This also validates the observation of (Chen et al., 2017b), which shows what the sub vector in the ESIM model is looking out for contradictory information.
our architecture allows the inspection of these vectors since they are compressed via factorization, leading to larger extents of explainability -a quality that neural models inherently lack.