Proactive dialogue generates dialogue utterance based on a conversation goal and a given knowledge graph(KG). Existing methods combine knowledge of each turn of dialogue with knowledge triples by hidden variables, res...Proactive dialogue generates dialogue utterance based on a conversation goal and a given knowledge graph(KG). Existing methods combine knowledge of each turn of dialogue with knowledge triples by hidden variables, resulting in the interpretability of generation results is relatively poor. An interpretable knowledge-aware path(KAP) model was proposed for knowledge reasoning in proactive dialogue generation.KAP model can transform explicit and implicit knowledge of each turn of dialogue into corresponding dialogue state matrix, thus forming the KAP for dialogue history. Based on KAP, the next turn of dialogue state vector can be infered from both the topology and semantic of KG. This vector can indicate knowledge distribution of next sentence, so it enhances the accuracy and interpretability of dialogue generation. Experiments show that KAP model’s dialogue generation is closer to actual conversation than other state-of-the-art proactive dialogue models.展开更多
To develop a knowledge-aware recommender system,a key issue is how to obtain rich and structured knowledge base(KB)information for recommender system(RS)items.Existing data sets or methods either use side information ...To develop a knowledge-aware recommender system,a key issue is how to obtain rich and structured knowledge base(KB)information for recommender system(RS)items.Existing data sets or methods either use side information from original RSs(containing very few kinds of useful information)or utilize a private KB.In this paper,we present KB4Rec v1.0,a data set linking KB information for RSs.It has linked three widely used RS data sets with two popular KBs,namely Freebase and YAGO.Based on our linked data set,we first preform qualitative analysis experiments,and then we discuss the effect of two important factors(i.e.,popularity and recency)on whether a RS item can be linked to a KB entity.Finally,we compare several knowledge-aware recommendation algorithms on our linked data set.展开更多
基金supported by the National Natural Science Foundation of China (61702047)。
文摘Proactive dialogue generates dialogue utterance based on a conversation goal and a given knowledge graph(KG). Existing methods combine knowledge of each turn of dialogue with knowledge triples by hidden variables, resulting in the interpretability of generation results is relatively poor. An interpretable knowledge-aware path(KAP) model was proposed for knowledge reasoning in proactive dialogue generation.KAP model can transform explicit and implicit knowledge of each turn of dialogue into corresponding dialogue state matrix, thus forming the KAP for dialogue history. Based on KAP, the next turn of dialogue state vector can be infered from both the topology and semantic of KG. This vector can indicate knowledge distribution of next sentence, so it enhances the accuracy and interpretability of dialogue generation. Experiments show that KAP model’s dialogue generation is closer to actual conversation than other state-of-the-art proactive dialogue models.
基金The work was partially supported by National Natural Science Foundation of China under the grant numbers 61872369,61832017 and 61502502.
文摘To develop a knowledge-aware recommender system,a key issue is how to obtain rich and structured knowledge base(KB)information for recommender system(RS)items.Existing data sets or methods either use side information from original RSs(containing very few kinds of useful information)or utilize a private KB.In this paper,we present KB4Rec v1.0,a data set linking KB information for RSs.It has linked three widely used RS data sets with two popular KBs,namely Freebase and YAGO.Based on our linked data set,we first preform qualitative analysis experiments,and then we discuss the effect of two important factors(i.e.,popularity and recency)on whether a RS item can be linked to a KB entity.Finally,we compare several knowledge-aware recommendation algorithms on our linked data set.
