bleu-11255080·3 events·first seen Aliases: BLEU
Researchers present a sentence-level sign language translation (SLT) system fine-tuned on How2Sign using QLoRA on a SHuBERT-ByT5 stack, achieving BLEU 15.9 and BLEURT 44.7 on the test split. The primary contribution is a hardware-aware streaming architecture that distributes camera capture to a Raspberry Pi 4B client while offloading perception and translation to a CPU/GPU backend. Latency optimizations including chunked ingestion, parallelized perception, and a sentence-boundary state machine reduce mean post-finalization response latency by 27.7% to 1.354 seconds. The system is designed for real-time deployment across diverse client devices rather than proposing a novel translation architecture.
A new arXiv preprint proposes a noisy-channel decomposition of Minimum Bayes Risk (MBR) decoding that breaks the process into four components: hypothesis-to-reference likelihood, reference-to-hypothesis likelihood, hypothesis prior, and reference prior. The decomposition addresses a known asymmetry problem in MBR decoding caused by directional evaluation metrics like BLEU and COMET. The framework unifies existing MBR variants under a single interpretation and suggests that channel-specific weighting could improve over standard MBR decoding.
Researchers introduce a data synthesis methodology for low-resource neural machine translation of Q'eqchi' Mayan, converting community-sourced dictionaries into a synthetic parallel corpus to avoid scraping target-language data. Using LoRA adapters on mT5-base, the approach achieves BLEU 42.02 on in-domain evaluation but only 0.59 against organic text, revealing a structural-semantic gap. An ablation with multi-task learning produced negative transfer, suggesting LoRA capacity limits conflict with auxiliary objectives. The study concludes synthetic bootstrapping is effective for structural priming but requires authentic data for semantic refinement via curriculum learning.