text: 209,74,1040574292449210368,806058672619212800,Guillaume Lample,"XNLI: Evaluating Cross-lingual Sentence Representations - @alex_conneau's #emnlp2018 new paper. Extends the NLI dataset to 15 languages, including low-resource ones such as Swahili and Urdu. <LINK> <LINK> If you had to classify Urdu sentences given English labeled data, what would you do between 1) translate the labeled data and train a Urdu classifier 2) translate Urdu sentences and feed them to the English classifier 3) use cross-lingual representations and a single classifier? For now, 2) works best, as cross-lingual representations are not perfect yet, and XNLI is a great way to evaluate this :)",https://arxiv.org/abs/1809.05053,"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. ",XNLI: Evaluating Cross-lingual Sentence Representations,3,"[""XNLI: Evaluating Cross-lingual Sentence Representations - @alex_conneau's #emnlp2018 new paper. Extends the NLI dataset to 15 languages, including low-resource ones such as Swahili and Urdu. <LINK> <LINK>"", 'If you had to classify Urdu sentences given English labeled data, what would you do between 1) translate the labeled data and train a Urdu classifier 2) translate Urdu sentences and feed them to the English classifier 3) use cross-lingual representations and a single classifier?', 'For now, 2) works best, as cross-lingual representations are not perfect yet, and XNLI is a great way to evaluate this :)']",18,09,606