Data-centric AI is at the center of a fundamental shift in software engineering where machine
learning becomes the new software, powered by big data and computing infrastructure …

Deep neural networks and huge language models are becoming omnipresent in natural
language applications. As they are known for requiring large amounts of training data, there …

Fine-tuned pre-trained language models (LMs) have achieved enormous success in many
natural language processing (NLP) tasks, but they still require excessive labeled data in the …

Cross-modal retrieval has been a compelling topic in the multimodal community. Recently,
to mitigate the high cost of data collection, the co-occurred pairs (eg, image and text) could …

Sentiment analysis is the computational study of people's opinions, sentiments, emotions,
moods, and attitudes. This fascinating problem offers numerous research challenges, but …

Aspect-based sentiment analysis (ABSA) aims at determining the sentiment polarity of the
given aspect term in a sentence. Recently, graph convolution network (GCN) has been used …

We study the problem of learning neural text classifiers without using any labeled data, but
only easy-to-provide rules as multiple weak supervision sources. This problem is …

Multilingual transformer models like mBERT and XLM-RoBERTa have obtained great
improvements for many NLP tasks on a variety of languages. However, recent works also …

The end-to-end aspect-based sentiment analysis (ABSA) task remains to be a long-standing
challenge, which aims to extract the aspect term and then identify its sentiment orientation. In …

While there has been substantial progress in developing systems to automate fact-checking,
they still lack credibility in the eyes of the users. Thus, an interesting approach has emerged …