Papers by Ivan Sanchez

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Challenge:	Currently, state-of-the-art models achieve impressive test set performance in the form of accuracy scores.
Approach:	They examine the models' robustness to semantically-valid alterations to the input data by identifying three factors and comparing their impact on three SNLI models.
Outcome:	The proposed models show that they can generalise to new in-domain instances while also showing that they suffer from insensitivity to small but semantically significant alterations.