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Natural language processing (NLP) tasks tend to suffer from a paucity of suitably annotated training data, hence the recent success of transfer learning across a wide variety of them. The typical recipe involves: (i) training a deep, possibly bidirectional, neural network with an objective related to language modeling, for which training data is plentiful; and (ii) using the trained network to derive contextual representations that are far richer than standard linear word embeddings such as word2vec, and thus result in important gains. In this work, we wonder whether the opposite perspective is also true: can contextual representations trained for different NLP tasks improve language modeling itself? Since language models (LMs) are predominantly locally optimized, other NLP tasks may help them make better predictions based on the entire semantic fabric of a document. We test the performance of several types of pre-trained embeddings in neural LMs, and we investigate whether it is possible to make the LM more aware of global semantic information through embeddings pre-trained with a domain classification model. Initial experiments suggest that as long as the proper objective criterion is used during training, pre-trained embeddings are likely to be beneficial for neural language modeling.

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Interspeech 2019

Apple attended Interspeech 2019, the world's largest conference on the science and technology of spoken language processing. The conference took place in Graz, Austria from September 15th to 19th. See accepted papers below.

Apple continues to build cutting-edge technology in the space of machine hearing, speech recognition, natural language processing, machine translation, text-to-speech, and artificial intelligence, improving the lives of millions of customers every day.

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Can Global Semantic Context Improve Neural Language Models?

Entering text on your iPhone, discovering news articles you might enjoy, finding out answers to questions you may have, and many other language-related tasks depend upon robust natural language processing (NLP) models. Word embeddings are a category of NLP models that mathematically map words to numerical vectors. This capability makes it fairly straightforward to find numerically similar vectors or vector clusters, then reverse the mapping to get relevant linguistic information. Such models are at the heart of familiar apps like News, search, Siri, keyboards, and Maps. In this article, we explore whether we can improve word predictions for the QuickType keyboard using global semantic context.

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