148 datasets found

Generated template sentences for a variety of relationships in French, Italian, and Spanish, using the format “OCCUPATION RELATIONSHIP-VERB RELATIONSHIP-TARGET.” HIS/HER

The dataset used for transliterating person names from English to Hebrew, supporting both backward transliteration of Hebrew names and Sideways Transliteration of Arabic names.

The IWSLT 2014 shared task dataset contains 152K, 156K, 141K and 172K training sentences for the de-en, zh-en, en-tr and en-es language pairs, respectively.

The Xlnet is a generalized autoregressive pretraining model for language understanding.

Robert is a robustly optimized BERT pre-training approach.

MARGE is a pre-trained sequence-to-sequence model learned with an unsupervised multi-lingual multi-document paraphrasing objective.

Character-level neural machine translation (NMT) dataset for English to German, English to Czech and English to Finnish language pairs

The dataset used in the paper is a subset of the OPUS-MT dataset, containing 1M randomly sampled examples from the OPUS-100 dataset.

LNMap: Departures from isomorphic assumption in bilingual lexicon induction through non-linear mapping in latent space.

Learning principled bilingual mappings of word embeddings while preserving monolingual invariance.

Bilingual lexicon induction induces the word translations by aligning independently trained word embeddings in two languages.

The dataset used for the experiments of simultaneous neural machine translation.

The dataset used for the experiments of simultaneous neural machine translation.

The dataset used for the experiments of simultaneous neural machine translation.

Non-autoregressive machine translation dataset

The WMT14 En-De dataset contains 4.5M pairs of English and German sentences.

The paraphrased reference translations used for the experiments in the paper.

The paraphrased reference translations used for the experiments in the paper.

The dataset used for the experiments in the paper, containing English-German news translation task.

The dataset is used for training and testing the proposed punctuation restoration and inverse text normalization models.