TurkuNLP/Turku-WebQA

Dataset Summary

The Turku WebQA dataset is a Finnish Question-Answer dataset that has been extracted from different CommonCrawl sources (Parsebank, mC4-Fi, CC-Fi).

The dataset has 237,000 question-answer pairs (altogether 290,000 questions, but not all have an answer). The questions with no answers can be discarded by taking out the rows with None (null). The codebase as well as the raw data can be found on GitHub.

The extracted question-answer pairs include various topics from the source corpora, some of which are explored in the paper for which the citing information can be found below.

Data Fields

• source: a string feature. Tells whether the question-answer pair is extracted from Parsebank, mC4-Fi or CC-Fi.
• id: a string feature. Id of the original text from which the question-answer pair is extracted.
• question: a string feature.
• answer: a string feature. Can also be None (null).

Manual Evalution of the Pairs

To get an idea on how good the extracted pairs were, a sample was annotated for noisy artefacts, insufficient answers and missing context. The evaluation showed that there is variation between the different source corpora.

Source	Noisy artefacts	Insufficient Answer	Missing context
Total (N=73)	0,29	0,22	0,08
CC-Fi (N=25)	0,36	0,22	0,03
mC4-Fi (N=25)	0,28	0,28	0,14
Parsebank (N=22)	0,23	0,14	0,07

Citing

To cite this dataset use the following bibtex.

@inproceedings{eskelinen-etal-2024-building-question,
    title = "Building Question-Answer Data Using Web Register Identification",
    author = "Eskelinen, Anni  and
      Myntti, Amanda  and
      Henriksson, Erik  and
      Pyysalo, Sampo  and
      Laippala, Veronika",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.234",
    pages = "2595--2611",
    abstract = "This article introduces a resource-efficient method for developing question-answer (QA) datasets by extracting QA pairs from web-scale data using machine learning (ML). Our method benefits from recent advances in web register (genre) identification and consists of two ML steps with an additional post-processing step. First, using XLM-R and the multilingual CORE web register corpus series with categories such as QA Forum, we train a multilingual classifier to retrieve documents that are likely to contain QA pairs from web-scale data. Second, we develop a NER-style token classifier to identify the QA text spans within these documents. To this end, we experiment with training on a semi-synthetic dataset built on top of the English LFQA, a small set of manually cleaned web QA pairs in English and Finnish, and a Finnish web QA pair dataset cleaned using ChatGPT. The evaluation of our pipeline demonstrates its capability to efficiently retrieve a substantial volume of QA pairs. While the approach is adaptable to any language given the availability of language models and extensive web data, we showcase its efficiency in English and Finnish, developing the first open, non-synthetic and non-machine translated QA dataset for Finnish {--} Turku WebQA {--} comprising over 200,000 QA pairs.",
}