Summary of 3am: An Ambiguity-aware Multi-modal Machine Translation Dataset, by Xinyu Ma et al.
3AM: An Ambiguity-Aware Multi-Modal Machine Translation Dataset
by Xinyu Ma, Xuebo Liu, Derek F. Wong, Jun Rao, Bei Li, Liang Ding, Lidia S. Chao, Dacheng Tao, Min Zhang
First submitted to arxiv on: 29 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the limitations of existing multimodal machine translation (MMT) datasets by introducing 3AM, a novel dataset designed to include more ambiguity and variety in both captions and images. The 26,000 parallel sentence pairs in English and Chinese come with corresponding images, making it a challenging benchmark for MMT models. By utilizing word sense disambiguation, the dataset is crafted to test models’ ability to exploit visual information. Experimental results show that state-of-the-art MMT models trained on 3AM outperform those trained on other datasets in leveraging visual cues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new and better way for machines to translate words between languages by using pictures too. Right now, the pictures used in machine translation don’t really help because they’re not very useful or diverse. This makes it hard for machines to learn how to use pictures effectively. The researchers created a new dataset with 26,000 pairs of sentences and images that are more challenging and realistic. They tested some of the best machine translation models on this new data and found that they can do better when using the pictures. |
Keywords
» Artificial intelligence » Translation
