MCQLL meets on Wednesdays at 1:00 in room 117. This week, Alessandro Sordoni will be discussing Natural Language Inference datasets such as MNLI, or paraphrase corpora such as QQP. NLU models trained on those datasets become usually brittle when they are tested on examples that are still grammatically correct but slightly out-of-distribution (see HANS and PAWS datasets). This talk presents preliminary results on how one can train state-of-the-art natural language understanding models on MNLI and QQP such that the resulting model is more robust when tested on OOD data. A useful starting point is this paper: