The field of natural language processing has seen impressive progress in recent years, with
neural network models replacing many of the traditional systems. A plethora of new models …

The field of machine translation (MT), the automatic translation of written text from one
natural language into another, has experienced a major paradigm shift in recent years …

Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant,
and fluent answers for various natural language processing (NLP) tasks. Taking document …

We present the first challenge set and evaluation protocol for the analysis of gender bias in
machine translation (MT). Our approach uses two recent coreference resolution datasets …

Current state-of-the-art NMT systems use large neural networks that are not only slow to
train, but also often require many heuristics and optimization tricks, such as specialized …

Natural language inference (NLI) is the task of determining if a natural language hypothesis
can be inferred from a given premise in a justifiable manner. NLI was proposed as a …

Neural Machine Translation (NMT) can be improved by including document-level contextual
information. For this purpose, we propose a hierarchical attention model to capture the …

Standard machine translation systems process sentences in isolation and hence ignore
extra-sentential information, even though extended context can both prevent mistakes in …

Although the Transformer translation model (Vaswani et al., 2017) has achieved state-of-the-
art performance in a variety of translation tasks, how to use document-level context to deal …

Though machine translation errors caused by the lack of context beyond one sentence have
long been acknowledged, the development of context-aware NMT systems is hampered by …