Linguistic Versus Latent Relations for Modeling Coherent Flow in Paragraphs
This work addresses the problem of improving text coherence for natural language generation applications, though it appears incremental by building on existing methods.
The paper tackled generating coherent paragraphs by modeling intersentential relations, comparing linguistic structures and latent representations, and found that both proposed models outperformed baselines in paragraph generation tasks.
Generating a long, coherent text such as a paragraph requires a high-level control of different levels of relations between sentences (e.g., tense, coreference). We call such a logical connection between sentences as a (paragraph) flow. In order to produce a coherent flow of text, we explore two forms of intersentential relations in a paragraph: one is a human-created linguistical relation that forms a structure (e.g., discourse tree) and the other is a relation from latent representation learned from the sentences themselves. Our two proposed models incorporate each form of relations into document-level language models: the former is a supervised model that jointly learns a language model as well as discourse relation prediction, and the latter is an unsupervised model that is hierarchically conditioned by a recurrent neural network (RNN) over the latent information. Our proposed models with both forms of relations outperform the baselines in partially conditioned paragraph generation task. Our codes and data are publicly available.