W eb NLG 2020 Challenge: Semantic Template Mining for Generating References from RDF (2020)

Abstract: 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.

## introduction natural language generation (nlg) plays a critical role in the modern era. researchers proposed different approaches to generate highquality text from input structured data in different domains @xcite @xcite @xcite @xcite @xcite @xcite . especially, with the growing need in the semantic web (sw) communities, there are the requirements for nlg works to provide a natural means for presenting this data in an organized, coherent and accessible way. in the first major task of webnlg 2020 1 shared task, the organizer provided english dataset for training which comprises data-text pairs for 16 distinct dbpedia 2 categories. each entry in the dataset comprises a resource description framework 3 (rdf) triple set paired with several natural language (nl) references. we illustrated an example of an english entry in the training data 1 in rdf @xmath0 ## background knowledge the main content of this section is to clarify the background knowledge that we apply in the research. the first part is to present flat triple meaning representation (mr) which is an intermediate structure to express core information from input rdf xml data. the second part is to present jaro-winkler similarity, which is used to find the phrases which have the similarity content and then we determine which one should be selected to form the templates. the third part is to present aliased parameters and semantic template which are the core information in our system. ## semantic template mining system we present in this section the principal components of our system. as illustrated in the general architecture in fig. 1 , there are two processes to perform: ## experiment and evaluation the organizer provided the testing data with three types of characteristics: ## conclusion we have presented our mining system for generating english natural language references from xml rdf structure. our approach has three most important sub-tasks: (i) extract the core information from input rdf; (ii) detect the group for each relationship and aliases for its parameters and generate semantic template; (iii) determine new information and augment to the warehouse. the evaluation results show that our approach overcomes the requirements: (i) references have lexical richness, syntactic variation, and discourse phenomena; (ii) references cover whole contents from the input rdf structure. in future works, we intend to apply techniques in deep learning and knowledge in linguistic theories to improve the quality and naturalness of generated texts. besides, we expand our approach to other datasets for a broader comparison.