# Gloss-Free Sign Language Translation: An Unbiased Evaluation of Progress in the Field

arXiv: https://arxiv.org/abs/2603.13240 · HTML: https://mlsanity.com/paper/2603.13240/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 59 references that are in the map.
- First posted on arXiv 18 Feb 2026.

## Builds on (most cited first, 30 of 44)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Few-shot Learning with Multilingual Language Models](https://mlsanity.com/paper/2112.10668/) (2021)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [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)
- [MS-ASL: A Large-Scale Data Set and Benchmark for Understanding American Sign Language](https://mlsanity.com/paper/1812.01053/) (2018)
- [Gloss-free Sign Language Translation: Improving from Visual-Language Pretraining](https://mlsanity.com/paper/2307.14768/) (2023)
- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [Visual Alignment Constraint for Continuous Sign Language Recognition](https://mlsanity.com/paper/2104.02330/) (2021)
- [A Simple Multi-Modality Transfer Learning Baseline for Sign Language Translation](https://mlsanity.com/paper/2203.04287/) (2022)
- [LLMs are Good Sign Language Translators](https://mlsanity.com/paper/2404.00925/) (2024)
- [AUTSL: A Large Scale Multi-modal Turkish Sign Language Dataset and Baseline Methods](https://mlsanity.com/paper/2008.00932/) (2020)
- [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)
- [Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production](https://mlsanity.com/paper/2203.15354/) (2022)
- [Sign2GPT: Leveraging Large Language Models for Gloss-Free Sign Language Translation](https://mlsanity.com/paper/2405.04164/) (2024)
- [Continuous Sign Language Recognition with Correlation Network](https://mlsanity.com/paper/2303.03202/) (2023)
- [Uni-Sign: Toward Unified Sign Language Understanding at Scale](https://mlsanity.com/paper/2501.15187/) (2025)
- [Gloss Attention for Gloss-free Sign Language Translation](https://mlsanity.com/paper/2307.07361/) (2023)
- [Natural Language-Assisted Sign Language Recognition](https://mlsanity.com/paper/2303.12080/) (2023)
- [Towards Privacy-Aware Sign Language Translation at Scale](https://mlsanity.com/paper/2402.09611/) (2024)
- [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)
- [ASL Citizen: A Community-Sourced Dataset for Advancing Isolated Sign Language Recognition](https://mlsanity.com/paper/2304.05934/) (2023)
- [CiCo: Domain-Aware Sign Language Retrieval via Cross-Lingual Contrastive Learning](https://mlsanity.com/paper/2303.12793/) (2023)

## Built on by (most cited first, 9 of 9)

- [SignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe Learning](https://mlsanity.com/paper/2603.10446/) (2026)
- [SignAgent: Agentic LLMs for Linguistically-Grounded Sign Language Annotation and Dataset Curation](https://mlsanity.com/paper/2603.19059/) (2026)
- [Bootstrapping Sign Language Annotations with Sign Language Models](https://mlsanity.com/paper/2604.07606/) (2026)
- [What's the Point? Spatial Grammar & Index Resolution for Sign Language Processing](https://mlsanity.com/paper/2606.08056/) (2026)
- [SIGNET: Motion-Level Knowledge Transfer for Cross-Language Sign Language Translation](https://mlsanity.com/paper/2606.28626/) (2026)
- [Corpus Augmentation for Sign Language Translation via LLM-Guided Video Stitching](https://mlsanity.com/paper/2606.11925/) (2026)
- [SignSeek: Learning Transferable Representations for Sign Dictionary Retrieval](https://mlsanity.com/paper/2609.03695/) (2026)
- [Beyond BLEU: A Case for Redefining Sign Language Translation Benchmarks](https://mlsanity.com/paper/2609.03734/) (2026)
- [Machine Translation for Sign Languages](https://mlsanity.com/paper/2610.00881/) (2026)

## Abstract (arXiv)

Sign Language Translation (SLT) aims to automatically convert visual sign language videos into spoken language text and vice versa. While recent years have seen rapid progress, the true sources of performance improvements often remain unclear. Do reported performance gains come from methodological novelty, or from the choice of a different backbone, training optimizations, hyperparameter tuning, or even differences in the calculation of evaluation metrics? This paper presents a comprehensive study of recent gloss-free SLT models by re-implementing key contributions in a unified codebase. We ensure fair comparison by standardizing preprocessing, video encoders, and training setups across all methods. Our analysis shows that many of the performance gains reported in the literature often diminish when models are evaluated under consistent conditions, suggesting that implementation details and evaluation setups play a significant role in determining results. We make the codebase publicly available here (https://github.com/ozgemercanoglu/sltbaselines) to support transparency and reproducibility in SLT research.