Slovak Dataset for Multilingual Question Answering

Hládek, Daniel; Staš, Ján; Juhár, Jozef; Koctur, Tomas

doi:10.1109/access.2023.3262308

Cited by 4 publications

References 48 publications

(39 reference statements)

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Pipeline and dataset generation for automated fact-checking in almost any language

Drchal,

Ullrich,

Mlynář

et al. 2024

Neural Comput & Applic

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This article presents a pipeline for automated fact-checking leveraging publicly available language models and data. The objective is to assess the accuracy of textual claims using evidence from a ground-truth evidence corpus. The pipeline consists of two main modules—the evidence retrieval and the claim veracity evaluation. Our primary focus is on the ease of deployment in various languages that remain unexplored in the field of automated fact-checking. Unlike most similar pipelines, which work with evidence sentences, our pipeline processes data on a paragraph level, simplifying the overall architecture and data requirements. Given the high cost of annotating language-specific fact-checking training data, our solution builds on the question answering for claim generation method, which we adapt and use to generate the data for all models of the pipeline. Our strategy enables the introduction of new languages through machine translation of only two fixed datasets of moderate size. Subsequently, any number of training samples can be generated based on an evidence corpus in the target language. We provide open access to all data and fine-tuned models for Czech, English, Polish, and Slovak pipelines, as well as to our codebase that may be used to reproduce the results. We comprehensively evaluate the pipelines for all four languages, including human annotations and per-sample difficulty assessment using Pointwise $${\mathcal {V}}$$ V -information. The presented experiments are based on full Wikipedia snapshots to promote reproducibility. To facilitate implementation and user interaction, we develop the FactSearch application featuring the proposed pipeline and the preliminary feedback on its performance.

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Pipeline and dataset generation for automated fact-checking in almost any language

Drchal,

Ullrich,

Mlynář

et al. 2024

Neural Comput & Applic

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EHMMQA: English, Hindi, and Marathi multilingual question answering framework using deep learning

Lahoti,

Mittal,

Singh

2024

Nat. lang. processing

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Multilingual question answering (MQA) is an effective access to multilingual data to provide accurate and precise answers, irrespective of language. Although a wide range of datasets is available for monolingual QA systems in natural language processing, benchmark datasets specifically designed for MQA are considerably limited. The absence of comprehensive and benchmark datasets hinders the development and evaluation of MQA systems. To overcome this issue, the proposed work attempts to develop the EHMQuAD dataset, an MQA dataset for low-resource languages such as Hindi and Marathi accompanying the English language. The EHMQuAD dataset is developed using a synthetic corpora generation approach, and an alignment is performed after translation to make the dataset more accurate. Further, the EHMMQA model is proposed to create an abstract framework that uses a deep neural network that accepts pairs of questions and context and returns an accurate answer based on those questions. The shared question and shared context representation have been designed separately to develop this system. The experiments of the proposed model are conducted on the MMQA, Translated SQuAD, XQuAD, MLQA, and EHMQuAD datasets, and EM and F1-score are used as performance measures. The proposed model (EHMMQA) is collated with state-of-the-art MQA baseline models for all possible monolingual and multilingual settings. The results signify that EHMMQA is a considerable step toward the MQA system utilizing Hindi and Marathi languages. Hence, it becomes a new state-of-the-art model for Hindi and Marathi languages.

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