Auditory Synaesthesia and Near Synonyms: A Corpus-Based Analysis of sheng1 and yin1 in Mandarin Chinese

This paper explores the nature of linguistic synaesthesia in the auditory domain through a corpus-based lexical semantic study of near synonyms. It has been established that the near synonyms 聲 sheng “sound ” and 音 yin “sound ” in Mandarin Chinese have different semantic functions in representing auditory production and auditory perception respec-tively. Thus, our study is devoted to test-ing whether linguistic synaesthesia is sensi-tive to this semantic dichotomy of cognition in particular, and to examining the relation-ship between linguistic synaesthesia and cog-nitive modelling in general. Based on the cor-pus, we find that the near synonyms exhibit both similarities and differences on synaesthe-sia. The similarities lie in that both 聲 and音 are productive recipients of synaesthetic trans-fers, and vision acts as the source domain most frequently. Besides, the differences exist in se-lective constraints for 聲 and 音 with synaes-thetic modifiers as well as syntactic functions of the whole combinations. We propose that the similarities can be explained by the cogni-tive characteristics of the sound, while the dif-ferences are determined by the influence of the semantic dichotomy of production/perception on synaesthesia. Therefore, linguistic synaes-thesia is not a random association, but can be motivated and predicted by cognition.

GAG: Global Attributed Graph Neural Network for Streaming Session-based Recommendation

Streaming session-based recommendation (SSR) is a challenging task that requires the recommender system to do the session-based recommendation (SR) in the streaming scenario. In the real-world applications of e-commerce and social media, a sequence of user-item interactions generated within a certain period are grouped as a session, and these sessions consecutively arrive in the form of streams. Most of the recent SR research has focused on the static setting where the training data is first acquired and then used to train a session-based recommender model. They need several epochs of training over the whole dataset, which is infeasible in the streaming setting. Besides, they can hardly well capture long-term user interests because of the neglect or the simple usage of the user information. Although some streaming recommendation strategies have been proposed recently, they are designed for streams of individual interactions rather than streams of sessions. In this paper, we propose a Global Attributed Graph (GAG) neural network model with a Wasserstein reservoir for the SSR problem. On one hand, when a new session arrives, a session graph with a global attribute is constructed based on the current session and its associate user. Thus, the GAG can take both the global attribute and the current session into consideration to learn more comprehensive representations of the session and the user, yielding a better performance in the recommendation. On the other hand, for the adaptation to the streaming session scenario, a Wasserstein reservoir is proposed to help preserve a representative sketch of the historical data. Extensive experiments on two real-world datasets have been conducted to verify the superiority of the GAG model compared with the state-of-the-art methods