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In implicit discourse relation classification, we want to predict therelation between adjacent sentences in the absence of any overt discourseconnectives. This is challenging even for humans, leading to shortage ofannotated data, a fact that makes the task even more difficult for supervisedmachine learning approaches. In the current study, we perform implicitdiscourse relation classification without relying on any labeled implicitrelation. We sidestep the lack of data through explicitation of implicitrelations to reduce the task to two sub-problems: language modeling andexplicit discourse relation classification, a much easier problem. Ourexperimental results show that this method can even marginally outperform thestate-of-the-art, in spite of being much simpler than alternative models ofcomparable performance. Moreover, we show that the achieved performance isrobust across domains as suggested by the zero-shot experiments on a completelydifferent domain. This indicates that recent advances in language modeling havemade language models sufficiently good at capturing inter-sentence relationswithout the help of explicit discourse markers.

Let's be explicit about that: Distant supervision for implicit discourse relation classification via connective prediction