University of Cambridge > Talks.cam > NLIP Seminar Series > Extending a Surface Realizer to Generate Coherent Discourse

Extending a Surface Realizer to Generate Coherent Discourse

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If you have a question about this talk, please contact Johanna Geiss.

The ultimate aim of research on natural language generation is to develop large-scale, domain independent NLG systems, which are able to generate high quality, fluent and well-formatted texts. In order to produce high quality, coherent text, generators need to be able to model referential coherence and pronominalization, insert appropriate discourse connectives using appropriate constructions (e.g. preposed, postposed or interposed subordinate clauses) and provide a way for the user to specify which bits of information should be emphasized in the text.

Many NLG systems use a pipeline architecture where linguistic information is distributed across several system modules. These systems typically introduce additional modules (e.g. an aggregation or revision module) in order to model the above phenomena, resulting in more complex systems with limited flexibility. Using this approach, the research challenges in NLG become system engineering tasks, limited to questions such as: what modules should a system have, how should these modules be ordered, and how should the interactions between modules be handled.

In this talk I would like to present a slightly different perspective, where some of the research challenges in NLG are reformulated as grammar engineering tasks. I will argue that when linguistic resources in an NLG system are centralized we can model constraints on discourse coherence by simply incorporating more linguistic information into the grammar of a surface realizer. This approach improves the flexibility of the system (i.e. produces more paraphrases for the same input) and makes it possible to generate coherent text without additional modules.

This talk is part of the NLIP Seminar Series series.

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