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Coherent discourse is distinguished from a mere collection of utterances bythe satisfaction of a diverse set of constraints, for example choice ofexpression, logical relation between denoted events, and implicit compatibilitywith world-knowledge. Do neural language models encode such constraints? Wedesign an extendable set of test suites addressing different aspects ofdiscourse and dialogue coherence. Unlike most previous coherence evaluationstudies, we address specific linguistic devices beyond sentence orderperturbations, allowing for a more fine-grained analysis of what constitutescoherence and what neural models trained on a language modelling objective doencode. Extending the targeted evaluation paradigm for neural language models(Marvin and Linzen, 2018) to phenomena beyond syntax, we show that thisparadigm is equally suited to evaluate linguistic qualities that contribute tothe notion of coherence.

Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language Models