Language models (LMs) have been used in cognitive modeling as well as engineering studies -- they compute information-theoretic complexity metrics that simulate humans' cognitive load during reading.
We present Semi-Structured Explanations for COPA (COPA-SSE), a new crowdsourced dataset of 9, 747 semi-structured, English common sense explanations for Choice of Plausible Alternatives (COPA) questions.
Here, we propose to explicitly learn a model that does well on both the easy test set with superficial cues and hard test set without superficial cues.
The writing process consists of several stages such as drafting, revising, editing, and proofreading.
This paper describes our system for the SemEval-2018 Task 12: Argument Reasoning Comprehension Task.