Language modeling is the task of predicting the next word or character in a document.
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We introduce a new pre-trainable generic representation for visual-linguistic tasks, called Visual-Linguistic BERT (VL-BERT for short).
We encourage this temporal latent space to capture the 'intention' about how to complete the sentence by mimicking a representation which summarizes the future.
Recurrent Neural Network (RNN) and its variations such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), have become standard building blocks for learning online data of sequential nature in many research areas, including natural language processing and speech data analysis.
We use a language-model-based approach for pronoun resolution in combination with our WikiCREM dataset.
So we propose a two step approach "Mask and Infill".
Large pretrained language models have changed the way researchers approach discriminative natural language understanding tasks, leading to the dominance of approaches that adapt a pretrained model for arbitrary downstream tasks.
Clinical text structuring is a critical and fundamental task for clinical research.
Most work on models for music transcription has focused on describing local sequential dependence of notes in musical scores and failed to capture their global repetitive structure, which can be a useful guide for transcribing music.