  Multimodal machine translation (MMT) is a challenging task that seeks to
improve translation quality by incorporating visual information. However,
recent studies have indicated that the visual information provided by existing
MMT datasets is insufficient, causing models to disregard it and overestimate
their capabilities. This issue presents a significant obstacle to the
development of MMT research. This paper presents a novel solution to this issue
by introducing 3AM, an ambiguity-aware MMT dataset comprising 26,000 parallel
sentence pairs in English and Chinese, each with corresponding images. Our
dataset is specifically designed to include more ambiguity and a greater
variety of both captions and images than other MMT datasets. We utilize a word
sense disambiguation model to select ambiguous data from vision-and-language
datasets, resulting in a more challenging dataset. We further benchmark several
state-of-the-art MMT models on our proposed dataset. Experimental results show
that MMT models trained on our dataset exhibit a greater ability to exploit
visual information than those trained on other MMT datasets. Our work provides
a valuable resource for researchers in the field of multimodal learning and
encourages further exploration in this area. The data, code and scripts are
freely available at https://github.com/MaxyLee/3AM.
