Propositional and Non-at-issue Content in Text Generation: Exploring the QUD–Perspective

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Question under discussion (QUD)-based approaches to discourse structuring facilitate concise analyses of a bunch of different pragmatic phenomena but up to now, they did not receive much attention in research on natural language generation. We want to investigate the descriptive and explanatory strength of the QUD approach for the generation of short road test reports. Inverting the perspective from interpretation to generation raises several new computationally and theoretically motivated research questions concerning the justification of assumed QUDs and QUD structures.For this, we investigate the conditions for establishing discourse relations, connectives and realizing corresponding discourse markers, sentence aggregation, the use of evaluative adverbs, and determining the focus, since these tasks for content determination and linguistic realization can, in principle, be derived from corresponding QUD-based theories of discourse structuring.We develop an NLG system as proof-of-concept that generates short road test reports about cars and motorbikes from information in a database and QUDs derived from texts.