Implicit discourse relations bind smaller linguistic units into coherent
texts. Automatic sense prediction for implicit relations is hard, because it
requires understanding the semantics of the linked arguments. Furthermore,
annotated datasets contain relatively few labeled examples, due to the scale of
the phenomenon: on average each discourse relation encompasses several dozen
words. In this paper, we explore the utility of pre-trained sentence embeddings
as base representations in a neural network for implicit discourse relation
sense classification. We present a series of experiments using both supervised
end-to-end trained models and pre-trained sentence encoding techniques -
SkipThought, Sent2vec and Infersent. The pre-trained embeddings are competitive
with the end-to-end model, and the approaches are complementary, with combined
models yielding significant performance improvements on two of the three
evaluations.

本文针对隐含的语篇关系进行了意义预测，利用预训练的句子嵌入作为神经网络基础表示，分别使用监督学习和预训练编码技术 - SkipThought，Sent2vec 和 Infersent 进行实验，并证实两种方法可以相互补充，结合模型可以显著提高模型性能。