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On this paper, we illustrate this scenario for a trending NLP task: Natural Language Inference (NLI). On this paper, we take a step towards explaining such deep learning primarily based models by means of a case research on a well-liked neural mannequin for NLI. We discover that the results of this work are robust: All variants of the model beneath study outperform all latent tree studying baselines, and perform competitively with symbolic grammar induction systems.
We find that sharing transition classifier parameters at all times helps, whereas the usefulness of sharing phrase and/or character LSTM parameters varies. We find that this model represents the primary empirical success for latent tree learning, and that neural network language modeling warrants further research as a setting for grammar induction.
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