Abstract
This paper proposes a preprocessing methodology, namely, Graded Word Drop (GWD) and its algorithm as a solution to the problem of bigger contexts in Extractive Question Answering for Hindi language, focusing mainly on “When” (
) type questions. This paper discusses in detail the problems associated with bigger contexts, such as increased prediction times, misleading text as a part of bigger context etc. It then discusses three methodologies, viz., Boolean Model and two new proposed methodologies of Word Drop. We used cross-linguality of transformer models, mBERT, XLM-RoBERTa and MuRIL and fine-tuned them using SQuAD dataset. We used 84 Hindi-language, “When” (
) type questions from chaii (Challenge in AI for India) dataset for evaluation. The GWD preprocessed text gave improvement over non-preprocessed results in terms of both accuracy and F1-score and achieved 53.57%, 38.09%, 55.95% accuracy, and 63.21, 68.37 and 67.09 F1-score in mBERT, XLM-RoBERTa and MuRIL respectively and improved prediction times by five fold in all these models.
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Vani, Singh, S., Burman, P., Jain, A., Tiwary, U.S. (2023). GWD: Graded Word Drop Model for When Type Questions for Hindi QA. In: Zaynidinov, H., Singh, M., Tiwary, U.S., Singh, D. (eds) Intelligent Human Computer Interaction. IHCI 2022. Lecture Notes in Computer Science, vol 13741. Springer, Cham. https://doi.org/10.1007/978-3-031-27199-1_15
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