Natural Language Understanding is an important field of Natural Language Processing which contains various tasks such as text classification, natural language inference and story comprehension.
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Our cell achieves a test set perplexity of 62. 4 on the Penn Treebank, which is 3. 6 perplexity better than the previous state-of-the-art model.
Then, instead of training a model that predicts the original identities of the corrupted tokens, we train a discriminative model that predicts whether each token in the corrupted input was replaced by a generator sample or not.
Unsupervised pre-training of large neural models has recently revolutionized Natural Language Processing.
On unsupervised machine translation, we obtain 34. 3 BLEU on WMT'16 German-English, improving the previous state of the art by more than 9 BLEU.
We demonstrate that large gains on these tasks can be realized by generative pre-training of a language model on a diverse corpus of unlabeled text, followed by discriminative fine-tuning on each specific task.
Ranked #7 on Natural Language Inference on SNLI
We present a new neural architecture for wide-coverage Natural Language Understanding in Spoken Dialogue Systems.
We have recently seen the emergence of several publicly available Natural Language Understanding (NLU) toolkits, which map user utterances to structured, but more abstract, Dialogue Act (DA) or Intent specifications, while making this process accessible to the lay developer.
In this work we present Ludwig, a flexible, extensible and easy to use toolbox which allows users to train deep learning models and use them for obtaining predictions without writing code.
IMAGE CAPTIONING IMAGE CLASSIFICATION LANGUAGE MODELLING MACHINE TRANSLATION MULTI-LABEL CLASSIFICATION MULTI-TASK LEARNING NAMED ENTITY RECOGNITION NATURAL LANGUAGE UNDERSTANDING ONE-SHOT LEARNING SENTIMENT ANALYSIS SPEAKER VERIFICATION TEXT CLASSIFICATION TIME SERIES FORECASTING VISUAL QUESTION ANSWERING
This paper presents the machine learning architecture of the Snips Voice Platform, a software solution to perform Spoken Language Understanding on microprocessors typical of IoT devices.
Ranked #23 on Speech Recognition on LibriSpeech test-other