The use of pan-ethnic-group products form knowledge primarily depends on a designer＇s subjective experience without user participation. The majority of studies primarily focus on the detection of the perceptual deman...The use of pan-ethnic-group products form knowledge primarily depends on a designer＇s subjective experience without user participation. The majority of studies primarily focus on the detection of the perceptual demands of consumers from the target product category. A pan-ethnic-group products form gene clustering method based on emotional semantic is constructed. Consumers＇ perceptual images of the pan-ethnic-group products are obtained by means of product form gene extraction and coding and computer aided product form clustering technology. A case of form gene clustering about the typical pan-ethnic-group products is investigated which indicates that the method is feasible. This paper opens up a new direction for the future development of product form design which improves the agility of product design process in the era of Industry 4.0.展开更多
Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input p...Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input posts and incorporating it into the gener-ation of semantically coherent and emotionally reasonable responses.However,most previous works generate emotional responses solely from input posts,which do not take full advantage of the training corpus and suffer from generating generic responses.In this study,we introduce a hierarchical semantic‐emotional memory module for emotional conversation generation(called HSEMEC),which can learn abstract semantic conver-sation patterns and emotional information from the large training corpus.The learnt semantic and emotional knowledge helps to enrich the post representation and assist the emotional conversation generation.Comprehensive experiments on a large real‐world conversation corpus show that HSEMEC can outperform the strong baselines on both automatic and manual evaluation.For reproducibility,we release the code and data publicly at:https://github.com/siat‐nlp/HSEMEC‐code‐data.展开更多
基金Supported by National Key Technology R&D Program,China(Grant No.2015BAH21F01)National 111 Project,China(Grant No.B13044)
文摘The use of pan-ethnic-group products form knowledge primarily depends on a designer＇s subjective experience without user participation. The majority of studies primarily focus on the detection of the perceptual demands of consumers from the target product category. A pan-ethnic-group products form gene clustering method based on emotional semantic is constructed. Consumers＇ perceptual images of the pan-ethnic-group products are obtained by means of product form gene extraction and coding and computer aided product form clustering technology. A case of form gene clustering about the typical pan-ethnic-group products is investigated which indicates that the method is feasible. This paper opens up a new direction for the future development of product form design which improves the agility of product design process in the era of Industry 4.0.
基金supported by the National Natural Science Foundation of China(No.61906185,61876053)the Natural Science Foundation of Guangdong Province of China(No.2019A1515011705 and No.2021A1515011905)+2 种基金the Youth Innovation Promotion Association of CAS China(No.2020357)the Shenzhen Basic Research Foundation(No.JCYJ20210324115614039 and No.JCYJ20200109113441941)the Shenzhen Science and Technology Innovation Program(Grant No.KQTD20190929172835662).
文摘Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input posts and incorporating it into the gener-ation of semantically coherent and emotionally reasonable responses.However,most previous works generate emotional responses solely from input posts,which do not take full advantage of the training corpus and suffer from generating generic responses.In this study,we introduce a hierarchical semantic‐emotional memory module for emotional conversation generation(called HSEMEC),which can learn abstract semantic conver-sation patterns and emotional information from the large training corpus.The learnt semantic and emotional knowledge helps to enrich the post representation and assist the emotional conversation generation.Comprehensive experiments on a large real‐world conversation corpus show that HSEMEC can outperform the strong baselines on both automatic and manual evaluation.For reproducibility,we release the code and data publicly at:https://github.com/siat‐nlp/HSEMEC‐code‐data.
