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An official repository for MIA 2022 (NAACL 2022 Workshop) Shared Task on Cross-lingual Open-Retrieval Question Answering.


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MIA 2022 Shared Task on Cross-lingual Open-Retrieval Question Answering.

This is an official repository for MIA 2022 Shared Task on Cross-lingual Open-Retrieval Question Answering. Please refer the details in our Shared Task call.

If you are interested in participating, please sign up at this form to get invitations for our google group!



Cross-lingual Open Question Answering is a challenging multilingual NLP task, where given questions are written in a user’s preferred language, a system needs to find evidence in large-scale document collections written in many different languages, and return an answer in the user's preferred language, as indicated by their question.

We have awards + prizes for the best Unconstrained system, Constrained system, and Creativity awards for participants without massive compute/resources but still obtain interesting results!

We evaluate models' performance in 1416 languages, 79 of which will not be covered in our training data. 7 languages have development data, while 2 surprise languages released two weeks before submission deadline does not have any development data.

The full list of the languages:

  • Languages with training data: Arabic (ar), Bengali (bn), English (en), Finnish (fi), Japanese (ja), Korean (ko), Russian (ru), Telugu (te)
  • Languages without training data: Spanish (es), Khmer (km), Malay (ms), Swedish (es), Turkish (tr), Chinese (simplified) (zh-cn)
  • New!! Surprise languages: Tagalog (tl), Tamil (ta)

Quick Links


Data Format

Our train and evaluation data files are jsonlines, each of which contains a list of a json data with id, question, lang, answers, split, source (i.e., nq for the data from Natural Questions, xor-tydi for the data from XOR-TyDi QA and mkqa for the data from MKQA).


  • Natural Questions training data
    'id': '-6802534628745605728',
    'question': 'total number of death row inmates in the us',
    'lang': 'en',
    'answers': ['2,718'],
    'split': 'train',
    'source': 'nq'
  • XOR-TyDi QA training data (with answers in the target language)
    'id': '7931051574381133444', 
    'question': '『指輪物語』はいつ出版された', 
    'answers': ['1954年から1955年'], 
    'lang': 'ja', 
    'split': 'train', 
    'source': 'xor-tydi'
  • XOR-TyDi QA training data (without answers in the target language) Note that the part of the XOR-TyDi data only includes English answers due to the limitation of annotation resources (Asai et al., 2021). Those questions are marked as has_eng_answer_only: True.
    'id': '7458071188830898854', 
    'question': 'チョンタル語はいつごろ誕生した', 
    'answers': ['5,000 years ago'], 
    'lang': 'ja', 
    'split': 'train', 
    'source': 'xor-tydi',
    has_eng_answer_only: True

Evaluation Data

The shared task valuation data is originally from XOR-TyDi QA (Asai et al., 2021) and MKQA (Longpre et al., 2021). ​ For this shared task, we re-split development and test data, and conduct additional answer normalizations, so the number on our shared task data is not directly comparable to the results on those datasets in prior paper.

NOTE: Test data will be released in March.

MIA 2022 XOR-TyDi QA data

The data is available at data/mia_2022_dev_xorqa.jsonl.

Dataset size statistics:

Language dev test
ar 590 1387
bn 203 490
fi 1368 974
ja 1056 693
ko 1048 473
ru 910 1018
te 873 564

MIA 2022 Shared Task MKQA data

The data is available at data/eval/

cd data/eval

The MKQA dev set has 1,758 questions per language, and the test set has 5,000 questions per language. All of the questions are parallel across different languages.

Training Data

(1) Constrained Setup

Our training data for the constrained setup consists of English open-QA data from Natural Questions-open (Kwiatkowski et al., 2019; Lee et al., 2019) and the XOR-TyDi QA train data.

The training is available at data/train/ We encourage participants to do data augmentation using Machine Translation or structured knowledge sources (e.g., Wikidata, Wikipedia interlanguage links), and as long as you do not use additional human annotated QA data or data, you submissions will be considered as constrained setup. NB: Using external blackbox APIs such as Google Search API / Google Translate API are not permitted in the constrained setup for inference, but they are permitted for offline data augmentation / training.

cd data/train
Dataset Language # of Examples
Natural Questions en 76635
XOR-TyDi QA ar 18402
XOR-TyDi QA bn 5007
XOR-TyDi QA fi 9768
XOR-TyDi QA ja 7815
XOR-TyDi QA ko 4319
XOR-TyDi QA ru 9290
XOR-TyDi QA te 6759

We also release the training data for our DPR-based baseline, which is created by collecting training for Natural Questions available at DPR repo and XOR-TyDI gold paragraph data. The data can be downloaded at mia2022_shared_task_train_dpr_train.json. If we want to download data programmatically from Google Drive, you can use gdown

import gdown
url = url = ""

Please see more details in the baseline section.

(2) Unconstrained Setup

For the unconstrained setup, you may use additional human-annotated question answering data. Yet, you must not use additional data from Natural Questions or XOR-TyDi QA for training. Participants using additional human-annotated question-answer data must report this and provide details of the additional resources used for training. NB: Using external blackbox APIs such as Google Search API / Google Translate API is permitted in the unconstrained setup.

Wikipedia Dumps

Following the original XOR-TyDi QA datasets, our baselines use the Wikipedia dump from February 2019. For the languages whose 2019 February dump is no longer available, we use October 2021 data.

Link to the Wikipedia dumps

You can find the links of web archive version of Wikipedia dumps at the TyDi QA repository below: TyDi QA's source data list

For the languages that are not listed here, here are the links:

Swedish Wikipedia Spanish Wikipedia Chinese (simplified Wikipedia Malay Wikipedia Khmer Wikipedia Turkish Wikipedia

Preprocessed data in the DPR (100-token) format

You can download preprocessed text data where we split each article in all target languages into 100 token chuncks and concatenate all of them. The download links are below:


We will add this instruction in our README. Section for your feedback! Let us know if you have any more questions. The code used to preprocess Wikipedia is at baseline/wikipedia_preprocess.


Participants will run their systems on the evaluation files (without answer data) and then submit their predictions to our competition site hosted at Systems will first be evaluated using automatic metrics: Exact match (EM) and token-level F1. Although EM is often used as a primarily evaluate metric for English open-retrieval QA, the risk of surface-level mismatching (Min et al., 2021) can be more pervasive in cross-lingual open-retrieval QA. Therefore, we will use F1 as our primary metric and rank systems using their macro averaged F1 scores.

Final Evaluation Procedure: Due to the difference of the datasets' nature, we will calculate macro-average scores on XOR-TyDi and MKQA datasets, and then take the average of the XOR-TyDi QA average {F1, EM} and MKQA average {F1, EM}.


For non-spacing languages (i.e., Japanese, Khmer and Chinese) we use token-level tokenizers, Mecab, khmernltk and jieba to tokenize both predictions and ground-truth answers.

Please install the dependencies by running the command below before running the evaluation scripts:

cd eval_scripts
pip install -r requirements.txt

Please use python 3.x to run the evaluation scripts.

Prediction file format

Evaluate locally

Your prediction file is a json file including a dictionary where the keys are corresponding to the question ids and the values are the answers.

{"7931051574381133444": "1954年から1955年", "-6802534628745605728": "2,718", ... }:

Final submission

To submit your prediction to our leaderboard, you have to create a single json file that includes predictions for all of the XOR-TyDi and MKQA subsets. Please see the details in the submission section.

Evaluate predictions locally

Please run the command below to evaluate your models' performance on MKQA and XOR-TyDi QA.

python --data_file /path/to/your/data/file --pred_file /path/to/prediction/file

For MKQA, you can run the command above for each language or you can run the command below that takes directory names of the prediction files and input data files.

python --data_dir /path/to/data/dir --pred_dir /path/to/pred/files/dir 

You can limit the target languages by setting the --target option. You can add the target languages' language codes (e.g., --target en es sw)


The baseline codes are available at baseline. Our baseline model is the state-of-the-art CORA, which runs a multilingual DPR model to retrieve documents from many different languages and then generate the final answers in the target languages using a multilingual seq2seq generation models. We have two versions:

  1. multilingual DPR + multilingual seq2seq (CORA without iterative training): We train mDPR and mGEN without iterative training process. We first train mDPR, retrieve top passages using the trained mDPR, and then fine-tuned mGEN after we preprocess and augment NQ data using WikiData as in the original CORA paper. Due to the computational costs, we do not re-train the CORA with iterative training on the new data and languages.

  2. CORA with iterative training: We run the publicly available CORA's trained models on our evaluation set. We generate dense embeddings for all of the target languages using their mDPR bi encoders as some of the languages (e.g., Chinese - simplified) are not covered by the CORA's original embeddings. There might be some minor differences in data preprocessing of the original CORA paper and our new data.

Prediction results of the baselines

We've released the final prediction results as well as the intermediate retrieval results for train, dev and test sets.

To download the data, follow the instructions at baseline README.

Final Prediction results

Intermediate Retrieval Results

See the Instructions at the Baseline's README.

Final results F1 | EM |

The final results of Baselines 2 and 3 are shown below. The final macro average scores of those baselines are:

  • Baseline 1 = (38.9 + 18.1 ) / 2= 28.5

  • Baseline 2 = (39.8 + 17.4) / 2= 28.6

  • XOR QA

Language (2) F1 (2) EM (1) F1 (1) EM
Arabic (ar) 51.3 36.0 49.7 33.7
Bengali (bn) 28.7 20.2 29.2 21.2
Finnish (fi) 44.4 35.7 42.7 32.9
Japanese (ja ) 43.2 32.2 41.2 29.6
Korean (ko) 29.8 23.7 30.6 24.5
Russian (ru) 40.7 31.9 40.2 31.1
Telugu (te) 40.2 32.1 38.6 30.7
Macro-Average 39.8 30.3 38.9 29.1
  • MKQA
Language (2) F1 (2) EM (1) F1 (1) EM
Arabic (ar) 8.8 5.7 8.9 5.1
English (en) 27.9 24.5 33.9 24.9
Spanish (es) 24.9 20.9 25.1 19.3
Finnish (fi) 23.3 20.0 21.1 17.4
Japanese (ja) 15.2 6.3 15.3 5.8
Khmer (km) 5.7 4.9 6.0 4.7
Korean (ko) 8.3 6.3 6.7 4.7
Malaysian (ms) 22.6 19.7 24.6 19.7
Russian (ru) 14.0 9.4 15.6 10.6
Swedish (sv) 24.1 21.1 25.5 20.6
Turkish (tr) 20.6 16.7 20.4 16.1
Chinese-simplified (zh_cn) 13.1 6.1 13.7 5.7
Macro-Average 17.4 13.5 18.1 12.9


Our shared task is hosted at

Submission file format

To be considered for the prizes, you have to submit predictions for all of the target languages included in XOR-TYDi and MKQA. A valid submission data is a dictionary with the following keys and the corresponding prediction results in the format discussed in the Evaluation section.

  • xor-tydi
  • mkqa-ar
  • mkqa-en
  • mkqa-es
  • mkqa-fi
  • mkqa-ja
  • mkqa-km
  • mkqa-ko
  • mkqa-ms
  • mkqa-ru
  • mkqa-sv
  • mkqa-tr
  • mkqa-zh_cn
  • sup_ta
  • sup_tl
{"xor-tydi: {...}, "mkqa-ar": {...}, "mkqa-ja": {...}}

We've released our baseline systems' prediction results in the submission format at submission.json (dev data predictions) and submission_test.json (test data predictions).

Shared Task Awards

There are 3 awards in this shared task, each with Google Cloud credits as prizes.

  1. The Best unconstrained system obtaining the highest macro-average F1 scores.
  2. The Best constrained system obtaining the highest macro-average F1 scores.
  3. Creativity award(s): We plan to give additional award(s) to systems that employ a creative approach to the problem, or undertake interesting experiments to better understand the problem. This award is designed to encourage interesting contributions even from teams without access to the largest models or computational resources. Examples include attempts to improve generalization ability/language equality, reducing model sizes, or understanding the weaknesses in existing systems.


If you have any questions, please feel free to email (akari[at] or start a Github issue with a mention to @AkariAsai or @shayne-longpre


An official repository for MIA 2022 (NAACL 2022 Workshop) Shared Task on Cross-lingual Open-Retrieval Question Answering.







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