  In implicit discourse relation classification, we want to predict the
relation between adjacent sentences in the absence of any overt discourse
connectives. This is challenging even for humans, leading to shortage of
annotated data, a fact that makes the task even more difficult for supervised
machine learning approaches. In the current study, we perform implicit
discourse relation classification without relying on any labeled implicit
relation. We sidestep the lack of data through explicitation of implicit
relations to reduce the task to two sub-problems: language modeling and
explicit discourse relation classification, a much easier problem. Our
experimental results show that this method can even marginally outperform the
state-of-the-art, in spite of being much simpler than alternative models of
comparable performance. Moreover, we show that the achieved performance is
robust across domains as suggested by the zero-shot experiments on a completely
different domain. This indicates that recent advances in language modeling have
made language models sufficiently good at capturing inter-sentence relations
without the help of explicit discourse markers.
