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- W4387237316 abstract "Question answering (QA) systems which can support the English language have been improvised in the past few years using several techniques. Presently, we are tackling this problem for one of the Indic languages, Telugu. Telugu is one of the majority languages used by the southern states of India. Literature suggested that a few techniques for solving the challenges of QA system for Telugu language have been pro- posed, with limited use. In QA, complex information retrieval methods are utilized to extract information from all documents, with some models focusing solely on extracting the most important data. As a result, two strategies are commonly used in any QA system, information retrieval and extraction. The main aim of this paper is to build a machine reading comprehension (MRC) model for Telugu corpus. Presently our ideology is to be identified by keyword matching technique including the additional structures from the question as well as the answer. The distance between word embedding is used to find similarity and finally suggest the right answer to the user. Limited availability of Telugu corpus over the internet is the first challenge to be faced apart from other challenges. However, in the Telugu language, we might face structural ambiguity compared to English, and hence, choosing an efficient technique is also a difficult task to get a precise answer to the given question. We have generated/augmented the existing Telugu QA dataset ‘TyDiQA’ and fine-tuned attention-based transformer model in extracting relevant answer from a given context to wh-questions." @default.
- W4387237316 created "2023-10-02" @default.
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- W4387237316 date "2023-01-01" @default.
- W4387237316 modified "2023-10-16" @default.
- W4387237316 title "Domain Adaptation of Pretrained Models for Telugu Wh-Questions" @default.
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- W4387237316 doi "https://doi.org/10.1007/978-981-99-2746-3_26" @default.
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