WebNLG 2020 Challenge: Semantic Template Mining for Generating References from RDF
We present in this paper our mining system for shared task WebNLG Challenge 2020. The general idea of the system is that we generate the semantic template of the output reference from the input RDF XML structure. In the training process, we perform the following subtasks: (i) extract the core information from input RDF; (ii) generate semantic templates from corresponding references. With new RDF XML data, we detect the core information, in turn add the new template into the warehouse and determine the output semantic template. We will evaluate the output natural language references in two processes: automatic and human evaluations. The results of the first tested process show that our system generates the high quality English descriptions from testing RDF XML structures and has a good contribution to the NLG state of the art.
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