Sensible at SemEval-2016 task 11: Neural nonsense mangled in ensemble mess

Abstract

This paper describes our submission to the Complex Word Identification (CWI) task in SemEval-2016. We test an experimental approach to blindly use neural nets to solve the CWI task that we know little/nothing about. By structuring the input as a series of sequences and the output as a binary that indicates 1 to denote complex words and 0 otherwise, we introduce a novel approach to complex word identification using Recurrent Neural Nets (RNN). We also show that it is possible to simply ensemble several RNN classifiers when we are unsure of the optimal hyper-parameters or the best performing models using eXtreme gradient boosted trees classifiers. Our systems submitted to the CWI task achieved the highest accuracy and F-score among the systems that uses neural networks.