An Automatic Method for Building a Data-to-Text Generator
نویسندگان
چکیده
We describe our contribution to the Generating from Knowledge Bases (KBgen) challenge. Our system is learned in a bottom-up fashion, by inducing a probabilistic grammar that represents alignments between strings and parts of a knowledge graph. From these alignments, we extract information about the linearization and lexical choices associated with the target knowledge base, and build a simple generate-and-rank system in the style of (Langkilde and Knight, 1998).1
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