Abstract Machine translation (MT) is an important sub-field of natural language processing
that aims to translate natural languages using computers. In recent years, end-to-end neural …

Large pre-trained language models have been shown to store factual knowledge in their
parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks …

Large pre-trained language models (LMs) such as GPT-3 have acquired a surprising ability
to perform zero-shot learning. For example, to classify sentiment without any training …

Human commonsense understanding of the physical and social world is organized around
intuitive theories. These theories support making causal and moral judgments. When …

Neural machine translation (NMT) often suffers from the vulnerability to noisy perturbations
in the input. We propose an approach to improving the robustness of NMT models, which …

Natural Language Generation (NLG) has made great progress in recent years due to the
development of deep learning techniques such as pre-trained language models. This …

Transfer learning with large pretrained transformer-based language models like BERT has
become a dominating approach for most NLP tasks. Simply fine-tuning those large language …

Neural Machine Translation (NMT) models are sensitive to small perturbations in the input.
Robustness to such perturbations is typically measured using translation quality metrics …

The Machine Translation System (MTS) serves as effective tool for communication by
translating text or speech from one language to another language. Recently, neural machine …

Neural Machine Translation (NMT) heavily depends on the context vectors generated via
attention network for the target word prediction. Existing works primarily focus on generating …