Investigating the use of deep learning for textual entailment in BRACU-NLI dataset
Abstract
This work aims to analyze the potential of deep neural models for text-based entailment
in Bangla Language. Entailment is the method of determining whether one
text infers or goes against another text. The study concentrates on the application
of deep learning methods, such as Recurrent Neural Networks (RNNs), BERT, GPT
for solving text-based entailment. The neural network method is trained to foretell
the relationship between two text sequences, such as whether one text sequence entails
the other or whether one text sequence provides evidence for the other. Other
tasks, such as question answering, can also be tackled by fine-tuning these models
on specific datasets. The findings of this work will contribute to the development of
further developed NLP systems that can perform complex reasoning and entailment
tasks.