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We also recommend that a 185 | file or class name and description of purpose be included on the 186 | same "printed page" as the copyright notice for easier 187 | identification within third-party archives. 188 | 189 | Copyright [yyyy] [name of copyright owner] 190 | 191 | Licensed under the Apache License, Version 2.0 (the "License"); 192 | you may not use this file except in compliance with the License. 193 | You may obtain a copy of the License at 194 | 195 | http://www.apache.org/licenses/LICENSE-2.0 196 | 197 | Unless required by applicable law or agreed to in writing, software 198 | distributed under the License is distributed on an "AS IS" BASIS, 199 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 200 | See the License for the specific language governing permissions and 201 | limitations under the License. 202 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # domain-specific-QA 2 | Extracting six domain-specific QA datasets from MS MARCO. More details can be found in our paper ["Forget Me Not: Reducing Catastrophic Forgetting for Domain Adaptation in Reading Comprehension"](https://arxiv.org/abs/1911.00202) 3 | 4 | ## To generate 6 domain specific QA datasets from MS MARCO 5 | 6 | Please use Python 2.7 environment. 7 | 8 | Download [MS MARCO Reading Comprehension v2.1 training set](http://www.msmarco.org/dataset.aspx) 9 | `pip install json argparse` 10 | `git clone https://github.com/ibm-aur-nlp/domain-specific-QA.git` 11 | `cd /domain-specific-QA` 12 | `python extract_domain_specific_samples.py --marco /train_v2.1.json --out_dir --lookup_table lookup_table.json` 13 | 14 | In ``, you should see json files of format `squad...json`. The statistics of the 6 domain specific QA datasets is: 15 | 16 | | Domain | Total | Train | Dev | Test | 17 | | :--- | :---: | :---: | :---: | :---: | 18 | | music | 3,596 | 2,517 | 539 | 540 | 19 | | biomedical | 31,620 | 22,134 | 4,743 | 4,743 | 20 | | film | 5,032 | 3,522 | 755 | 755 | 21 | | finance | 9,700 | 6,790 | 1,455 | 1,455 | 22 | | law | 4,436 | 3,105 | 665 | 666 | 23 | | computing | 4,316 | 3,021 | 647 | 648 | 24 | -------------------------------------------------------------------------------- /extract_domain_specific_samples.py: -------------------------------------------------------------------------------- 1 | # Licensed to the Apache Software Foundation (ASF) under one 2 | # or more contributor license agreements. See the NOTICE file 3 | # distributed with this work for additional information 4 | # regarding copyright ownership. The ASF licenses this file 5 | # to you under the Apache License, Version 2.0 (the 6 | # "License"); you may not use this file except in compliance 7 | # with the License. You may obtain a copy of the License at 8 | # 9 | # http://www.apache.org/licenses/LICENSE-2.0 10 | # 11 | # Unless required by applicable law or agreed to in writing, 12 | # software distributed under the License is distributed on an 13 | # "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY 14 | # KIND, either express or implied. See the License for the 15 | # specific language governing permissions and limitations 16 | # under the License. 17 | 18 | import json 19 | import argparse 20 | import os 21 | 22 | parser = argparse.ArgumentParser(description='Extract extractive domains specific questions answering samples from MS MARCO') 23 | parser.add_argument('--marco', help='path to MS MARCO training set') 24 | parser.add_argument('--out_dir', help='path to save domain specific datasets') 25 | parser.add_argument('--lookup_table', help='path to the lookup table') 26 | args = parser.parse_args() 27 | 28 | # Load lookup table 29 | # {query_id: {domain:str, split:str}} 30 | with open(args.lookup_table, 'r') as fp: 31 | domain_table = json.load(fp) 32 | 33 | # Load MS MARCO training set 34 | with open(args.marco, 'r') as fp: 35 | marco = json.load(fp) 36 | rev_query_id = dict() 37 | for query_id in marco['query']: 38 | rev_query_id[marco['query_id'][query_id]] = query_id 39 | 40 | domains = {'finance': {'train': [], 'dev': [], 'test': []}, 41 | 'law': {'train': [], 'dev': [], 'test': []}, 42 | 'music': {'train': [], 'dev': [], 'test': []}, 43 | 'film': {'train': [], 'dev': [], 'test': []}, 44 | 'biomedical': {'train': [], 'dev': [], 'test': []}, 45 | 'computing': {'train': [], 'dev': [], 'test': []}} 46 | 47 | # Convert extractive domain specific samples into the SQUAD format 48 | for domain in domain_table: 49 | for split in domain_table[domain]: 50 | for id in domain_table[domain][split]: 51 | qid, pid = map(int, id.split('_')) 52 | query_id = rev_query_id[qid] 53 | context = marco['passages'][query_id][pid]['passage_text'].strip() 54 | datum = None 55 | for answer in marco['answers'][query_id]: 56 | answer = answer.strip() 57 | if len(answer) > 0: 58 | # Find answer position 59 | answer_start = context.lower().find(answer.lower()) 60 | if answer_start >= 0: 61 | answer = context[answer_start:answer_start + len(answer)] 62 | if datum is None: 63 | # Create a QA sample in SQUAD format 64 | datum = { 65 | 'paragraphs': [{ 66 | 'context': context, 67 | 'qas': [{ 68 | 'answers': [{'answer_start': answer_start, 'text': answer}], 69 | 'question': marco['query'][query_id], 70 | 'id': id 71 | }] 72 | }] 73 | } 74 | else: # Handle multiple-answers case 75 | datum['paragraphs'][0]['qas'][0]['answers'].append( 76 | {'answer_start': answer_start, 77 | 'text': answer} 78 | ) 79 | domains[domain][split].append(datum) 80 | 81 | # Save 82 | if not os.path.exists(args.out_dir): 83 | os.makedirs(args.out_dir) 84 | for domain in domains: 85 | for split in domains[domain]: 86 | with open(os.path.join(args.out_dir, 'squad.%s.%s.json' % (domain, split)), 'w') as fp: 87 | json.dump({'data': domains[domain][split]}, fp) 88 | --------------------------------------------------------------------------------