THE COMPARATIVE ANALYSIS WITH BERT AND ELMOMETHODS FORMOVIE REVIEWS PREDICTION USING NLP
ELMo follows more traditional design and uses LSTMs for its computation of word embeddings while state of the art BERT uses Transformers which involves in two approach for the language modellng. The ELMo uses two layers each of them consists of forward and backward pass to compute the intermediate word vectors. It helps the ELMo to achieve high efficiency compared to other traditional language models. On the other hand, the BERT uses the transformer for the same thing the ELMo does. But instead of having two separate forward and backward pass layer, it uses single layer and analyses the a given sentence from both sides using the unique masking technique. In the masking technique random words are masked in order to prevent the model to see the word indirectly to itself only. The dataset IMDB Large Movie Review Dataset used to compare the two different models is a well balanced dataset which is hosted by the Stanford University, US. It consists of variety of strings that help us to check performance of the language model in handling different context of the situation. The main comparison factor in it is the accuracy which is the benchmark of this research.