Language modeling is the task of predicting the next word or character in a document.
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Successful application processing sequential data, such as text and speech, requires an improved generalization performance of recurrent neural networks (RNNs).
On LibriSpeech, we achieve 6. 8% WER on test-other without the use of a language model, and 5. 8% WER with shallow fusion with a language model.
This research note combines two methods that have recently improved the state of the art in language modeling: Transformers and dynamic evaluation.
#2 best model for Language Modelling on enwiki8
This paper contributes a sober view of the problem, a survey of techniques to address it, novel techniques, and extensions to the model.
The nature of what people enjoy is not just a central question for the creative industry, it is a driving force of cultural evolution.
The proposed model does not require parallel text-summary pairs, achieving promising results in unsupervised sentence compression on benchmark datasets.
In this paper, we report state-of-the-art results on LibriSpeech among end-to-end speech recognition models without any external training data.
#2 best model for Speech Recognition on LibriSpeech test-clean