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Rethinking Sign Language Translation: The Impact of Signer Dependence on Model Evaluation
Keren Artiaga, Sabyasachi Kamila, Haithem Afli +2
Sign Language Translation has advanced with deep learning, yet evaluations remain largely signer-dependent, with overlapping signers across train/dev/test. This raises concerns abo…
The Harder Text Embedding Benchmark (HTEB): Beyond One-dimensional Static Robustness
Manuel Frank, Haithem Afli
Embedding benchmarks like MTEB report a single score per model, implicitly treating robustness as a static, scalar property. We argue that embedding robustness is multidimensional,…
The Influence of Iconicity in Transfer Learning for Sign Language Recognition
Keren Artiaga, Conor Lynch, Haithem Afli +1
Most sign language recognition research relies on Transfer Learning (TL) from vision-based datasets such as ImageNet. Some extend this to alternatively available language datasets,…
PTEB: Towards Robust Text Embedding Evaluation via Stochastic Paraphrasing at Evaluation Time with LLMs
Manuel Frank, Haithem Afli
Current sentence embedding evaluations typically rely on static test beds like the Massive Text Embedding Benchmark (MTEB). While invaluable, repeated tuning on a fixed suite can i…
GASE: Generatively Augmented Sentence Encoding
Manuel Frank, Haithem Afli
We propose a training-free approach to improve sentence embeddings leveraging test-time compute by applying generative text models for data augmentation at inference time. Unlike c…