2021 Fiscal Year Annual Research Report
Multilingual Knowledge Discovery in Digital Cultural Collections
| Project/Area Number |
20K20135
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| Research Institution | Ritsumeikan University |
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Principal Investigator |
SONG Yuting 立命館大学, 情報理工学部, 助教 (50849388)
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| Project Period (FY) |
2020-04-01 – 2022-03-31
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| Keywords | Entity matching / MT Evaluation / Entity recognition / Relation extraction |
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| Outline of Annual Research Achievements |
This year we focused on improving the method of cross-lingual entity matching and collecting datasets for machine translation evaluation.
First, we proposed a novel method to identify records that refer to the same Japanese artwork entity in Japanese and English data sources. Our approach considered an entity as a sequence of attributes and employed a multilingual BERT-based network to enable cross-lingual entities to be compared without aligning the schema. In addition, we collected datasets and conducted further experiments to evaluate machine translations on translating ukiyo-e metadata records, especially the genre of bijin-e. In another work, we have investigated and evaluated the current state-of-the-art models to automatically discover entities and relations in short texts.
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