# BackTranslation2.0 -- A Linguistically Motivated Metric to Assess Sign Language Production

arXiv: https://arxiv.org/abs/2606.28673 · HTML: https://mlsanity.com/paper/2606.28673/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 39 of its 69 references that are in the map.
- First posted on arXiv 27 Jun 2026.

## Builds on (most cited first, 30 of 39)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [Judging LLM-as-a-Judge with MT-Bench and Chatbot Arena](https://mlsanity.com/paper/2306.05685/) (2023)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023)
- [Wan: Open and Advanced Large-Scale Video Generative Models](https://mlsanity.com/paper/2503.20314/) (2025)
- [Expressive Body Capture: 3D Hands, Face, and Body from a Single Image](https://mlsanity.com/paper/1904.05866/) (2019)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods](https://mlsanity.com/paper/2412.05579/) (2024)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose](https://mlsanity.com/paper/2303.07399/) (2023)
- [WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild](https://mlsanity.com/paper/2409.12259/) (2024)
- [Progressive Transformers for End-to-End Sign Language Production](https://mlsanity.com/paper/2004.14874/) (2020)
- [Skeleton Aware Multi-modal Sign Language Recognition](https://mlsanity.com/paper/2103.08833/) (2021)
- [LLMs are Good Sign Language Translators](https://mlsanity.com/paper/2404.00925/) (2024)
- [Signing at Scale: Learning to Co-Articulate Signs for Large-Scale Photo-Realistic Sign Language Production](https://mlsanity.com/paper/2203.15354/) (2022)
- [Neural Localizer Fields for Continuous 3D Human Pose and Shape Estimation](https://mlsanity.com/paper/2407.07532/) (2024)
- [Adversarial Training for Multi-Channel Sign Language Production](https://mlsanity.com/paper/2008.12405/) (2020)
- [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)
- [Neural Sign Actors: A diffusion model for 3D sign language production from text](https://mlsanity.com/paper/2312.02702/) (2023)
- [Natural Language-Assisted Sign Language Recognition](https://mlsanity.com/paper/2303.12080/) (2023)
- [T2S-GPT: Dynamic Vector Quantization for Autoregressive Sign Language Production from Text](https://mlsanity.com/paper/2406.07119/) (2024)
- [Lost in Translation, Found in Context: Sign Language Translation with Contextual Cues](https://mlsanity.com/paper/2501.09754/) (2025)
- [GUAVA: Generalizable Upper Body 3D Gaussian Avatar](https://mlsanity.com/paper/2505.03351/) (2025)
- [Signs as Tokens: A Retrieval-Enhanced Multilingual Sign Language Generator](https://mlsanity.com/paper/2411.17799/) (2024)
- [SignCLIP: Connecting Text and Sign Language by Contrastive Learning](https://mlsanity.com/paper/2407.01264/) (2024)
- [Sign-IDD: Iconicity Disentangled Diffusion for Sign Language Production](https://mlsanity.com/paper/2412.13609/) (2024)
- [SignRep: Enhancing Self-Supervised Sign Representations](https://mlsanity.com/paper/2503.08529/) (2025)
- [TEASER: Token Enhanced Spatial Modeling for Expressions Reconstruction](https://mlsanity.com/paper/2502.10982/) (2025)

## Built on by (most cited first, 1 of 1)

- [Beyond BLEU: A Case for Redefining Sign Language Translation Benchmarks](https://mlsanity.com/paper/2609.03734/) (2026)

## Abstract (arXiv)

Sign Languages (SLs) are the primary means of communication for millions of deaf individuals, yet existing evaluation metrics for generated SL remain simplistic and poorly aligned with human judgements. We introduce BackTranslation2.0, a linguistically grounded evaluation metric for text-to-sign translation that moves beyond na"ive backtranslation. Our approach adopts an agentic framework in which a deterministic pipeline orchestrates a suite of specialised tools to assess four scoring dimensions - grammatical correctness, phonological accuracy, motion fluency, and generation fidelity - aligned with human rater assessments. Tool outputs are not treated independently: a set of large language model (LLM)-based cross-referential comparison modules evaluates consistency across tools and checks outputs against linguistic expectations, enabling structured reasoning over grammatical, phonological, and motion-level evidence. Final dimension scores are computed through deterministic weighted formulas over validated tool outputs. To validate BackTranslation2.0, we introduce and evaluate on a British Sign Language (BSL) dataset rated in a human rater study across the same quality dimensions, following a protocol developed in collaboration between linguists and deaf experts, benchmarking against six baseline metrics. Our method demonstrates strong correlation with human judgements across all dimensions, providing a more comprehensive, interpretable, and linguistically principled evaluation framework for sign language production systems.