Georgios Meditskos


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LARD: Large-scale Artificial Disfluency Generation
Tatiana Passali | Thanassis Mavropoulos | Grigorios Tsoumakas | Georgios Meditskos | Stefanos Vrochidis
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Disfluency detection is a critical task in real-time dialogue systems. However, despite its importance, it remains a relatively unexplored field, mainly due to the lack of appropriate datasets. At the same time, existing datasets suffer from various issues, including class imbalance issues, which can significantly affect the performance of the model on rare classes, as it is demonstrated in this paper. To this end, we propose LARD, a method for generating complex and realistic artificial disfluencies with little effort. The proposed method can handle three of the most common types of disfluencies: repetitions, replacements, and restarts. In addition, we release a new large-scale dataset with disfluencies that can be used on four different tasks: disfluency detection, classification, extraction, and correction. Experimental results on the LARD dataset demonstrate that the data produced by the proposed method can be effectively used for detecting and removing disfluencies, while also addressing limitations of existing datasets.


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A Case Study of NLG from Multimedia Data Sources: Generating Architectural Landmark Descriptions
Simon Mille | Spyridon Symeonidis | Maria Rousi | Montserrat Marimon Felipe | Klearchos Stavrothanasopoulos | Petros Alvanitopoulos | Roberto Carlini Salguero | Jens Grivolla | Georgios Meditskos | Stefanos Vrochidis | Leo Wanner
Proceedings of the 3rd International Workshop on Natural Language Generation from the Semantic Web (WebNLG+)

In this paper, we present a pipeline system that generates architectural landmark descriptions using textual, visual and structured data. The pipeline comprises five main components:(i) a textual analysis component, which extracts information from Wikipedia pages; (ii)a visual analysis component, which extracts information from copyright-free images; (iii) a retrieval component, which gathers relevant (property, subject, object) triples from DBpedia; (iv) a fusion component, which stores the contents from the different modalities in a Knowledge Base (KB) and resolves the conflicts that stem from using different sources of information; (v) an NLG component, which verbalises the resulting contents of the KB. We show that thanks to the addition of other modalities, we can make the verbalisation of DBpedia triples more relevant and/or inspirational.