | |
| # 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. | |
| # Lint as: python3 | |
| """MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents""" | |
| import json | |
| import os | |
| import datasets | |
| logger = datasets.logging.get_logger(__name__) | |
| _CITATION = """\ | |
| @inproceedings{feng2021multidoc2dial, | |
| title={MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents}, | |
| author={Feng, Song and Patel, Siva Sankalp and Wan, Hui and Joshi, Sachindra}, | |
| booktitle={EMNLP}, | |
| year={2021} | |
| } | |
| """ | |
| _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. | |
| """ | |
| _HOMEPAGE = "https://doc2dial.github.io/multidoc2dial/" | |
| _URL = "https://doc2dial.github.io/multidoc2dial/file/multidoc2dial.zip" | |
| class MultiDoc2dial(datasets.GeneratorBasedBuilder): | |
| """MultiDoc2Dial v1.0""" | |
| VERSION = datasets.Version("1.0.0") | |
| BUILDER_CONFIGS = [ | |
| datasets.BuilderConfig( | |
| name="dialogue_domain", | |
| version=VERSION, | |
| description="This part of the dataset covers the dialogue domain that has questions, answers and the associated doc ids", | |
| ), | |
| datasets.BuilderConfig( | |
| name="document_domain", | |
| version=VERSION, | |
| description="This part of the dataset covers the document domain which details all the documents in the various domains", | |
| ), | |
| datasets.BuilderConfig( | |
| name="multidoc2dial", | |
| version=VERSION, | |
| description="Load MultiDoc2Dial dataset for machine reading comprehension tasks", | |
| ), | |
| ] | |
| DEFAULT_CONFIG_NAME = "multidoc2dial" | |
| def _info(self): | |
| if self.config.name == "dialogue_domain": | |
| features = datasets.Features( | |
| { | |
| "dial_id": datasets.Value("string"), | |
| "domain": datasets.Value("string"), | |
| "turns": [ | |
| { | |
| "turn_id": datasets.Value("int32"), | |
| "role": datasets.Value("string"), | |
| "da": datasets.Value("string"), | |
| "references": [ | |
| { | |
| "id_sp": datasets.Value("string"), | |
| "label": datasets.Value("string"), | |
| "doc_id": datasets.Value("string"), | |
| } | |
| ], | |
| "utterance": datasets.Value("string"), | |
| } | |
| ], | |
| } | |
| ) | |
| elif "document_domain" in self.config.name: | |
| features = datasets.Features( | |
| { | |
| "domain": datasets.Value("string"), | |
| "doc_id": datasets.Value("string"), | |
| "title": datasets.Value("string"), | |
| "doc_text": datasets.Value("string"), | |
| "spans": [ | |
| { | |
| "id_sp": datasets.Value("string"), | |
| "tag": datasets.Value("string"), | |
| "start_sp": datasets.Value("int32"), | |
| "end_sp": datasets.Value("int32"), | |
| "text_sp": datasets.Value("string"), | |
| "title": datasets.Value("string"), | |
| "parent_titles": datasets.features.Sequence( | |
| { | |
| "id_sp": datasets.Value("string"), | |
| "text": datasets.Value("string"), | |
| "level": datasets.Value("string"), | |
| } | |
| ), | |
| "id_sec": datasets.Value("string"), | |
| "start_sec": datasets.Value("int32"), | |
| "text_sec": datasets.Value("string"), | |
| "end_sec": datasets.Value("int32"), | |
| } | |
| ], | |
| "doc_html_ts": datasets.Value("string"), | |
| "doc_html_raw": datasets.Value("string"), | |
| } | |
| ) | |
| else: | |
| features = datasets.Features( | |
| { | |
| "id": datasets.Value("string"), | |
| "title": datasets.Value("string"), | |
| "context": datasets.Value("string"), | |
| "question": datasets.Value("string"), | |
| "da": datasets.Value("string"), | |
| "answers": datasets.features.Sequence( | |
| { | |
| "text": datasets.Value("string"), | |
| "answer_start": datasets.Value("int32"), | |
| } | |
| ), | |
| "utterance": datasets.Value("string"), | |
| "domain": datasets.Value("string"), | |
| } | |
| ) | |
| return datasets.DatasetInfo( | |
| description=_DESCRIPTION, | |
| features=features, | |
| supervised_keys=None, | |
| homepage=_HOMEPAGE, | |
| citation=_CITATION, | |
| ) | |
| def _split_generators(self, dl_manager): | |
| data_dir = dl_manager.download_and_extract(_URL) | |
| if self.config.name == "dialogue_domain": | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, "multidoc2dial/multidoc2dial_dial_train.json"), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, "multidoc2dial/multidoc2dial_dial_validation.json"), | |
| }, | |
| ), | |
| ] | |
| elif self.config.name == "document_domain": | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, "multidoc2dial/multidoc2dial_doc.json"), | |
| }, | |
| ) | |
| ] | |
| elif "multidoc2dial_" in self.config.name: | |
| domain = self.config.name.split("_")[-1] | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "filepath": os.path.join( | |
| data_dir, | |
| "multidoc2dial_domain", | |
| domain, | |
| "multidoc2dial_dial_validation.json", | |
| ), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": os.path.join( | |
| data_dir, | |
| "multidoc2dial_domain", | |
| domain, | |
| "multidoc2dial_dial_train.json", | |
| ), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "filepath": os.path.join( | |
| data_dir, | |
| "multidoc2dial_domain", | |
| domain, | |
| "multidoc2dial_dial_test.json", | |
| ), | |
| }, | |
| ), | |
| ] | |
| elif self.config.name == "multidoc2dial": | |
| return [ | |
| datasets.SplitGenerator( | |
| name=datasets.Split.VALIDATION, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, "multidoc2dial/multidoc2dial_dial_validation.json"), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TRAIN, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, "multidoc2dial/multidoc2dial_dial_train.json"), | |
| }, | |
| ), | |
| datasets.SplitGenerator( | |
| name=datasets.Split.TEST, | |
| gen_kwargs={ | |
| "filepath": os.path.join(data_dir, "multidoc2dial/multidoc2dial_dial_test.json"), | |
| }, | |
| ), | |
| ] | |
| def _load_doc_data_rc(self, filepath): | |
| doc_filepath = os.path.join(os.path.dirname(filepath), "multidoc2dial_doc.json") | |
| with open(doc_filepath, encoding="utf-8") as f: | |
| data = json.load(f)["doc_data"] | |
| return data | |
| def _get_answers_rc(self, references, spans, doc_text): | |
| """Obtain the grounding annotation for a given dialogue turn""" | |
| if not references: | |
| return [] | |
| start, end = -1, -1 | |
| ls_sp = [] | |
| for ele in references: | |
| id_sp = ele["id_sp"] | |
| start_sp, end_sp = spans[id_sp]["start_sp"], spans[id_sp]["end_sp"] | |
| if start == -1 or start > start_sp: | |
| start = start_sp | |
| if end < end_sp: | |
| end = end_sp | |
| ls_sp.append(doc_text[start_sp:end_sp]) | |
| answer = {"text": doc_text[start:end], "answer_start": start} | |
| return [answer] | |
| def _generate_examples(self, filepath): | |
| """This function returns the examples in the raw (text) form.""" | |
| if self.config.name == "dialogue_domain": | |
| logger.info("generating examples from = %s", filepath) | |
| with open(filepath, encoding="utf-8") as f: | |
| data = json.load(f) | |
| for domain in data["dial_data"]: | |
| for dialogue in data["dial_data"][domain]: | |
| x = { | |
| "dial_id": dialogue["dial_id"], | |
| "turns": dialogue["turns"], | |
| "domain": domain, | |
| } | |
| yield dialogue["dial_id"], x | |
| elif self.config.name == "document_domain": | |
| logger.info("generating examples from = %s", filepath) | |
| with open(filepath, encoding="utf-8") as f: | |
| data = json.load(f) | |
| for domain in data["doc_data"]: | |
| for doc_id in data["doc_data"][domain]: | |
| yield doc_id, { | |
| "domain": domain, | |
| "doc_id": doc_id, | |
| "title": data["doc_data"][domain][doc_id]["title"], | |
| "doc_text": data["doc_data"][domain][doc_id]["doc_text"], | |
| "spans": [ | |
| { | |
| "id_sp": data["doc_data"][domain][doc_id]["spans"][i]["id_sp"], | |
| "tag": data["doc_data"][domain][doc_id]["spans"][i]["tag"], | |
| "start_sp": data["doc_data"][domain][doc_id]["spans"][i]["start_sp"], | |
| "end_sp": data["doc_data"][domain][doc_id]["spans"][i]["end_sp"], | |
| "text_sp": data["doc_data"][domain][doc_id]["spans"][i]["text_sp"], | |
| "title": data["doc_data"][domain][doc_id]["spans"][i]["title"], | |
| "parent_titles": data["doc_data"][domain][doc_id]["spans"][i]["parent_titles"], | |
| "id_sec": data["doc_data"][domain][doc_id]["spans"][i]["id_sec"], | |
| "start_sec": data["doc_data"][domain][doc_id]["spans"][i]["start_sec"], | |
| "text_sec": data["doc_data"][domain][doc_id]["spans"][i]["text_sec"], | |
| "end_sec": data["doc_data"][domain][doc_id]["spans"][i]["end_sec"], | |
| } | |
| for i in data["doc_data"][domain][doc_id]["spans"] | |
| ], | |
| "doc_html_ts": data["doc_data"][domain][doc_id]["doc_html_ts"], | |
| "doc_html_raw": data["doc_data"][domain][doc_id]["doc_html_raw"], | |
| } | |
| elif "multidoc2dial" in self.config.name: | |
| logger.info("generating examples from = %s", filepath) | |
| doc_data = self._load_doc_data_rc(filepath) | |
| d_doc_data = {} | |
| for domain, d_doc in doc_data.items(): | |
| for doc_id, data in d_doc.items(): | |
| d_doc_data[doc_id] = data | |
| with open(filepath, encoding="utf-8") as f: | |
| dial_data = json.load(f)["dial_data"] | |
| for domain, dialogues in dial_data.items(): | |
| for dial in dialogues: | |
| all_prev_utterances = [] | |
| for idx, turn in enumerate(dial["turns"]): | |
| doc_id = turn["references"][0]["doc_id"] | |
| doc = d_doc_data[doc_id] | |
| utterance_line = turn["utterance"].replace("\n", " ").replace("\t", " ") | |
| all_prev_utterances.append("{}: {}".format(turn["role"], utterance_line)) | |
| if turn["role"] == "agent": | |
| continue | |
| if idx + 1 < len(dial["turns"]): | |
| if ( | |
| dial["turns"][idx + 1]["role"] == "agent" | |
| and dial["turns"][idx + 1]["da"] != "respond_no_solution" | |
| ): | |
| turn_to_predict = dial["turns"][idx + 1] | |
| else: | |
| continue | |
| else: | |
| continue | |
| question_str = utterance_line + "[SEP]" + "||".join(reversed(all_prev_utterances[:-1])) | |
| id_ = "{}_{}".format(dial["dial_id"], turn["turn_id"]) | |
| qa = { | |
| "id": id_, | |
| "title": doc_id, | |
| "context": doc["doc_text"], | |
| "question": question_str, | |
| "da": turn["da"], | |
| "answers": self._get_answers_rc( | |
| turn_to_predict["references"], | |
| doc["spans"], | |
| doc["doc_text"], | |
| ), | |
| "utterance": turn_to_predict["utterance"], | |
| "domain": domain, | |
| } | |
| yield id_, qa | |