Discourse coherence is strongly associated with text quality, making it
important to natural language generation and understanding. Yet existing models
of coherence focus on measuring individual aspects of coherence (lexical
overlap, rhetorical structure, entity centering) in narrow domains.
In this paper, we describe domain-independent neural models of discourse
coherence that are capable of measuring multiple aspects of coherence in
existing sentences and can maintain coherence while generating new sentences.
We study both discriminative models that learn to distinguish coherent from
incoherent discourse, and generative models that produce coherent text,
including a novel neural latent-variable Markovian generative model that
captures the latent discourse dependencies between sentences in a text.
Our work achieves state-of-the-art performance on multiple coherence
evaluations, and marks an initial step in generating coherent texts given
discourse contexts.

本研究提出了一种无域限制的神经模型，可用于度量多个方面的连贯性，并能在生成新语句时保持连贯性，该模型包括区分性模型和生成性模型，以及一种新的基于神经潜变量马尔科夫生成模型，可捕获文本中句子之间的隐含连贯性依赖关系，并在多个连贯性评估方面取得了最先进的表现，为处理语篇上下文生成连贯性文本迈出了一步。