Construction and Evaluation of a Self-Attention Model for Semantic Understanding of Sentence-Final Particles

2022/10/01 by Shuhei Mandokoro, Natsuki Oka, Mandokoro, Shuhei +11
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #I.2.7 #Natural Language Processing Techniques #Neural and Evolutionary Computing (cs.NE) #Speech and dialogue systems #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2210.00282

openalex publication_date 2022/10/01 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28

Abstract

Sentence-final particles serve an essential role in spoken Japanese because they express the speaker's mental attitudes toward a proposition and/or an interlocutor. They are acquired at early ages and occur very frequently in everyday conversation. However, there has been little proposal for a computational model of acquiring sentence-final particles. This paper proposes Subjective BERT, a self-attention model that takes various subjective senses in addition to language and images as input and learns the relationship between words and subjective senses. An evaluation experiment revealed that the model understands the usage of "yo", which expresses the speaker's intention to communicate new information, and that of "ne", which denotes the speaker's desire to confirm that some information is shared.

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