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Improving Context Aware Language Models
[article]
2017
arXiv
pre-print
Increased adaptability of RNN language models leads to improved predictions that benefit many applications. However, current methods do not take full advantage of the RNN structure. We show that the most widely-used approach to adaptation (concatenating the context with the word embedding at the input to the recurrent layer) is outperformed by a model that has some low-cost improvements: adaptation of both the hidden and output layers. and a feature hashing bias term to capture context
arXiv:1704.06380v1
fatcat:xom3fnbitrc5hlhrte7g5l47yu