Summary of Lost in Translation? Translation Errors and Challenges For Fair Assessment Of Text-to-image Models on Multilingual Concepts, by Michael Saxon et al.
Lost in Translation? Translation Errors and Challenges for Fair Assessment of Text-to-Image Models on Multilingual Concepts
by Michael Saxon, Yiran Luo, Sharon Levy, Chitta Baral, Yezhou Yang, William Yang Wang
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV); Computers and Society (cs.CY); Image and Video Processing (eess.IV)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract discusses the limitations of a benchmark, “Conceptual Coverage Across Languages” (CoCo-CroLa), used to evaluate the multilingual capabilities of text-to-image models. Specifically, it highlights translation errors in Spanish, Japanese, and Chinese that affect the utility and validity of this benchmark. The authors provide corrected translations and analyze how these changes impact the results of multiple baseline T2I models. They also show that similar methods can be applied to predict the impact of corrections on image-domain benchmarks using text-domain similarity scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at a way to test if text-to-image models are good at generating pictures in different languages. Right now, there’s a problem with this test because some words were translated incorrectly from Spanish, Japanese, and Chinese. The authors found these mistakes and fixed them. Then they looked at how making these changes affected the results of several other text-to-image models. They also showed that if you know what works in one language, you can make good guesses about what will happen when you try it with another language. |
Keywords
» Artificial intelligence » Translation
