We share the findings of the first shared task on improving robustness of Machine
Translation (MT). The task provides a testbed representing challenges facing MT models …

Neural Machine Translation (NMT) models have been proved strong when translating clean
texts, but they are very sensitive to noise in the input. Improving NMT models robustness can …

Sarcasm is a term that refers to the use of words to mock, irritate, or amuse someone. It is
commonly used on social media. The metaphorical and creative nature of sarcasm presents …

Neural machine translation (NMT) is a deep learning based approach for machine
translation, which outperforms traditional statistical machine translation (SMT) and yields the …

While we live in an increasingly interconnected world, different places still exhibit strikingly
different cultures and many events we experience in our every day life pertain only to the …

In this paper, we look at the case of a Generic text-to-text NMT model that has to deal with
data coming from various modalities, like speech, images, or noisy text extracted from the …

The performance of neural machine translation (NMT) systems only trained on a single
language variant degrades when confronted with even slightly different language variations …

Abstract Resource scarcity in Neural Machine Translation is a challenging problem in both
industry applications and in the support of less-spoken languages represented, in the worst …

Neural Machine Translation (NMT) models are known to suffer from noisy inputs. To make
models robust, we generate adversarial augmentation samples that attack the model and …

Machine translation (MT), a branch of artificial intelligence and computational linguistics,
uses algorithms and models to automatically translate text from one language to another …