As an effective strategy, data augmentation (DA) alleviates data scarcity scenarios where
deep learning techniques may fail. It is widely applied in computer vision then introduced to …

Text Generation aims to produce plausible and readable text in human language from input
data. The resurgence of deep learning has greatly advanced this field, in particular, with the …

This paper focuses on the Data Augmentation for low-resource Natural Language
Understanding (NLU) tasks. We propose Prompt-based D} ata Augmentation model …

Abstract Large Language Models (LLMs) have performed well on various reasoning tasks,
but their inaccessibility and numerous parameters hinder wide application in practice. One …

Spoken dialogue systems (SDSs) have been separately developed under two different
categories, task-oriented and chit-chat. The former focuses on achieving functional goals …

In the deployment of real-world text classification models, label scarcity is a common
problem and as the number of classes increases, this problem becomes even more …

Aspect-based sentiment analysis (ABSA), a fine-grained sentiment classification task, has
received much attention recently. Many works investigate sentiment information through …

In this paper, we aim to generate text classification data given arbitrary class definitions (ie,
user instruction), so one can train a small text classifier without any human annotation or raw …

Dialogue systems need to produce responses that realize multiple types of dialogue acts
(DAs) with high semantic fidelity. In the past, natural language generators (NLGs) for …

Emotion recognition is a vital task within Natural Language Processing (NLP) that involves
automatically identifying emotions from text. As the need for specialized and nuanced …