Aspect-based sentiment analysis (ABSA) determines the sentiment polarity of specific
aspects mentioned in the review. However, some existing ABSA studies have limitations …

Federated learning (FL) is a decentralized machine learning (ML) framework that allows
models to be trained without sharing the participants' local data. FL thus preserves privacy …

End-to-end Aspect-based Sentiment Analysis (EASA) is a natural language processing
(NLP) task that involves extracting aspect terms and identifying the sentiments for them …

Aspect-level sentiment analysis is a more fine-grained task that aims to determine the
sentiment polarity of specific aspects. Recent studies have employed graph attention …

Abstract Aspect Category Sentiment Analysis (ACSA) involves identifying sentiment
categories for specific aspects of a sentence. Despite the progress made in pre-trained …

Despite the versatility of pre-trained language models (PLMs) across domains, their large
memory footprints pose significant challenges in federated learning (FL), where the training …

Most recent studies for relation extraction (RE) leverage the dependency tree of the input
sentence to incorporate syntax-driven contextual information to improve model performance …

To protect user privacy and meet legal regulations, federated learning (FL) is attracting
significant attention. Training neural machine translation (NMT) models with traditional FL …

Differential privacy (DP) enables model training with a guaranteed bound on privacy
leakage, therefore is widely adopted in federated learning (FL) to protect the model update …

Federated learning (FL) is a promising approach for solving multilingual tasks, potentially
enabling clients with their own language-specific data to collaboratively construct a high …