While it has been established that transitions between discourse relations are important for coherence, such information has not so far been used to aid in language generation. We introduce an approach to discourse planning for concept-to-text generation systems which simultaneously determines the order of messages and the discourse relations between them. This approach makes it straightforward to use statistical transition models, such as n-gram models of discourse relations learned from an annotated corpus. We show that using such a model significantly improves the quality of the generated text as judged by humans.
展开▼
