masakhane-io / afriqa

Crosslingual Question Answering for African Languages

AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages

This work is licensed under a Creative Commons Attribution 4.0 International License.

Paper: https://arxiv.org/abs/2305.06897

AfriQA is the first cross-lingual question answering (QA) dataset with a focus on African languages. The dataset includes over 12,000 XOR QA examples across 10 African languages, making it an invaluable resource for developing more equitable QA technology. African languages have historically been underserved in the digital landscape, with far less in-language content available online. This makes it difficult for QA systems to provide accurate information to users in their native language. However, cross-lingual open-retrieval question answering (XOR QA) systems can help fill this gap by retrieving answer content from other languages. AfriQA focuses specifically on African languages where cross-lingual answer content is the only high-coverage source of information. Previous datasets have primarily focused on languages where cross-lingual QA augments coverage from the target language, but AfriQA highlights the importance of African languages as a realistic use case for XOR QA.

Languages

There are currently 10 languages covered in AfriQA:

• Bemba (bem)
• Fon (fon)
• Hausa (hau)
• Igbo (ibo)
• Kinyarwanda (kin)
• Swahili (swa)
• Twi (twi)
• Wolof (wol)
• Yorùbá (yor)

Dataset Download

Question-answer pairs for each language and train-dev-test split are in the data directory in jsonlines format.

• Dataset Naming Convention ==> queries.afriqa.{lang_code}.{en/fr}.{split}.json

  • Data Format:

    • id : Question ID
    • question : Question in African Language
    • translated_question : Question translated into a pivot language (English/French)
    • answers : Answer in African Language
    • lang : Datapoint Language (African Language) e.g bem
    • split : Dataset Split
    • translated_answer : Answer in Pivot Language
    • translation_type : Translation type of question and answers

    {   "id": 0, 
        "question": "Bushe icaalo ca Egypt caali tekwapo ne caalo cimbi?", 
        "translated_question": "Has the country of Egypt been colonized before?", 
        "answers": "['Emukwai']", 
        "lang": "bem", 
        "split": "dev", 
        "translated_answer": "['yes']", 
        "translation_type": "human_translation"
        }

  • The dataset is also available on Hugging Face

    from datasets import load_dataset
    dataset = load_dataset("masakhane/afriqa", "bem")

• Gold Passages for 7 languages is currently available in data directory in jsonlines format.

  • Dataset Naming Convention: ==> gold_span_passages.afriqa.{lang_code}.{en/fr}.{split}.json
  • Gold_Passages Data Format:
    • answer_pivot: Answer in English/French
      • answer_start: Index of answer start in context
      • text: Answer span
    • Context: Gold Passage
    • id : Question ID
    • question_lang: Question in African language
    • question_translated: Question translated into pivot language (French/English)
    • title: Gold passage Title

Environment and Repository Setup

• Set up a virtual environment using Conda or Virtualenv or

  conda create -n xor_qa_venv python=3.9 anaconda
  conda activate xor_qa_venv

  or

  python3 -m venv xor_qa_venv
  source xor_qa_venv/bin/activate

• Clone the repo

  git clone https://github.com/ToluClassics/masakhane_xqa --recurse-submodules

• Install Requirements

  pip install -r requirements.txt

Processing Wikipedia dumps for Retrieval

The already processed dumps are available on HuggingFace 😊, It is recommended to use this exact corpora to be able to reproduce the baseline results. To download:

• English
• French

However, to prepare the Wikipedia retrieval corpus yourself, consult docs/process_wiki_dumps.md.

Data Splits

For all languages, there are three splits.

The original splits were named train, dev and test and they correspond to the train, validation and test splits.

The splits have the following sizes :

Language	train	dev	test
Bemba	502	503	314
Fon	427	428	386
Hausa	435	436	300
Igbo	417	418	409
Kinyarwanda	407	409	347
Swahili	415	417	302
Twi	451	452	490
Wolof	503	504	334
Yoruba	360	361	332
Zulu	387	388	325
Total	4333	4346	3560

BibTeX entry and citation info

@misc{ogundepo2023afriqa,
      title={AfriQA: Cross-lingual Open-Retrieval Question Answering for African Languages}, 
      author={Odunayo Ogundepo and Tajuddeen R. Gwadabe and Clara E. Rivera and Jonathan H. Clark and Sebastian Ruder and David Ifeoluwa Adelani and Bonaventure F. P. Dossou and Abdou Aziz DIOP and Claytone Sikasote and Gilles Hacheme and Happy Buzaaba and Ignatius Ezeani and Rooweither Mabuya and Salomey Osei and Chris Emezue and Albert Njoroge Kahira and Shamsuddeen H. Muhammad and Akintunde Oladipo and Abraham Toluwase Owodunni and Atnafu Lambebo Tonja and Iyanuoluwa Shode and Akari Asai and Tunde Oluwaseyi Ajayi and Clemencia Siro and Steven Arthur and Mofetoluwa Adeyemi and Orevaoghene Ahia and Aremu Anuoluwapo and Oyinkansola Awosan and Chiamaka Chukwuneke and Bernard Opoku and Awokoya Ayodele and Verrah Otiende and Christine Mwase and Boyd Sinkala and Andre Niyongabo Rubungo and Daniel A. Ajisafe and Emeka Felix Onwuegbuzia and Habib Mbow and Emile Niyomutabazi and Eunice Mukonde and Falalu Ibrahim Lawan and Ibrahim Said Ahmad and Jesujoba O. Alabi and Martin Namukombo and Mbonu Chinedu and Mofya Phiri and Neo Putini and Ndumiso Mngoma and Priscilla A. Amuok and Ruqayya Nasir Iro and Sonia Adhiambo},
      year={2023},
      eprint={2305.06897},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}