# 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