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Vision outlooker improves the performance of vision transformers, whichimplements a self-attention mechanism by adding an outlook attention, a form oflocal attention.  In natural language processing, as has been the case in computer vision andother domains, transformer-based models constitute the state-of-the-art formost processing tasks. In this domain, too, many authors have argued anddemonstrated the importance of local context.  We present an outlook attention mechanism, COOL, for natural languageprocessing. COOL, added on top of the self-attention layers of atransformer-based model, encodes local syntactic context considering wordproximity and more pair-wise constraints than dynamic convolution used byexisting approaches.  A comparative empirical performance evaluation of an implementation of COOLwith different transformer-based models confirms the opportunity forimprovement over a baseline using the original models alone for various naturallanguage processing tasks, including question answering. The proposed approachachieves competitive performance with existing state-of-the-art methods on sometasks.

COOL, a Context Outlooker, and its Application to Question Answering and other Natural Language Processing Tasks