Split and rephrase is the task of breaking down a sentence into shorter ones that together convey the same meaning.We extract a rich new dataset for this task by mining Wikipedia's edit history: WikiSplit contains one million naturally occurring sentence rewrites, providing sixty times more distinct split examples and a ninety times larger vocabulary than the WebSplit corpus introduced by Narayan et al. (2017) as a benchmark for this task.Incorporating WikiSplit as training data produces a model with qualitatively better predictions that score 32 BLEU points above the prior best result on the WebSplit benchmark.

Deep neural networks (DNNs) have recently become the state of the art technology in speech recognition systems. In this pa-per we propose a new approach to constructing large high qual-ity unsupervised sets to train DNN models for large vocabulary speech recognition. The core of our technique consists of two steps. We first redecode speech logged by our production rec-ognizer with a very accurate (and hence too slow for real-time usage) set of speech models to improve the quality of ground truth transcripts used for training alignments. Using confidence scores, transcript length and transcript flattening heuristics de-signed to cull salient utterances from three decades of speech per language, we then carefully select training data sets consist-ing of up to 15K hours of speech to be used to train acoustic models without any reliance on manual transcription. We show that this approach yields models with approximately 18K con-text dependent states that achieve 10 % relative improvement in large vocabulary dictation and voice-search systems for Brazil-ian Portuguese, French, Italian and Russian languages. Index Terms: large unsupervised training sets, data selection,