A Cross-Domain Transferable Neural Coherence Model
This work addresses the challenge of ensuring text coherence across different domains, which is crucial for improving readability in applications like summarization and content generation, though it appears incremental as it builds on existing discriminative approaches.
The authors tackled the problem of cross-domain generalization in neural coherence models by proposing a local discriminative model with a smaller negative sampling space, which significantly outperformed previous state-of-the-art methods on standard benchmarks and in transfer to unseen categories.
Coherence is an important aspect of text quality and is crucial for ensuring its readability. One important limitation of existing coherence models is that training on one domain does not easily generalize to unseen categories of text. Previous work advocates for generative models for cross-domain generalization, because for discriminative models, the space of incoherent sentence orderings to discriminate against during training is prohibitively large. In this work, we propose a local discriminative neural model with a much smaller negative sampling space that can efficiently learn against incorrect orderings. The proposed coherence model is simple in structure, yet it significantly outperforms previous state-of-art methods on a standard benchmark dataset on the Wall Street Journal corpus, as well as in multiple new challenging settings of transfer to unseen categories of discourse on Wikipedia articles.