Abstract
Resolving cultural differences is an important factor in ensuring successful communication. Today's technologies, such as machine translation (MT), enable communication and collaboration among speakers of multiple languages, but the barrier of cultural difference remains, and depending on the country of origin of each party, a word with a positive connotation in one country may have a negative connotation in another. Using a BERT masked language model, constructed from Wikipedia in English and Japanese, we calculated the probability of co-occurrence of positive and negative connotations and the target concepts in template sentences. The proposed method was applied to 13 concepts, and the results are presented in this paper.
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Acknowledgements
This research was partially supported by a Grant-in-Aid for Scientific Research (A) (17H00759, 2017–2020), a Grant-in-Aid for Scientific Research (B) (21H03561,2021–2024) and a Grant-in-Aid for Early-Career Scientists (21K17794,2021–2024) from the Japan Society for the Promotion of Sciences (JSPS).
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Pituxcoosuvarn, M., Murakami, Y., Miwa, K. (2023). Detection for Cultural Difference in Impression Using Masked Language Model. In: Rauterberg, M. (eds) Culture and Computing. HCII 2023. Lecture Notes in Computer Science, vol 14035. Springer, Cham. https://doi.org/10.1007/978-3-031-34732-0_44
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DOI: https://doi.org/10.1007/978-3-031-34732-0_44
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