Multilingual decoder representations have reduced isotropy compared to bilingual models, impacting performance.
Developing a dual translation model for Taiwanese Hokkien bridges the resource gap, emphasizing the importance of monolingual corpora and standardization.
The author explores the impact of context on evaluating chat translation quality, highlighting the need for robust metrics to capture errors specific to conversational texts.
The author argues for evaluating multimodal translation models using a framework that considers visual information and the ability to translate complex sentences. They propose a new evaluation method to address the limitations of current evaluation practices.