Summary of Gloss2text: Sign Language Gloss Translation Using Llms and Semantically Aware Label Smoothing, by Pooya Fayyazsanavi et al.
Gloss2Text: Sign Language Gloss translation using LLMs and Semantically Aware Label Smoothing
by Pooya Fayyazsanavi, Antonios Anastasopoulos, Jana Košecká
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
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 proposed solution for sign language translation from video to spoken text leverages pre-trained large language models (LLMs), data augmentation, and a novel label-smoothing loss function. The approach aims to improve the performance of state-of-the-art methods by exploiting gloss translation ambiguities. Extensive experiments on the PHOENIX Weather 2014T dataset demonstrate significant performance gains, surpassing current state-of-the-art results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Sign language translation from video to spoken text is a complex task due to differences in grammar, expression, and visual appearance. The proposed solution focuses on the Gloss2Text stage and improves performance by using pre-trained LLMs, data augmentation, and a new label-smoothing loss function. This advancement can help address sign language translation challenges and open up new research opportunities. |
Keywords
» Artificial intelligence » Data augmentation » Loss function » Translation
