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 …

Deep learning models have achieved great success in solving a variety of natural language
processing (NLP) problems. An ever-growing body of research, however, illustrates the …

Character-based neural machine translation (NMT) models alleviate out-of-vocabulary
issues, learn morphology, and move us closer to completely end-to-end translation systems …

The visual classification performance of vision-language models such as CLIP has been
shown to benefit from additional semantic knowledge from large language models (LLMs) …

High-performance neural language models have obtained state-of-the-art results on a wide
range of Natural Language Processing (NLP) tasks. However, results for common …

This paper presents TransRepair, a fully automatic approach for testing and repairing the
consistency of machine translation systems. TransRepair combines mutation with …

Machine translation plays an essential role in people's daily international communication.
However, machine translation systems are far from perfect. To tackle this problem …

Contemporary machine translation systems achieve greater coverage by applying subword
models such as BPE and character-level CNNs, but these methods are highly sensitive to …

Natural Language Inference (NLI) datasets often contain hypothesis-only biases---artifacts
that allow models to achieve non-trivial performance without learning whether a premise …

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 …