We propose a simple solution to use a single Neural Machine Translation (NMT) model to
translate between multiple languages. Our solution requires no changes to the model …

NLP has achieved great progress in the past decade through the use of neural models and
large labeled datasets. The dependence on abundant data prevents NLP models from being …

Cross-lingual representations of words enable us to reason about word meaning in
multilingual contexts and are a key facilitator of cross-lingual transfer when develo** …

Most existing machine translation systems operate at the level of words, relying on explicit
segmentation to extract tokens. We introduce a neural machine translation (NMT) model that …

As large language models (LLMs) gain popularity among speakers of diverse languages,
we believe that it is crucial to benchmark them to better understand model behaviors …

Cross-lingual transfer, where a high-resource transfer language is used to improve the
accuracy of a low-resource task language, is now an invaluable tool for improving …

Linguistic typology aims to capture structural and semantic variation across the world's
languages. A large-scale typology could provide excellent guidance for multilingual Natural …

Abstract We introduce Multi-SimLex, a large-scale lexical resource and evaluation
benchmark covering data sets for 12 typologically diverse languages, including major …

Many intelligent systems in business, government or academy process natural language as
an input during inference or they might even communicate with users in natural language …

In recent years, large language models (eg, Open AI's GPT-4, Meta's LLaMa, Google's
PaLM) have become the dominant approach for building AI systems to analyze and …