Discourse Planning with an N-gram Model of Relations Or Biran

While it has been established that transi-tions between discourse relations are im-portant for coherence, such information has not so far been used to aid in lan-guage generation. We introduce an ap-proach to discourse planning for concept-to-text generation systems which simul-taneously determines the order of mes-sages and the discourse relations between them. This approach makes it straightfor-ward to use statistical transition models, such as n-gram models of discourse re-lations learned from an annotated corpus. We show that using such a model signif-icantly improves the quality of the gener-ated text as judged by humans.