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Factoid Question Answering over Bangla Comprehension
- Source :
- 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT).
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
Abstract
- Question Answering (QA) framework is a data recovery framework in which an explicit reply is required because of a submitted query, as opposed to a lot of references that may contain the appropriate responses. It is a man-machine specialized gadget. The essential thought of QA frameworks in Natural Language Processing (NLP) is to give the right responses to the queries. This paper shows a step towards building a question answering framework in Bangla. A significant initial phase in building up a QA framework is the dataset. As the Bangla QA framework is at the beginning phase of advancement, we have prepared our own Bangla question answering dataset. The proposed model for Bangla-QA contains careful stacking of several deep learning-based sub-models to grasp the context from a comprehension and generate appropriate answers for diverse question types. We have accomplished up to 92.16% F1 score considering partial match and 76.8% exact match on test dataset utilizing LSTM.
Details
- Database :
- OpenAIRE
- Journal :
- 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)
- Accession number :
- edsair.doi...........b4a2aa9635fc025e58b0f44f90208a84