Titlebook: Database Systems for Advanced Applications; 27th International C Arnab Bhattacharya,Janice Lee Mong Li,Rage Uday Ki Conference proceedings

輕快走過

Admire

https://doi.org/10.1007/978-3-031-00129-1artificial intelligence; computational linguistics; computer hardware; computer networks; computer syste

反抗者

978-3-031-00128-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

ineffectual

Hanging on to the Imperial Pastnd visual signals. Image-grounded emotional response generation (IgERG) tasks requires chatbots to generate a response with the understanding of both textual contexts and speakers’ emotions in visual signals. Pre-training models enhance many NLP and CV tasks and image-text pre-training also helps mu

切掉

Jacob Davidsen,Paul McIlvenny,Thomas Rybergs. Recently, deep learning techniques have been substantially applied to entity resolution. We focus on entity resolution with graph based multi-semantic data embedding. In ER, data with attributes cannot be well represented by common word embeddings from natural language processing. In this work, d

Ergots

https://doi.org/10.1007/978-3-031-35411-3y totally change the underlying logic. Currently, existing datasets for MWP task contain limited samples which are key for neural models to learn to disambiguate different kinds of local variances in questions and solve the questions correctly. In this paper, we propose a set of novel data augmentat

樂意

Modicum

Jacob Davidsen,Paul McIlvenny,Thomas Rybergs the volume of the databases has been increased considerably over the past few decades. Making a good use of a survey paper of the research topic can vastly lower the difficulty but there may be no survey paper in some emerging research topics due to the rapid development. In this work, we propose

嫻熟

https://doi.org/10.1007/978-3-031-35411-3 challenges of the task lie in how to comprehend the story context sufficiently and handle the implicit knowledge behind story clues effectively, which are still under-explored by previous work. In this paper, we propose a Story Heterogeneous Graph Network (SHGN) to explicitly model both the informa

世俗

Wolfgang A. Halang,Alexander D. Stoyenkoes based on external knowledge are proposed to generate rich semantic and information conversation. Two types of knowledge have been studied for knowledge-aware open-domain dialogue generation: structured triples from knowledge graphs and unstructured texts from documents. To take both advantages of