Dialogue systems are becoming an ubiquitous presence in our everyday lives having a
huge impact on business and society. Spoken language understanding (SLU) is the critical …

Text classification is usually studied by labeling natural language texts with relevant
categories from a predefined set. In the real world, new classes might keep challenging the …

This paper investigates the effectiveness of pre-training for few-shot intent classification.
While existing paradigms commonly further pre-train language models such as BERT on a …

It is challenging to train a good intent classifier for a task-oriented dialogue system with only
a few annotations. Recent studies have shown that fine-tuning pre-trained language models …

The influence of social media is one of the most dominating characteristics of the current era,
and this has led cyberbullying to grow into a more serious social issue. As a result …

We consider the task of few-shot intent detection, which involves training a deep learning
model to classify utterances based on their underlying intents using only a small amount of …

Large Transformer-based natural language understanding models have achieved state-of-
the-art performance in dialogue systems. However, scarce labeled data for training, the …

Relation extraction (RE) aims to identify the relation between two entities within a sentence,
which plays a crucial role in information extraction. Traditional supervised setting on RE …

Few-shot intent detection is a challenging task due to the scare annotation problem. In this
paper, we propose a Pseudo Siamese Network (PSN) to generate labeled data for few-shot …

Few-shot intent detection aims to detect fast-emerging new intents from limited labeled
utterances, which attracts increasing attention recently. Although current few-shot learning …