XNLI: Evaluating Cross-lingual Sentence Representations

EMNLP 2018 Alexis Conneau • Guillaume Lample • Ruty Rinott • Adina Williams • Samuel R. Bowman • Holger Schwenk • Veselin Stoyanov

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.

Full paper

Evaluation


No evaluation results yet. Help compare this paper to other papers by submitting the tasks and evaluation metrics from the paper.