  State-of-the-art natural language processing systems rely on supervision in
the form of annotated data to learn competent models. These models are
generally trained on data in a single language (usually English), and cannot be
directly used beyond that language. Since collecting data in every language is
not realistic, there has been a growing interest in cross-lingual language
understanding (XLU) and low-resource cross-language transfer. In this work, we
construct an evaluation set for XLU by extending the development and test sets
of the Multi-Genre Natural Language Inference Corpus (MultiNLI) to 15
languages, including low-resource languages such as Swahili and Urdu. We hope
that our dataset, dubbed XNLI, will catalyze research in cross-lingual sentence
understanding by providing an informative standard evaluation task. In
addition, we provide several baselines for multilingual sentence understanding,
including two based on machine translation systems, and two that use parallel
data to train aligned multilingual bag-of-words and LSTM encoders. We find that
XNLI represents a practical and challenging evaluation suite, and that directly
translating the test data yields the best performance among available
baselines.

Computation and Language;Artificial Intelligence;Machine Learning