KenSwQuAD—A Question Answering Dataset for Swahili Low-resource Language

Abstract

The need for Question Answering datasets in low resource languages is the motivation of this research, leading to the development of Kencorpus Swahili Question Answering Dataset, KenSwQuAD. This dataset is annotated from raw story texts of Swahili low resource language, which is a predominantly spoken in Eastern African and in other parts of the world. Question Answering (QA) datasets are important for machine comprehension of natural language for tasks such as internet search and dialog systems. Machine learning systems need training data such as the gold standard Question Answering set developed in this research. The research engaged annotators to formulate QA pairs from Swahili texts collected by the Kencorpus project, a Kenyan languages corpus. The project annotated 1,445 texts from the total 2,585 texts with at least 5 QA pairs each, resulting into a final dataset of 7,526 QA pairs. A quality assurance set of 12.5% of the annotated texts confirmed that the QA pairs were all correctly annotated. A proof of concept on applying the set to the QA task confirmed that the dataset can be usable for such tasks. KenSwQuAD has also contributed to resourcing of the Swahili language.

Building Text and Speech Benchmark Datasets and Models for Low‐Resourced East African Languages: Experiences and Lessons

“…The dataset was created as a collection of magazine PDF editions manually cleaned and reviewed. Work has been carried out to create the Kencorpus dataset [35] which is a Kenyan language corpus for Swahili, Dholuo, and Luhya languages. The Kencorpus contains a subset of Dholuo to Swahili translations and Luhya to Swahili translations.…”

“…The Kencorpus [35], which contains 177 hours of speech data for Swahili, Dholuo, and Luhya languages, was collected using voice recorders and the rest was obtained through collaborating with media houses.…”

Slovak Dataset for Multilingual Question Answering

“…In some cases, the source of data is exam questions and the student's answers assigned to them, official documents containing FAQs, various quizzes, etc. The QA datasets for Bulgarian [41], Portuguese [42], Turkish [43], or Kenyan Swahili [44] were created in this way.…”

Cross-lingual Open-Retrieval Question Answering for African Languages

“…Datasets for QA and Information Retrieval tasks have also been created. They are, however, very few and cater to individual languages (Abedissa et al, 2023;Wanjawa et al, 2023) or a small subset of languages spoken in individual countries (Daniel et al, 2019;Zhang et al, 2022). Given the region's large number of linguistically diverse and information-scarce languages, multilingual and cross-lingual datasets are encouraged to catalyze research efforts.…”

“…Open Retrieval? # Languages # African Languages XQA (Liu et al, 2019) ✓ ✓ ✓ 9 Nil XOR QA (Asai et al, 2021) ✓ ✓ ✓ 7 Nil XQuAD (Artetxe et al, 2020) ✓ ✗ ✗ 11 Nil MLQA ✓ ✗ ✗ 7 Nil MKQA (Longpre et al, 2021) ✓ ✗ ✓ 26 Nil TyDi QA (Clark et al, 2020) ✓ ✗ ✓ 11 1 AmQA (Abedissa et al, 2023) ✓ ✗ ✗ 1 1 KenSwQuAD (Wanjawa et al, 2023) ✓ ✗ ✗ 1 1 AFRIQA (Ours) ✓ ✓ ✓ 10 10 (see Table 3)…”