Summary of Signattention: on the Interpretability Of Transformer Models For Sign Language Translation, by Pedro Alejandro Dal Bianco et al.
SignAttention: On the Interpretability of Transformer Models for Sign Language Translation
by Pedro Alejandro Dal Bianco, Oscar Agustín Stanchi, Facundo Manuel Quiroga, Franco Ronchetti, Enzo Ferrante
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 The paper presents an in-depth analysis of a Transformer-based Sign Language Translation (SLT) model that translates video-based Greek Sign Language into glosses and text. The study uses the Greek Sign Language Dataset to examine how the model processes and aligns visual input with sequential glosses, highlighting the attention mechanisms within the model. The findings reveal that the model focuses on clusters of frames rather than individual ones, with a diagonal alignment pattern emerging between poses and glosses. Additionally, the paper explores the relative contributions of cross-attention and self-attention at each decoding step, showing that the model initially relies on video frames but shifts its focus to previously predicted tokens as the translation progresses. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks into how a special computer program can translate sign language from videos into words. The researchers used a big dataset of sign language and looked at how the computer works to understand what it’s doing. They found that the computer focuses on groups of images instead of individual ones, and that it gets better at understanding the signs as it goes along. |
Keywords
» Artificial intelligence » Alignment » Attention » Cross attention » Self attention » Transformer » Translation
