Surface Realisation from Knowledge-Bases
Bikash Gyawali, Claire Gardent · 2014
We present a simple, data-driven approach to generation from knowledge bases (KB).A key feature of this approach is that grammar induction is driven by the extended domain of locality principle of TAG (Tree Adjoining Grammar); and that it takes into account both syntactic and semantic information.The resulting extracted TAG includes a unification based semantics and can be used by an existing surface realiser to generate sentences from KB data.Experimental evaluation on the KBGen data shows that our model outperforms a data-driven generate-and-rank approach based on an automatically induced probabilistic grammar; and is comparable with a handcrafted symbolic approach.