# C2RL: Content and Context Representation Learning for Gloss-free Sign Language Translation and Retrieval

arXiv: https://arxiv.org/abs/2408.09949 · HTML: https://mlsanity.com/paper/2408.09949/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 40 of its 64 references that are in the map.
- First posted on arXiv 19 Aug 2024.

## Builds on (most cited first, 30 of 40)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation](https://mlsanity.com/paper/2201.12086/) (2022)
- [Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://mlsanity.com/paper/2102.05918/) (2021)
- [ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks](https://mlsanity.com/paper/1908.02265/) (2019)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [CoCa: Contrastive Captioners are Image-Text Foundation Models](https://mlsanity.com/paper/2205.01917/) (2022)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [Beyond English-Centric Multilingual Machine Translation](https://mlsanity.com/paper/2010.11125/) (2020)
- [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://mlsanity.com/paper/2008.00401/) (2020)
- [Sign Language Transformers: Joint End-to-end Sign Language Recognition and Translation](https://mlsanity.com/paper/2003.13830/) (2020)
- [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)
- [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)
- [TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation](https://mlsanity.com/paper/2010.05468/) (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)
- [Transferring Cross-domain Knowledge for Video Sign Language Recognition](https://mlsanity.com/paper/2003.03703/) (2020)
- [Sign2GPT: Leveraging Large Language Models for Gloss-Free Sign Language Translation](https://mlsanity.com/paper/2405.04164/) (2024)
- [Gloss Attention for Gloss-free Sign Language Translation](https://mlsanity.com/paper/2307.07361/) (2023)
- [Sign Language Translation from Instructional Videos](https://mlsanity.com/paper/2304.06371/) (2023)

## Built on by (most cited first, 24 of 24)

- [Uni-Sign: Toward Unified Sign Language Understanding at Scale](https://mlsanity.com/paper/2501.15187/) (2025)
- [SHuBERT: Self-Supervised Sign Language Representation Learning via Multi-Stream Cluster Prediction](https://mlsanity.com/paper/2411.16765/) (2024)
- [Geo-Sign: Hyperbolic Contrastive Regularisation for Geometrically Aware Sign Language Translation](https://mlsanity.com/paper/2506.00129/) (2025)
- [Gloss-Free Sign Language Translation: An Unbiased Evaluation of Progress in the Field](https://mlsanity.com/paper/2603.13240/) (2026)
- [MaDiS: Taming Masked Diffusion Language Models for Sign Language Generation](https://mlsanity.com/paper/2601.19577/) (2026)
- [RVLF: A Reinforcing Vision-Language Framework for Gloss-Free Sign Language Translation](https://mlsanity.com/paper/2512.07273/) (2025)
- [Diverse Sign Language Translation](https://mlsanity.com/paper/2410.19586/) (2024)
- [SignBind-LLM: Multi-Stage Modality Fusion for Sign Language Translation](https://mlsanity.com/paper/2509.00030/) (2025)
- [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)
- [SignDPO: Multi-level Direct Preference Optimisation for Skeleton-based Gloss-free Sign Language Translation](https://mlsanity.com/paper/2604.18034/) (2026)
- [Selective Contrastive Learning For Gloss Free Sign Language Translation](https://mlsanity.com/paper/2604.22374/) (2026)
- [SLU-2K: A Question-Based Benchmark for Semantic Evaluation of Sign Language Translation](https://mlsanity.com/paper/2606.03788/) (2026)
- [Corpus Augmentation for Sign Language Translation via LLM-Guided Video Stitching](https://mlsanity.com/paper/2606.11925/) (2026)
- [VTaMo: Video-Text Alignment Model for Sign Language Translation](https://mlsanity.com/paper/2607.09126/) (2026)
- [Q-BridgeNet: A Quantization Network for Cross-Lingual Sign Language Translation](https://mlsanity.com/paper/2607.11215/) (2026)
- [Attention-Steered Vision-Language Models for Sign Language Translation](https://mlsanity.com/paper/2608.00235/) (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)
- [SignDino: Self-Supervised Sign Language Representation Learning via Temporal-Axis Self-Distillation](https://mlsanity.com/paper/2609.06296/) (2026)
- [OneSign: Unifying Sign Language Understanding Tasks with One Model](https://mlsanity.com/paper/2609.33090/) (2026)
- [SignFLIP: A Unified Model for Sign Language Translation and Generation via Stage-wise Alignment at Scale](https://mlsanity.com/paper/2609.35225/) (2026)
- [Machine Translation for Sign Languages](https://mlsanity.com/paper/2610.00881/) (2026)
- [ReSCUE: Re-translation with Sentence Commitment for Unsegmented Long-Form Simultaneous Sign Language Translation](https://mlsanity.com/paper/2610.03022/) (2026)

## Abstract (arXiv)

Sign Language Representation Learning (SLRL) is crucial for a range of sign language-related downstream tasks such as Sign Language Translation (SLT) and Sign Language Retrieval (SLRet). Recently, many gloss-based and gloss-free SLRL methods have been proposed, showing promising performance. Among them, the gloss-free approach shows promise for strong scalability without relying on gloss annotations. However, it currently faces suboptimal solutions due to challenges in encoding the intricate, context-sensitive characteristics of sign language videos, mainly struggling to discern essential sign features using a non-monotonic video-text alignment strategy. Therefore, we introduce an innovative pretraining paradigm for gloss-free SLRL, called C2RL, in this paper. Specifically, rather than merely incorporating a non-monotonic semantic alignment of video and text to learn language-oriented sign features, we emphasize two pivotal aspects of SLRL: Implicit Content Learning (ICL) and Explicit Context Learning (ECL). ICL delves into the content of communication, capturing the nuances, emphasis, timing, and rhythm of the signs. In contrast, ECL focuses on understanding the contextual meaning of signs and converting them into equivalent sentences. Despite its simplicity, extensive experiments confirm that the joint optimization of ICL and ECL results in robust sign language representation and significant performance gains in gloss-free SLT and SLRet tasks. Notably, C2RL improves the BLEU-4 score by +5.3 on P14T, +10.6 on CSL-daily, +6.2 on OpenASL, and +1.3 on How2Sign. It also boosts the R@1 score by +8.3 on P14T, +14.4 on CSL-daily, and +5.9 on How2Sign. Additionally, we set a new baseline for the OpenASL dataset in the SLRet task.