Intent Classification
Large language models (LLM) have achieved remarkable success in natural language generation but lesser focus has been given to their …
In today’s digitally driven world, dialogue systems play a pivotal role in enhancing user interactions, from customer service to …
Data augmentation is a widely used technique to address the problem of
text classification when there is a limited amount of training …
TK-KNN: A Balanced Distance-Based Pseudo Labeling Approach for Semi-Supervised Intent Classification
The ability to detect intent in dialogue systems has become increasingly important in modern technology.
These systems often generate a …
In-context learning (ICL) using large language models for tasks with many labels is challenging due to the limited context window, …
Data augmentation is a widely employed technique to alleviate the problem of data scarcity. In this work, we propose a prompting-based …
Data augmentation alleviates the problem of data scarcity when training language models (LMs) by generating new examples based on the …