import os import json import datasets from typing import Any import sys _CITATION = """\ @inproceedings{lecorve2022sparql2text, title={Coqar: Question rewriting on coqa}, author={Lecorv\'e, Gw\'enol\'e and Veyret, Morgan and Brabant, Quentin and Rojas-Barahona, Lina M.}, journal={Proceedings of the Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the International Joint Conference on Natural Language Processing (AACL-IJCNLP)}, year={2022} } """ _HOMEPAGE = "" _DESCRIPTION = """\ Special version of CSQA for the SPARQL-to-Text task """ _URLS = { "all": "json/csqa_sparql_to_text.tar.gz" } class CSQA(datasets.GeneratorBasedBuilder): """ Complex Sequential Question Answering dataset """ VERSION = datasets.Version("1.0.0") def _info(self): return datasets.DatasetInfo( # This is the description that will appear on the datasets page. description=_DESCRIPTION, # datasets.features.FeatureConnectors #"active_set" #"all_entities" #"bool_ques_type" #"count_ques_sub_type" #"count_ques_type" #"description" #"entities" #"entities_in_utterance" #"gold_actions" #"inc_ques_type" #"is_inc" #"is_incomplete" #"is_spurious" #"masked_verbalized_answer" #"parsed_active_set" #"ques_type_id" #"question-type" #"relations" #"sec_ques_sub_type" #"sec_ques_type" #"set_op_choice" #"set_op" #"sparql_query" #"speaker" #"type_list" #"utterance" #"utterance" #"verbalized_all_entities" #"verbalized_answer" #"verbalized_entities_in_utterance" #"verbalized_gold_actions" #"verbalized_parsed_active_set" #"verbalized_sparql_query" #"verbalized_triple" #"verbalized_type_list" features=datasets.Features( { "id": datasets.Value("string"), "turns": [ { "id": datasets.Value("int64"), "ques_type_id": datasets.Value("int64"), "question-type": datasets.Value("string"), "description": datasets.Value("string"), "entities_in_utterance": [datasets.Value("string")], "relations": [datasets.Value("string")], "type_list": [datasets.Value("string")], "speaker": datasets.Value("string"), "utterance": datasets.Value("string"), "all_entities": [datasets.Value("string")], "active_set": [datasets.Value("string")], 'sec_ques_sub_type': datasets.Value("int64"), 'sec_ques_type': datasets.Value("int64"), 'set_op_choice': datasets.Value("int64"), 'is_inc': datasets.Value("int64"), 'count_ques_sub_type': datasets.Value("int64"), 'count_ques_type': datasets.Value("int64"), 'is_incomplete': datasets.Value("int64"), 'inc_ques_type': datasets.Value("int64"), 'set_op': datasets.Value("int64"), 'bool_ques_type': datasets.Value("int64"), 'entities': [datasets.Value("string")], "clarification_step": datasets.Value("int64"), "gold_actions": [[datasets.Value("string")]], "is_spurious": datasets.Value("bool"), "masked_verbalized_answer": datasets.Value("string"), "parsed_active_set": [datasets.Value("string")], "sparql_query": datasets.Value("string"), "verbalized_all_entities": [datasets.Value("string")], "verbalized_answer": datasets.Value("string"), "verbalized_entities_in_utterance": [datasets.Value("string")], "verbalized_gold_actions": [[datasets.Value("string")]], "verbalized_parsed_active_set": [datasets.Value("string")], "verbalized_sparql_query": datasets.Value("string"), "verbalized_triple": datasets.Value("string"), "verbalized_type_list": [datasets.Value("string")] } ] } ), # If there's a common (input, target) tuple from the features, # specify them here. They'll be used if as_supervised=True in # builder.as_dataset supervised_keys=None, # Homepage of the dataset for documentation homepage=_HOMEPAGE, citation=_CITATION, ) def _split_generators(self, dl_manager): """Returns SplitGenerators.""" # Downloads the data and defines the splits # dl_manager is a datasets.download.DownloadManager that can be used to # download and extract URLs downloaded_files = dl_manager.download_and_extract(_URLS) train_path = os.path.join(downloaded_files['all'],'csqa_sparql_to_text/train/') test_path = os.path.join(downloaded_files['all'],'csqa_sparql_to_text/test/') valid_path = os.path.join(downloaded_files['all'],'csqa_sparql_to_text/valid/') return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"filepath": train_path, "split": "train"} ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"filepath": test_path, "split": "test"} ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"filepath": valid_path, "split": "valid"} ), ] def _generate_examples(self, filepath, split): """Yields examples.""" # Yields (key, example) tuples from the dataset def _transform(x): pattern = { "id": None, "ques_type_id": None, "question-type": "", "description": "", "entities_in_utterance": [], "relations": [], "type_list": [], "speaker": "", "utterance": "", "all_entities": [], "active_set": [], 'sec_ques_sub_type': None, 'sec_ques_type': None, 'set_op_choice': None, 'is_inc': None, 'count_ques_sub_type': None, 'count_ques_type': None, 'is_incomplete': None, 'inc_ques_type': None, 'set_op': None, 'bool_ques_type': None, 'entities': [], "clarification_step": None, "gold_actions": [], "is_spurious": None, "masked_verbalized_answer": None, "parsed_active_set": [], "sparql_query": None, "verbalized_all_entities": [], "verbalized_answer": None, "verbalized_entities_in_utterance": [], "verbalized_gold_actions": [], "verbalized_parsed_active_set": [], "verbalized_sparql_query": None, "verbalized_triple": [], "verbalized_type_list": [] } # if "verbalized_triple" in x: # x["verbalized_triple"] = json.dumps(x["verbalized_triple"]) # for k in ["parsed_active_set", "verbalized_gold_actions", "verbalized_parsed_active_set"]: # if k in x: # del x[k] pattern.update(x) # if "verbalized_triple" in pattern: # if type(pattern["verbalized_triple"]) != list: # print(pattern["verbalized_triple"]) # sys.exit() return pattern data_keys = {} for root, dirs, files in os.walk(filepath): dialog_id = root.split('/')[-1] for i,filename in enumerate(files): sample_id = "%s:%s"%(dialog_id,i) with open(os.path.join(root,filename),'r') as f: data = json.load(f) # print("--") for x in data: for k,v in x.items(): if not k in data_keys: data_keys[k] = type(v) new_data = list() for i,_ in enumerate(data): # if "verbalized_triple" in data[i]: # print(json.dumps(data[i]["verbalized_triple"], indent=2)) # if i < len(data)-1: # if "verbalized_triple" in data[i+1]: # print("i+1", json.dumps(data[i+1]["verbalized_triple"], indent=2)) new_data.append(data[i]) data = [ _transform(x) for x in data] yield sample_id, { "id": sample_id, "turns": data }