Humans are increasingly integrated with devices that enable the collection of vast
unstructured opinionated data. Accurately analysing subjective information from this data is …

In this work we explore Unsupervised Domain Adaptation (UDA) of pretrained language
models for downstream tasks. We introduce UDALM, a fine-tuning procedure, using a mixed …

Abstract Cross-domain Aspect-Based Sentiment Analysis (ABSA) aims to leverage the
useful knowledge from a source domain to identify aspect-sentiment pairs in sentences from …

Recent advances in NLP are brought by a range of large-scale pretrained language models
(PLMs). These PLMs have brought significant performance gains for a range of NLP tasks …

Supervised learning methods have proven to be effective for Aspect-Based Sentiment
Analysis (ABSA). However, the lack of finegrained labeled data hinders their effectiveness in …

In cross-lingual named entity recognition (NER), self-training is commonly used to bridge the
linguistic gap by training on pseudo-labeled target-language data. However, due to sub …

Abstract Models such as mBERT and XLMR have shown success in solving Code-Mixed
NLP tasks even though they were not exposed to such text during pretraining. Code-Mixed …

Fine-tuning a large multi-lingual pretrained language model demonstrates impressive
results in cross-language understanding. However, it still suffers when the training and test …

Language features are evolving in real-world social media, resulting in the deteriorating
performance of text classification in dynamics. To address this challenge, we study temporal …

In this work, we study Unsupervised Domain Adaptation (UDA) in a challenging self-
supervised approach. One of the difficulties is how to learn task discrimination in the …