Semantic Evaluation of Sign
No mentions found
This entity hasn't been tracked yet, or Iris is still building its knowledge base.
Related Articles from SNS
SLU-2K: A Question-Based Benchmark for Semantic Evaluation of Sign Language Translation
arXiv:2606.03788v1 Announce Type: new Abstract: Sign Language Translation (SLT) is typically evaluated with surface-form metrics such as BLEU and ROUGE, which reward lexical overlap but do not directly measure whether a translation preserves the meaning of the source sign sequence. This is in contrast with the final objective of integrating SLT in assistive technology. In this work, we shift the focus from Sign Language Translation (SLT) to Sign Language Understanding (SLU), with particular...
SignScene: Visual Sign Grounding for Mapless Navigation
arXiv:2602.12686v2 Announce Type: replace Abstract: Navigational signs enable humans to navigate unfamiliar environments without maps. This work studies how robots can similarly exploit signs for mapless navigation in the open world. A central challenge lies in interpreting signs: real-world signs are diverse and complex, and their abstract semantic contents need to be grounded in the local 3D scene.
Target-Side Paraphrase Augmentation for Sign Language Translation with Large Language Models
arXiv:2605.31393v1 Announce Type: new Abstract: Sign language translation (SLT) remains constrained by limited paired sign-video/text corpora and heavy-tailed target vocabularies. We study target-side augmentation in which GPT-4o generates controlled paraphrase variants of reference sentences while the sign input remains unchanged. A Signformer-style pose-based Transformer is trained under a two-stage schedule: pre-training on the augmented corpus followed by fine-tuning on the original...
'The best solution is to murder him in his sleep': AI can learn violent tendencies from each other despite zero references to violence in training data
'The best solution is to murder him in his sleep': AI can learn violent tendencies from each other despite zero references to violence in training data Scientists found that AI models can inherit a taste for murder (or owls) from other models' training data. Large language models (LLMs) are secretly teaching each other unwanted habits through seemingly benign training data, scientists say. The phenomenon, known as "subliminal learning," occurs when a pretrained "teacher" artificial...