Language modeling is the task of predicting the next word or character in a document.
( Image credit: Exploring the Limits of Language Modeling )
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We introduce "talking-heads attention" - a variation on multi-head attention which includes linearprojections across the attention-heads dimension, immediately before and after the softmax operation. While inserting only a small number of additional parameters and a moderate amount of additionalcomputation, talking-heads attention leads to better perplexities on masked language modeling tasks, aswell as better quality when transfer-learning to language comprehension and question answering tasks.
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.
We propose a new benchmark corpus to be used for measuring progress in statistical language modeling.
Ranked #15 on Language Modelling on One Billion Word
Traditional NLP has long held (supervised) syntactic parsing necessary for successful higher-level language understanding.
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.
Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences.
Ranked #2 on Question Answering on Quasart-T
Language models have become a key step to achieve state-of-the art results in many different Natural Language Processing (NLP) tasks.
Large transformer-based language models (LMs) trained on huge text corpora have shown unparalleled generation capabilities.