Translating culture-related content is vital for effective cross-cultural communication. However, many culture-specific items (CSIs) often lack viable translations across languages, making it challenging to collect high-quality, diverse parallel corpora with CSI annotations. This difficulty hinders the analysis of cultural awareness of machine translation (MT) systems, including traditional neural MT and the emerging MT paradigm using large language models (LLM). To address this gap, we introduce a novel parallel corpus, enriched with CSI annotations in 6 language pairs for investigating Culturally-Aware Machine Translation---CAMT. Furthermore, we design two evaluation metrics to assess CSI translations, focusing on their pragmatic translation quality. Our findings show the superior ability of LLMs over neural MTs in leveraging external cultural knowledge for translating CSIs, especially those lacking translations in the target culture.
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Transys: Leveraging Common Security Properties Across Hardware Designs
This paper presents Transys, a tool for translating security critical properties written for one hardware design to analogous properties suitable for a second design. Transys works in three passes adjusting the variable names, arithmetic expressions, logical preconditions, and timing constraints of the original property to retain the intended semantics of the property while making it valid for the second design. We evaluate Transys by translating 27 assertions written in a temporal logic and 9 properties written for use with gate level information flow tracking across 38 AES designs, 3 RSA designs, and 5 RISC processor designs. Transys successfully translates 96% of the properties. Among these, the translation of 23 (64%) of the properties achieved a semantic equivalence rate of above 60%. The average translation time per property is about 70 seconds.
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- Award ID(s):
- 1816637
- PAR ID:
- 10207526
- Date Published:
- Journal Name:
- 2020 IEEE Symposium on Security and Privacy (SP)
- Page Range / eLocation ID:
- 1713 to 1727
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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