Intent classification, to identify the speaker's intention, and slot filling, to label each token
with a semantic type, are critical tasks in natural language understanding. Traditionally the …

With the support of advanced hardware and software technology, Artificial Intelligence (AI)
techniques, especially the increasing number of deep learning algorithms, have spawned …

Effective learning schemes such as fine-tuning, zero-shot, and few-shot learning, have been
widely used to obtain considerable performance with only a handful of annotated training …

Emotion detection from text is a crucial task in understanding natural language with wide-
ranging applications. Existing approaches for multilingual emotion detection from text face …

[en] Pre-trained Language Models such as BERT have become ubiquitous in NLP where
they have achieved state-of-the-art performance in most NLP tasks. While these models are …

Training large language models is challenging when data availability is limited, as it is the
case for low-resource languages. We investigate different data augmentation techniques for …

Intent recognition aids conversational AI in understanding the meaning of user messages,
which has to be realised in multiple languages for multilingual scenarios. In this paper, we …

Abstract Machine Translation (MT)-empowered chatbots are not established yet, however,
we see an amazing future breaking language barriers and enabling conversation in multiple …

This paper presents a novel approach for finding the most semantically similar
conversational sentences in Korean and English. Our method involves training separate …

Despite the widespread use of pre-trained models in NLP, well-performing pre-trained
models for low-resource languages are scarce. To address this issue, we propose two novel …