  We consider the task of incorporating real-world commonsense knowledge into
deep Natural Language Inference (NLI) models. Existing external knowledge
incorporation methods are limited to lexical level knowledge and lack
generalization across NLI models, datasets, and commonsense knowledge sources.
To address these issues, we propose a novel NLI model-independent neural
framework, BiCAM. BiCAM incorporates real-world commonsense knowledge into NLI
models. Combined with convolutional feature detectors and bilinear feature
fusion, BiCAM provides a conceptually simple mechanism that generalizes well.
Quantitative evaluations with two state-of-the-art NLI baselines on SNLI and
SciTail datasets in conjunction with ConceptNet and Aristo Tuple KGs show that
BiCAM considerably improves the accuracy the incorporated NLI baselines. For
example, our BiECAM model, an instance of BiCAM, on the challenging SciTail
dataset, improves the accuracy of incorporated baselines by 7.0% with
ConceptNet, and 8.0% with Aristo Tuple KG.

Computation and Language;Machine Learning