轻快走过
发表于 2025-3-28 16:20:22
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Admire
发表于 2025-3-28 21:50:59
https://doi.org/10.1007/978-3-031-00129-1artificial intelligence; computational linguistics; computer hardware; computer networks; computer syste
反抗者
发表于 2025-3-28 23:49:09
978-3-031-00128-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
ineffectual
发表于 2025-3-29 06:20:37
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
发表于 2025-3-29 10:48:32
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
发表于 2025-3-29 15:19:39
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
乐意
发表于 2025-3-29 16:39:31
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Modicum
发表于 2025-3-29 19:58:48
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
娴熟
发表于 2025-3-29 23:54:47
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
发表于 2025-3-30 06:28:23
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