# Beyond BLEU: A Case for Redefining Sign Language Translation Benchmarks

arXiv: https://arxiv.org/abs/2609.03734 · HTML: https://mlsanity.com/paper/2609.03734/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 43 of its 66 references that are in the map.
- First posted on arXiv 3 Sep 2026.

## Builds on (most cited first, 30 of 43)

- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [PAWS: Paraphrase Adversaries from Word Scrambling](https://mlsanity.com/paper/1904.01130/) (2019)
- [Experts, Errors, and Context: A Large-Scale Study of Human Evaluation for Machine Translation](https://mlsanity.com/paper/2104.14478/) (2021)
- [PAWS-X: A Cross-lingual Adversarial Dataset for Paraphrase Identification](https://mlsanity.com/paper/1908.11828/) (2019)
- [Word-level Deep Sign Language Recognition from Video: A New Large-scale Dataset and Methods Comparison](https://mlsanity.com/paper/1910.11006/) (2019)
- [Improving Sign Language Translation with Monolingual Data by Sign Back-Translation](https://mlsanity.com/paper/2105.12397/) (2021)
- [How2Sign: A Large-scale Multimodal Dataset for Continuous American Sign Language](https://mlsanity.com/paper/2008.08143/) (2020)
- [Two-Stream Network for Sign Language Recognition and Translation](https://mlsanity.com/paper/2211.01367/) (2022)
- [Gloss-free Sign Language Translation: Improving from Visual-Language Pretraining](https://mlsanity.com/paper/2307.14768/) (2023)
- [Video-based Sign Language Recognition without Temporal Segmentation](https://mlsanity.com/paper/1801.10111/) (2018)
- [Sign Language Recognition, Generation, and Translation: An Interdisciplinary Perspective](https://mlsanity.com/paper/1908.08597/) (2019)
- [A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation](https://mlsanity.com/paper/2203.04287/) (2022)
- [YouTube-ASL: A Large-Scale, Open-Domain American Sign Language-English Parallel Corpus](https://mlsanity.com/paper/2306.15162/) (2023)
- [Open-Domain Sign Language Translation Learned from Online Video](https://mlsanity.com/paper/2205.12870/) (2022)
- [Adversarial Training for Multi-Channel Sign Language Production](https://mlsanity.com/paper/2008.12405/) (2020)
- [YouTube-SL-25: A Large-Scale, Open-Domain Multilingual Sign Language Parallel Corpus](https://mlsanity.com/paper/2407.11144/) (2024)
- [SLTUNET: A Simple Unified Model for Sign Language Translation](https://mlsanity.com/paper/2305.01778/) (2023)
- [CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning](https://mlsanity.com/paper/2303.12793/) (2023)
- [Improving Gloss-free Sign Language Translation by Reducing Representation Density](https://mlsanity.com/paper/2405.14312/) (2024)
- [T2S-GPT: Dynamic Vector Quantization for Autoregressive Sign Language Production from Text](https://mlsanity.com/paper/2406.07119/) (2024)
- [SignAvatars: A Large-scale 3D Sign Language Holistic Motion Dataset and Benchmark](https://mlsanity.com/paper/2310.20436/) (2023)
- [BBC-Oxford British Sign Language Dataset](https://mlsanity.com/paper/2111.03635/) (2021)
- [SimulSLT: End-to-End Simultaneous Sign Language Translation](https://mlsanity.com/paper/2112.04228/) (2021)
- [Open Subtitles Paraphrase Corpus for Six Languages](https://mlsanity.com/paper/1809.06142/) (2018)
- [Factorized Learning Assisted with Large Language Model for Gloss-free Sign Language Translation](https://mlsanity.com/paper/2403.12556/) (2024)
- [C2RL: Content and Context Representation Learning for Gloss-free Sign Language Translation and Retrieval](https://mlsanity.com/paper/2408.09949/) (2024)
- [Systemic Biases in Sign Language AI Research: A Deaf-Led Call to Reevaluate Research Agendas](https://mlsanity.com/paper/2403.02563/) (2024)
- [Human-Paraphrased References Improve Neural Machine Translation](https://mlsanity.com/paper/2010.10245/) (2020)
- [A Tale of Two Languages: Large-Vocabulary Continuous Sign Language Recognition from Spoken Language Supervision](https://mlsanity.com/paper/2405.10266/) (2024)
- [Sign Language Production with Latent Motion Transformer](https://mlsanity.com/paper/2312.12917/) (2023)

## Abstract (arXiv)

BLEU-4 is the standard metric for evaluating sign language translation (SLT), but spoken-language metrics may not adequately reflect sign language proficiency. The multimodal, low-resource context of SLT allows models to exploit spurious correlations and spoken-language priors, rather than learning stronger sign representations. In this paper, we evaluate the relationship between spatio-temporal understanding and BLEU-4 across six SLT models on Phoenix-2014T and CSL-Daily, showing that gains in BLEU-4 are not on their own evidence of better sign language understanding. This work introduces an alternative inspired by language-learning assessment, using an open-weight-LLM QA protocol that measures salient content preservation. It aligns more closely with human rankings and is six to seven times more paraphrase-invariant than BLEU-4. Applied to SLT, this protocol targets content transfer, is more robust to train-test overlap, and gives a different picture of the field: the five gloss-free systems are largely within noise of one another on Phoenix-2014T, while the gloss-supervised system stands 9.3 points higher, a gap invisible to BLEU-4.