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        <title>Exploring Expected Answer Types for Effective Question Answering Systems for low resource language</title>
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        <namePart type="given">Chindukuri</namePart>
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            <namePart type="given">Sobha</namePart>
            <namePart type="family">Lalitha Devi</namePart>
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            <namePart type="given">Karunesh</namePart>
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    <abstract>Question-answering (QA) systems play a pivotal role in natural language processing (NLP), powering applications such as search engines and virtual assistants by providing accurate responses to user queries. However, building effective QA systems for Dravidian languages, like Tamil, poses distinct challenges due to the scarcity of resources and the linguistic complexities inherent to these languages. This paper introduces a novel method to enhance QA accuracy by integrating answer-type features alongside traditional question and context inputs. We fine-tuned both mono- and multilingual pre-trained models on the Extended Chaii dataset, which comprises Tamil translations from the SQuAD dataset, as well as on the SQuAD-EAT-5000 dataset, consisting of English-language instances. Our experiments reveal that incorporating answer-type features significantly improves model performance compared to using only question and context inputs. Specifically, for the Extended Chaii dataset, the MuRIL model achieved the highest F1 score of 53.89, surpassing other pre-trained models, while RoBERTa outperformed BERT on the SQuAD-EAT-5000 dataset with a score of 82.07. This research advances QA systems for Dravidian languages and underscores the importance of integrating linguistic features for improved accuracy.</abstract>
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%0 Conference Proceedings
%T Exploring Expected Answer Types for Effective Question Answering Systems for low resource language
%A Mallikarjuna, Chindukuri
%A Sivanesan, Sangeetha
%Y Lalitha Devi, Sobha
%Y Arora, Karunesh
%S Proceedings of the 21st International Conference on Natural Language Processing (ICON)
%D 2024
%8 December
%I NLP Association of India (NLPAI)
%C AU-KBC Research Centre, Chennai, India
%F mallikarjuna-sivanesan-2024-exploring
%X Question-answering (QA) systems play a pivotal role in natural language processing (NLP), powering applications such as search engines and virtual assistants by providing accurate responses to user queries. However, building effective QA systems for Dravidian languages, like Tamil, poses distinct challenges due to the scarcity of resources and the linguistic complexities inherent to these languages. This paper introduces a novel method to enhance QA accuracy by integrating answer-type features alongside traditional question and context inputs. We fine-tuned both mono- and multilingual pre-trained models on the Extended Chaii dataset, which comprises Tamil translations from the SQuAD dataset, as well as on the SQuAD-EAT-5000 dataset, consisting of English-language instances. Our experiments reveal that incorporating answer-type features significantly improves model performance compared to using only question and context inputs. Specifically, for the Extended Chaii dataset, the MuRIL model achieved the highest F1 score of 53.89, surpassing other pre-trained models, while RoBERTa outperformed BERT on the SQuAD-EAT-5000 dataset with a score of 82.07. This research advances QA systems for Dravidian languages and underscores the importance of integrating linguistic features for improved accuracy.
%U https://aclanthology.org/2024.icon-1.2/
%P 12-20

Markdown (Informal)

[Exploring Expected Answer Types for Effective Question Answering Systems for low resource language](https://aclanthology.org/2024.icon-1.2/) (Mallikarjuna & Sivanesan, ICON 2024)

• Exploring Expected Answer Types for Effective Question Answering Systems for low resource language (Mallikarjuna & Sivanesan, ICON 2024)

ACL

• Chindukuri Mallikarjuna and Sangeetha Sivanesan. 2024. Exploring Expected Answer Types for Effective Question Answering Systems for low resource language. In Proceedings of the 21st International Conference on Natural Language Processing (ICON), pages 12–20, AU-KBC Research Centre, Chennai, India. NLP Association of India (NLPAI).