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This paper presents Masked ELMo, a new RNN-based model for language modelpre-training, evolved from the ELMo language model. Contrary to ELMo which onlyuses independent left-to-right and right-to-left contexts, Masked ELMo learnsfully bidirectional word representations. To achieve this, we use the sameMasked language model objective as BERT. Additionally, thanks to optimizationson the LSTM neuron, the integration of mask accumulation and bidirectionaltruncated backpropagation through time, we have increased the training speed ofthe model substantially. All these improvements make it possible to pre-train abetter language model than ELMo while maintaining a low computational cost. Weevaluate Masked ELMo by comparing it to ELMo within the same protocol on theGLUE benchmark, where our model outperforms significantly ELMo and iscompetitive with transformer approaches.

Masked ELMo: An evolution of ELMo towards fully contextual RNN language models