1 citations · 1 across the 5 of their papers we have counts for
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CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation
Emilio Villa-Cueva, Sholpan Bolatzhanova, Diana Turmakhan +32
Translating cultural content poses challenges for machine translation systems due to the differences in conceptualizations between cultures, where language alone may fail to convey…
Language-Specific Latent Process Hinders Cross-Lingual Performance
Zheng Wei Lim, Alham Fikri Aji, Trevor Cohn
Large language models (LLMs) are demonstrably capable of cross-lingual transfer, but can produce inconsistent output when prompted with the same queries written in different langua…
Mufu: Multilingual Fused Learning for Low-Resource Translation with LLM
Zheng Wei Lim, Nitish Gupta, Honglin Yu +1
Multilingual large language models (LLMs) are great translators, but this is largely limited to high-resource languages. For many LLMs, translating in and out of low-resource langu…
Simpson's Paradox and the Accuracy-Fluency Tradeoff in Translation
Zheng Wei Lim, Ekaterina Vylomova, Trevor Cohn +1
A good translation should be faithful to the source and should respect the norms of the target language. We address a theoretical puzzle about the relationship between these object…
Predicting Human Translation Difficulty with Neural Machine Translation
Zheng Wei Lim, Ekaterina Vylomova, Charles Kemp +1
Human translators linger on some words and phrases more than others, and predicting this variation is a step towards explaining the underlying cognitive processes. Using data from…