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 …

Most existing methods on robust neural machine translation (NMT) construct adversarial
examples by injecting noise into authentic examples and indiscriminately exploit two types …

This survey provides an overview of the challenges of misspellings in natural language
processing (NLP). While often unintentional, misspellings have become ubiquitous in digital …

Neural Machine Translation models are sensitive to noise in the input texts, such as
misspelled words and ungrammatical constructions. Existing robustness techniques …

There are around 7000 languages that are alive worldwide; among them, only 50-200
languages are well-resourced. In many regions of the world, there are languages and …

Abstract Neural Machine Translation (NMT) yields remarkable results for high-resource
languages trained on large amounts of data. When there is a lack of linguistic data, the …

In this work, we analyze the robustness of neural machine translation systems towards
grammatical perturbations in the source. In particular, we focus on morphological inflection …