Retrieval and Generative Approaches for a Pregnancy Chatbot in Nepali
  with Stemmed and Non-Stemmed Data : A Comparative Study
Sujan Poudel Nabin Ghimire Bipesh Subedi Saugat Singh
The field of Natural Language Processing which involves the use of artificial
intelligence to support human languages has seen tremendous growth due to its
high-quality features. Its applications such as language translation, chatbots,
virtual assistants, search autocomplete, and autocorrect are widely used in
various domains including healthcare, advertising, customer service, and target
advertising. To provide pregnancy-related information a health domain chatbot
has been proposed and this work explores two different NLP-based approaches for
developing the chatbot. The first approach is a multiclass classification-based
retrieval approach using BERTbased multilingual BERT and multilingual
DistilBERT while the other approach employs a transformer-based generative
chatbot for pregnancy-related information. The performance of both stemmed and
non-stemmed datasets in Nepali language has been analyzed for each approach.
The experimented results indicate that BERT-based pre-trained models perform
well on non-stemmed data whereas scratch transformer models have better
performance on stemmed data. Among the models tested the DistilBERT model
achieved the highest training and validation accuracy and testing accuracy of
0.9165 on the retrieval-based model architecture implementation on the
non-stemmed dataset. Similarly, in the generative approach architecture
implementation with transformer 1 gram BLEU and 2 gram BLEU scores of 0.3570
and 0.1413 respectively were achieved.