Publication: Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models
Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models
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Li, J., Ataman, D., & Sennrich, R. (2021). Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models. 8556–8562. https://aclanthology.org/2021.emnlp-main.673
Abstract
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Abstract
Multimodal machine translation (MMT) systems have been shown to outperform their text-only neural machine translation (NMT) counterparts when visual context is available. However, recent studies have also shown that the performance of MMT models is only marginally impacted when the associated image is replaced with an unrelated image or noise, which suggests that the visual context might not be exploited by the model at all. We hypothesize that this might be caused by the nature of the commonly used evaluation benchmark, also known as
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Citations
Li, J., Ataman, D., & Sennrich, R. (2021). Vision Matters When It Should: Sanity Checking Multimodal Machine Translation Models. 8556–8562. https://aclanthology.org/2021.emnlp-main.673
