Bengali Intent Classification with Generative Adversarial BERT
Mehedi Hasan Mohammad Jahid Ibna Basher Md. Tanvir Rouf Shawon
Intent classification is a fundamental task in natural language
understanding, aiming to categorize user queries or sentences into predefined
classes to understand user intent. The most challenging aspect of this
particular task lies in effectively incorporating all possible classes of
intent into a dataset while ensuring adequate linguistic variation. Plenty of
research has been conducted in the related domains in rich-resource languages
like English. In this study, we introduce BNIntent30, a comprehensive Bengali
intent classification dataset containing 30 intent classes. The dataset is
excerpted and translated from the CLINIC150 dataset containing a diverse range
of user intents categorized over 150 classes. Furthermore, we propose a novel
approach for Bengali intent classification using Generative Adversarial BERT to
evaluate the proposed dataset, which we call GAN-BnBERT. Our approach leverages
the power of BERT-based contextual embeddings to capture salient linguistic
features and contextual information from the text data, while the generative
adversarial network (GAN) component complements the model's ability to learn
diverse representations of existing intent classes through generative modeling.
Our experimental results demonstrate that the GAN-BnBERT model achieves
superior performance on the newly introduced BNIntent30 dataset, surpassing the
existing Bi-LSTM and the stand-alone BERT-based classification model.