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https://github.com/google-research-datasets/tydiqa

TyDi QA contains 200k human-annotated question-answer pairs in 11 Typologically Diverse languages, written without seeing the answer and without the use of translation, and is designed for the training and evaluation of automatic question answering systems. This repository provides evaluation code and a baseline system for the dataset.
https://github.com/google-research-datasets/tydiqa

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TyDi QA contains 200k human-annotated question-answer pairs in 11 Typologically Diverse languages, written without seeing the answer and without the use of translation, and is designed for the training and evaluation of automatic question answering systems. This repository provides evaluation code and a baseline system for the dataset.

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README 

        
# TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages



[**Tasks**](#the-tasks) | [**Download**](#download-the-dataset) |

[**Baselines**](#building-a-baseline-system) | [**Evaluation**](#evaluation) |

[**Leaderboard**](#leaderboard-submissions) |

[**Website/Glosses**](https://ai.google.com/research/tydiqa) |

[**Paper**](https://storage.googleapis.com/tydiqa/tydiqa.pdf) |

[**Slides**](https://storage.googleapis.com/tydiqa/tydiqa_slides_stanford.pdf) |

[**Podcast**](#podcast) | [**Changelog**](CHANGELOG.md)



This repository contains information about TyDi QA, code for evaluating results

on the dataset, implementations of baseline systems for the dataset, and some

advice for working with the dataset.



Want to keep up to date on updates and new releases? Join our low-traffic

[announcement email list](https://groups.google.com/forum/#!forum/tydiqa-announce).



# Introduction



TyDi QA is a question answering dataset covering 11 typologically diverse

languages with 204K question-answer pairs. The languages of TyDi QA are diverse

with regard to their typology -- the set of linguistic features that each

language expresses -- such that we expect models performing well on this set to

generalize across a large number of the languages in the world. It contains

language phenomena that would not be found in English-only corpora. To provide a

realistic information-seeking task and avoid priming effects, questions are

written by people who want to know the answer, but don’t know the answer yet,

(unlike SQuAD and its descendents) and the data is collected directly in each

language without the use of translation (unlike MLQA and XQuAD).



To see some examples from the dataset with linguistic glosses or for information

on TyDi QA's leaderboard, see the

[website](https://ai.google.com/research/tydiqa).



For a full description of the dataset, how it was collected, and the quality

measurements for the baseline system, see the

[TACL article](https://storage.googleapis.com/tydiqa/tydiqa.pdf).



# The Tasks



*   Primary tasks:

    *   **Passage selection task (SelectP):** Given a list of the passages in

        the article, return either (a) the index of the passage that answers the

        question or (b) NULL if no such passage exists.

    *   **Minimal answer span task (MinSpan):** Given the full text of an

        article, return one of (a) the start and end byte indices of the minimal

        span that completely answers the question; (b) YES or NO if the question

        requires a yes/no answer and we can draw a conclusion from the passage;

        (c) NULL if it is not possible to produce a minimal answer for this

        question.

*   Secondary task:

    *   **Gold passage task (GoldP):** Given a passage that is guaranteed to

        contain the answer, predict the single contiguous span of characters

        that answers the question. This is more similar to existing reading

        comprehension datasets (as opposed to the information-seeking task

        outlined above). This task is constructed with two goals in mind: (1)

        more directly comparing with prior work and (2) providing a simplified

        way for researchers to use TyDi QA by providing compatibility with

        existing code for SQuAD 1.1, XQuAD, and MLQA. Toward these goals, the

        gold passage task differs from the primary task in several ways:

        *   only the gold answer passage is provided rather than the entire

            Wikipedia article;

        *   unanswerable questions have been discarded, similar to MLQA and

            XQuAD;

        *   we evaluate with the SQuAD 1.1 metrics like XQuAD; and

        *   Thai and Japanese are removed since the lack of whitespace breaks

            some tools.



We of course encourage you to participate in the primary tasks as we believe

these are a fuller and more robust representative of information-seeking

question answering. However, we realize that not all researchers may be able to

jump directly into these tasks. If you are constrained by computational

resources or are tied to existing code that processes the SQuAD format, the gold

passage task may be a better way for you to get started.



**When reporting results for any TyDi QA tasks, please include the full task

descriptor using one of the strings: TyDiQA-SelectP, TyDiQA-MinSpan, or

TyDiQA-GoldP**. Please do NOT simply list 'TyDi QA' in your results table, since

we do have several flavors of the task, which are quite different from one

another and we want to avoid confusion.



# Download the Dataset



Once you've chosen which task to work on (above), you can download the data at

the following URLs.



For the primary tasks:



*   [https://storage.googleapis.com/tydiqa/v1.0/tydiqa-v1.0-dev.jsonl.gz](https://storage.googleapis.com/tydiqa/v1.0/tydiqa-v1.0-dev.jsonl.gz)

*   [https://storage.googleapis.com/tydiqa/v1.0/tydiqa-v1.0-train.jsonl.gz](https://storage.googleapis.com/tydiqa/v1.0/tydiqa-v1.0-train.jsonl.gz)



The primary task training set is about 1.6GB while the dev set is about 150MB.



For the gold passage task:



*   [https://storage.googleapis.com/tydiqa/v1.1/tydiqa-goldp-v1.1-dev.json](https://storage.googleapis.com/tydiqa/v1.1/tydiqa-goldp-v1.1-dev.json)

*   [https://storage.googleapis.com/tydiqa/v1.1/tydiqa-goldp-v1.1-train.json](https://storage.googleapis.com/tydiqa/v1.1/tydiqa-goldp-v1.1-train.json)

*   [https://storage.googleapis.com/tydiqa/v1.1/tydiqa-goldp-v1.1-dev.tgz](https://storage.googleapis.com/tydiqa/v1.1/tydiqa-goldp-v1.1-dev.tgz)



The gold passage training set is about 50MB and the dev set is about 10MB. The

extra tarball for the dev set contains JSON files that are split along language

boundaries; these are used for evaluation while the single large JSON dev file

makes it easier to run inference on the entire dev set in a single invocation.



# Building a Baseline System



## Primary Tasks (TyDiQA-SelectP and TyDiQA-MinSpan)



We provide a baseline system based on multilingual BERT in this repo. Please see

[baseline/README.md](baseline) for details on running and modifying that system.

You may also find this code useful even if you plan to build a system from

scratch as it is designed to be easily re-used.



## Gold Passage Task (TyDiQA-GoldP)



Because the gold passage task has been simplified to fit the constraints of the

SQuAD 1.1 setting, it can generally be swapped into any code that accepts SQuAD

1.1 JSON inputs by simply changing a few file paths in your code. We provide an

example of doing exactly this with the original/unmodified multilingual BERT

reference implementation. See

[gold_passage_baseline/README.md](gold_passage_baseline) for details.



# Evaluation



## Primary Task Evaluation



The predictions can be evaluated using a command like the following:



```

python3 tydi_eval.py \

  --gold_path=tydiqa-v1.0-dev.jsonl.gz \

  --predictions_path=your_model_predictions.jsonl

```



This script computes language-wise F1 scores and then averages over languages,

excluding English. Spans are compared based on predicted byte positions and

partial credit is assigned within spans based on F1 positional overlap. See the

description of evaluation in the TACL article for details.



Please see the [evaluation script](tydi_eval.py) for a description of the

prediction format that your model should output.



## Gold Passage Task Evaluation



For the gold passage task, we re-use the existing SQuAD 1.1 evaluation code to

allow maximal re-use of existing pipelines. An example of calling the code for

evaluation is in

[gold_passage_baseline/eval_gold_passage_baseline.sh](gold_passage_baseline/eval_gold_passage_baseline.sh).



```

cd gold_passage_baseline

vim eval_gold_passage_baseline.sh  # Edit path to `TYDIQA_GOLDP_DIR`

./eval_gold_passage_baseline.sh predictions.jsonl /tmp

```



Note that for dev and test evaluation, each language is evaluated separately and

the overall score is the average over languages, excluding English.



# Leaderboard Submissions



In addition to reporting results on the dev set in your own research articles,

we also encourage you to submit to our

[public leaderboard](https://ai.google.com/research/tydiqa), to create a record

of your experiments. We believe leaderboard submissions serve two main purposes:



(a) to create an existence proof that such a result is **possible** under

carefully isolated conditions (i.e. cheating, intentional or accidental is

difficult) so that the community knows such a score is possible; and (b) to

inform the community **how** the result was obtained. Toward this latter goal,

we request that you submit a description (e.g. paper draft) of your submission

and also answer a few "repoducibility questions" that let the community know if

it will be possible to reproduce and build on your result. These include:



1.  Is there a research paper describing the system you are submitting? (The

    community benefits far more from knowing how to achieve a result than the

    fact that it exists.)

2.  Is the source code for the system you are submitting publicly available?

    (Your results will be replicated and trusted more if the community can

    quickly and reliably reproduce your results).

3.  Was the system you are submitting trained on any additional public data?

4.  Was the system you are submitting trained on any NON-public data? (The

    community cannot reproduce results on non-public data.)

5.  Was the system you are submitting trained with, or does it use, any external

    APIs, data labelers, or data transformations (e.g. a translation API)? (The

    use of public APIs is not reproducible and creates a black box effect since

    the community does not know the details of the underlying model and data it

    was built on.)



For step-by-step instructions on submitting, see

[leaderboard.md](leaderboard.md).



In addition to submitting to the leaderboard we encourage you to make both your

source code and your Docker images public so that others can easily run

inference with your system. This opens up the possibility of others (such as

MT-focused researchers) building on top of (and citing!) your QA system.



# Analyze Your Results



We encourage those working with the data to not only report numeric results, but

also analyze the results at a linguistic level. Consider partnering with

linguists and/or native speakers of these languages to create glosses that

explain how your model is interacting with language. See the TACL article for

examples of glossed examples with explanations (Figures 2 - 7).



# Source Data



The articles for TyDi QA are drawn from single coherent snapshots of Wikipedia

from the Internet Archive to enable open-retrieval experiments. You can download

the original article data in Wikitext format from the following URLS:



*   https://archive.org/download/arwiki-20190201/arwiki-20190201-pages-articles-multistream.xml.bz2

*   https://archive.org/download/bnwiki-20190201/bnwiki-20190201-pages-articles-multistream.xml.bz2

*   https://archive.org/download/enwiki-20190201/enwiki-20190201-pages-articles-multistream.xml.bz2

*   https://archive.org/download/fiwiki-20190201/fiwiki-20190201-pages-articles-multistream.xml.bz2

*   https://archive.org/download/idwiki-20190201/idwiki-20190201-pages-articles-multistream.xml.bz2

*   https://archive.org/download/jawiki-20190201/jawiki-20190201-pages-articles-multistream.xml.bz2

*   https://archive.org/download/kowiki-20190201/kowiki-20190201-pages-articles-multistream.xml.bz2

*   https://archive.org/download/ruwiki-20190201/ruwiki-20190201-pages-articles-multistream.xml.bz2

*   https://archive.org/download/tewiki-20190201/tewiki-20190201-pages-articles-multistream.xml.bz2

*   https://archive.org/download/tlwiki-20190201/tlwiki-20190201-pages-articles-multistream.xml.bz2

*   https://archive.org/download/swwiki-20190201/swwiki-20190201-pages-articles-multistream.xml.bz2

*   https://archive.org/download/thwiki-20190101/thwiki-20190101-pages-articles-multistream.xml.bz2



# Podcast



We spoke with AI2's NLP Highlights Podcast about TyDi QA. Have a listen on

[https://soundcloud.com/nlp-highlights/111-typologically-diverse-multi-lingual-information-seeking-questions-with-jon-clark](SoundCloud).



# Citation



Please cite the

[TyDi QA TACL article](https://storage.googleapis.com/tydiqa/tydiqa.pdf) as:



```

@article{tydiqa,

title   = {TyDi QA: A Benchmark for Information-Seeking Question Answering in Typologically Diverse Languages},

author  = {Jonathan H. Clark and Eunsol Choi and Michael Collins and Dan Garrette and Tom Kwiatkowski and Vitaly Nikolaev and Jennimaria Palomaki}

year    = {2020},

journal = {Transactions of the Association for Computational Linguistics}

}

```



# Contact us



If you have a technical question regarding the dataset, code or publication,

please create an issue in this repository. This is the fastest way to reach us.