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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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.
These approaches corrupt the input by replacing some tokens with [MASK] and then train a model to reconstruct the original tokens.
Ranked #3 on Semantic Textual Similarity on STS Benchmark
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 #6 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.
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 #19 on Speech Recognition on LibriSpeech test-other
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.