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 …

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 …

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 …

Dialectal Arabic (DA) refers to varieties of everyday spoken languages in the Arab world.
These dialects differ according to the country and region of the speaker, and their textual …

Large language models (LLMs) have showcased their capability with few-shot inference
known as in-context learning. However, in-domain demonstrations are not always readily …

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 …

Abstract Cross-domain Named Entity Recognition (NER) transfers the NER knowledge from
high-resource domains to the low-resource target domain. Due to limited labeled resources …