An Empirical Study on End-to-End Sentence Modelling
نویسنده
چکیده
Accurately representing the meaning of a piece of text, otherwise known as sentence modelling, is an important component in many natural language inference tasks. We survey the spectrum of these methods, which lie along two dimensions: input representation granularity and composition model complexity. Using this framework, we reveal in our quantitative and qualitative experiments the limitations of the current state-of-the-art model in the context of sentence similarity tasks.
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