├── CODE_OF_CONDUCT.md ├── CONTRIBUTING.md ├── mlqa_evaluation_v1.py ├── README.md └── LICENSE /CODE_OF_CONDUCT.md: -------------------------------------------------------------------------------- 1 | Facebook has adopted a Code of Conduct that we expect project participants to adhere to. 2 | Please read the [full text](https://code.fb.com/codeofconduct/) 3 | so that you can understand what actions will and will not be tolerated. 4 | -------------------------------------------------------------------------------- /CONTRIBUTING.md: -------------------------------------------------------------------------------- 1 | # Contributing to UnsupervisedQA 2 | We want to make contributing to this project as easy and transparent as 3 | possible. 4 | 5 | ## Pull Requests 6 | We actively welcome your pull requests. 7 | 8 | 1. Fork the repo and create your branch from `master`. 9 | 2. If you've added code that should be tested, add tests. 10 | 3. If you've changed APIs, update the documentation. 11 | 4. Ensure the test suite passes. 12 | 5. Make sure your code lints. 13 | 6. If you haven't already, complete the Contributor License Agreement ("CLA"). 14 | 15 | ## Contributor License Agreement ("CLA") 16 | In order to accept your pull request, we need you to submit a CLA. You only need 17 | to do this once to work on any of Facebook's open source projects. 18 | 19 | Complete your CLA here: 20 | 21 | ## Issues 22 | We use GitHub issues to track public bugs. Please ensure your description is 23 | clear and has sufficient instructions to be able to reproduce the issue. 24 | 25 | ## Coding Style 26 | We follow the PEP style guidelines and encourage you to as well. 27 | 28 | ## License 29 | By contributing to UnsupervisedQA, you agree that your contributions will be licensed 30 | under the LICENSE file in the root directory of this source tree. 31 | -------------------------------------------------------------------------------- /mlqa_evaluation_v1.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2019-present, Facebook, Inc. 2 | # All rights reserved. 3 | # 4 | # This source code is licensed under the license found in the 5 | # LICENSE file in the root directory of this source tree. 6 | # 7 | """ Official evaluation script for the MLQA dataset. """ 8 | from __future__ import print_function 9 | from collections import Counter 10 | import string 11 | import re 12 | import argparse 13 | import json 14 | import sys 15 | import unicodedata 16 | 17 | 18 | PUNCT = {chr(i) for i in range(sys.maxunicode) if unicodedata.category(chr(i)).startswith('P')}.union(string.punctuation) 19 | WHITESPACE_LANGS = ['en', 'es', 'hi', 'vi', 'de', 'ar'] 20 | MIXED_SEGMENTATION_LANGS = ['zh'] 21 | 22 | 23 | def whitespace_tokenize(text): 24 | return text.split() 25 | 26 | 27 | def mixed_segmentation(text): 28 | segs_out = [] 29 | temp_str = "" 30 | for char in text: 31 | if re.search(r'[\u4e00-\u9fa5]', char) or char in PUNCT: 32 | if temp_str != "": 33 | ss = whitespace_tokenize(temp_str) 34 | segs_out.extend(ss) 35 | temp_str = "" 36 | segs_out.append(char) 37 | else: 38 | temp_str += char 39 | 40 | if temp_str != "": 41 | ss = whitespace_tokenize(temp_str) 42 | segs_out.extend(ss) 43 | 44 | return segs_out 45 | 46 | 47 | def normalize_answer(s, lang): 48 | """Lower text and remove punctuation, articles and extra whitespace.""" 49 | 50 | def remove_articles(text, lang): 51 | if lang == 'en': 52 | return re.sub(r'\b(a|an|the)\b', ' ', text) 53 | elif lang == 'es': 54 | return re.sub(r'\b(un|una|unos|unas|el|la|los|las)\b', ' ', text) 55 | elif lang == 'hi': 56 | return text # Hindi does not have formal articles 57 | elif lang == 'vi': 58 | return re.sub(r'\b(của|là|cái|chiếc|những)\b', ' ', text) 59 | elif lang == 'de': 60 | return re.sub(r'\b(ein|eine|einen|einem|eines|einer|der|die|das|den|dem|des)\b', ' ', text) 61 | elif lang == 'ar': 62 | return re.sub('\sال^|ال', ' ', text) 63 | elif lang == 'zh': 64 | return text # Chinese does not have formal articles 65 | else: 66 | raise Exception('Unknown Language {}'.format(lang)) 67 | 68 | def white_space_fix(text, lang): 69 | if lang in WHITESPACE_LANGS: 70 | tokens = whitespace_tokenize(text) 71 | elif lang in MIXED_SEGMENTATION_LANGS: 72 | tokens = mixed_segmentation(text) 73 | else: 74 | raise Exception('Unknown Language {}'.format(lang)) 75 | return ' '.join([t for t in tokens if t.strip() != '']) 76 | 77 | def remove_punc(text): 78 | return ''.join(ch for ch in text if ch not in PUNCT) 79 | 80 | def lower(text): 81 | return text.lower() 82 | 83 | return white_space_fix(remove_articles(remove_punc(lower(s)), lang), lang) 84 | 85 | 86 | def f1_score(prediction, ground_truth, lang): 87 | prediction_tokens = normalize_answer(prediction, lang).split() 88 | ground_truth_tokens = normalize_answer(ground_truth, lang).split() 89 | common = Counter(prediction_tokens) & Counter(ground_truth_tokens) 90 | num_same = sum(common.values()) 91 | if num_same == 0: 92 | return 0 93 | precision = 1.0 * num_same / len(prediction_tokens) 94 | recall = 1.0 * num_same / len(ground_truth_tokens) 95 | f1 = (2 * precision * recall) / (precision + recall) 96 | return f1 97 | 98 | 99 | def exact_match_score(prediction, ground_truth, lang): 100 | return (normalize_answer(prediction, lang) == normalize_answer(ground_truth, lang)) 101 | 102 | 103 | def metric_max_over_ground_truths(metric_fn, prediction, ground_truths, lang): 104 | scores_for_ground_truths = [] 105 | for ground_truth in ground_truths: 106 | score = metric_fn(prediction, ground_truth, lang) 107 | scores_for_ground_truths.append(score) 108 | return max(scores_for_ground_truths) 109 | 110 | 111 | def evaluate(dataset, predictions, lang): 112 | f1 = exact_match = total = 0 113 | for article in dataset: 114 | for paragraph in article['paragraphs']: 115 | for qa in paragraph['qas']: 116 | total += 1 117 | if qa['id'] not in predictions: 118 | message = 'Unanswered question ' + qa['id'] + \ 119 | ' will receive score 0.' 120 | print(message, file=sys.stderr) 121 | continue 122 | ground_truths = list(map(lambda x: x['text'], qa['answers'])) 123 | prediction = predictions[qa['id']] 124 | exact_match += metric_max_over_ground_truths( 125 | exact_match_score, prediction, ground_truths, lang) 126 | f1 += metric_max_over_ground_truths( 127 | f1_score, prediction, ground_truths, lang) 128 | 129 | exact_match = 100.0 * exact_match / total 130 | f1 = 100.0 * f1 / total 131 | 132 | return {'exact_match': exact_match, 'f1': f1} 133 | 134 | 135 | if __name__ == '__main__': 136 | expected_version = '1.0' 137 | parser = argparse.ArgumentParser( 138 | description='Evaluation for MLQA ' + expected_version) 139 | parser.add_argument('dataset_file', help='Dataset file') 140 | parser.add_argument('prediction_file', help='Prediction File') 141 | parser.add_argument('answer_language', help='Language code of answer language') 142 | 143 | args = parser.parse_args() 144 | with open(args.dataset_file) as dataset_file: 145 | dataset_json = json.load(dataset_file) 146 | if (str(dataset_json['version']) != expected_version): 147 | print('Evaluation expects v-' + expected_version + 148 | ', but got dataset with v-' + dataset_json['version'], 149 | file=sys.stderr) 150 | dataset = dataset_json['data'] 151 | with open(args.prediction_file) as prediction_file: 152 | predictions = json.load(prediction_file) 153 | print(json.dumps(evaluate(dataset, predictions, args.answer_language))) 154 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # MLQA 2 | 3 | MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. 4 | MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. 5 | MLQA is *highly parallel*, with QA instances parallel between 4 different languages on average. 6 | 7 |
8 |

9 | Facebook AI Research and UCL NLP 10 |
11 |

12 |
13 | 14 | This repository contains both links to download the data, and the official evaluation script to evaluate models. 15 | 16 | 17 |
18 |

19 | Two Instances from MLQA 20 |
21 | Two Instances from MLQA 22 |

23 |
24 | 25 | For more details on how the dataset was created, and baseline models testing cross-lingual transfer from English, 26 | please refer to our paper, [MLQA: Evaluating Cross-lingual Extractive Question Answering](http://arxiv.org/abs/1910.07475) 27 | 28 | ## Data download 29 | 30 | The data can be downloaded from here: [MLQA_V1.zip](https://dl.fbaipublicfiles.com/MLQA/MLQA_V1.zip) 31 | 32 | MLQA is split into development and test splits. The files are formatted using the popular SQuAD format. The files are named with the following convention: 33 | ``` 34 | {split}-context-{context_language}-question-{question_language}.json 35 | ``` 36 | e.g. the file `dev-context-ar-question-es.json` contains the development split, with questions in Spanish, and context documents (and answers) in Arabic. 37 | 38 | The following table shows how much data is in each language: 39 | 40 | Split | en | de | es | ar | zh| vi | hi | 41 | |:---: |:---: |:---: | :---: |:---: | :---: | :---: | :---: | 42 | dev | 1148 | 512 | 500 | 517 | 504 | 511 | 507 | 43 | test | 11590 | 4517 | 5254 | 5335 | 5137 | 5495 | 4918 | 44 | 45 | ## Evaluation 46 | 47 | The official evaluation script is `mlqa_evaluation_v1.py`. See the paper for a description of our evaluation procedure. 48 | 49 | The script is run in an analogous way to the SQuAD evaluation script, but requires the answer language as an additional command line parameter. E.g. to evaluate Hindi predictions, run the following: 50 | 51 | ``` 52 | python mlqa_evaluation_v1.py \ 53 | path/to/MLQA_V1/test-context-hi-question-hi.json \ 54 | path/to/hindi_predictions.json \ 55 | hi 56 | ``` 57 | 58 | where `path/to/hindi_predictions.json` contains the model's predicted answers as a json dict, with keys being the question id, and values being the predicted answer string. 59 | 60 | ## Baselines 61 | 62 | The MLQA [paper](http://arxiv.org/abs/1910.07475) presents several baselines for zero-shot experiments on MLQA, with training QA data taken from SQuAD V1.1, and using the MLQA English development set for early stopping. 63 | 64 | The F1 scores for zero-shot transfer from training with english questions and documents to target language questions and documents are shown below (see the paper for further details). There is lots of room for improvement, and we hope the community will engage in this QA challenge. 65 | 66 | 67 | | Model F1 Score | en | es | de | ar | hi| vi | zh | 68 | |:--- |:---: |:---: | :---: |:---: | :---: | :---: | :---: | 69 | BERT-Large | **80.2**| - | - | - |- |- |- | 70 | Multilingual-BERT | 77.7| 64.3| 57.9| 45.7| 43.8| 57.1| 57.5| 71 | XLM |74.9| **68.0**| **62.2**|**54.8**| 48.8| 61.4| 61.1| 72 | Translate-test BERT-L | -| 65.4 | 57.9 | 33.6 | 23.8 | 58.2 |44.2 | 73 | Translate-train M-BERT | - | 53.9 | 62.0 | 51.8 | **55.0**| **62.0**| **61.4** | 74 | Translate-train XLM | -| 65.2| 61.4| 54.0| 50.7| 59.3| 59.8 | 75 | 76 | 77 | ## Best Practices 78 | 79 | MLQA is intended to be an evaluation corpus. Please limit evaluations on the test set to an absolute minimum to prevent overfitting. There is a development dataset split which can be used for running intermediate evaluations during model development. 80 | 81 | If you are running experiments on MLQA, it is important that you clearly state your experimental settings. 82 | If you are performing *zero-shot* experiments, you should only use the development data in the language you are training on. 83 | Using MLQA dev data for particular target language to tune performance in that target language is a valid research direction, 84 | but is not strictly *zero-shot* and you should make sure that you explicitly state how you use the development data to ensure fair comparison in the future. 85 | 86 | ## "No Answer" Instances 87 | 88 | As mentioned in the paper, some instances that cannot be answered are generated by our annotation procedure. We will release these as a separate resource shortly here. 89 | 90 | ## Translate-Train and Translate-Test Data 91 | 92 | Machine-translated data for Translate-train (SQuAD Train and Dev sets machine-translated into Arabic, German, Hindi, Vietnamese, Simplified Chinese and Spanish) can be downloaded here: [mlqa-translate-train.tar.gz](https://dl.fbaipublicfiles.com/MLQA/mlqa-translate-train.tar.gz) 93 | 94 | Machine-translated data for Translate-Test (MLQA-test set machine-translated into English) can be downloaded here: [mlqa-translate-test.tar.gz](https://dl.fbaipublicfiles.com/MLQA/mlqa-translate-test.tar.gz) 95 | 96 | ## References 97 | 98 | Please cite [[1]](https://arxiv.org/abs/1901.07291) if you found the resources in this repository useful. 99 | 100 | 101 | [1] P Lewis, B Oğuz, R. Rinot, S. Riedel and H. Schwenk [*MLQA: Evaluating Cross-lingual Extractive Question Answering*](http://arxiv.org/abs/1910.07475) 102 | 103 | 104 | ``` 105 | @article{lewis2019mlqa, 106 | title={MLQA: Evaluating Cross-lingual Extractive Question Answering}, 107 | author={Lewis, Patrick and O\u{g}uz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger}, 108 | journal={arXiv preprint arXiv:1910.07475}, 109 | year={2019} 110 | } 111 | ``` 112 | 113 | ## License 114 | 115 | The dataset, which is derived from paragraphs in Wikipedia, is licensed under [CC-BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/legalcode). 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