Think in Latent Thoughts: A Reasoning-Driven Paradigm for Gloss-Free Sign Language Translation

Forum topic · 小凯 · 2026-04-18

Summary

Researchers Yiyang Jiang, Li Zhang, and Xiao-Yong Wei introduce a new reasoning-driven framework for gloss-free Sign Language Translation (SLT), presented in arXiv paper 2504.13083 (April 2025). The work challenges the common assumption that short signing segments map directly to spoken-language words, arguing instead that signers construct meaning dynamically through context, space, and movement, making SLT fundamentally a cross-modal reasoning task rather than simple video-to-text conversion. The proposed framework inserts an ordered sequence of latent thoughts as an explicit intermediate layer between the input video and the generated text, gradually extracting and organizing meaning over time. A plan-then-ground decoding strategy further improves output quality: the model first decides what to say, then retrieves evidence from the video to ground the statement, improving coherence and faithfulness. The authors also release a new large-scale gloss-free SLT dataset with stronger contextual dependence and more realistic meaning. Experiments on multiple benchmarks consistently outperform existing gloss-free methods. Code and data will be made available at the project's GitHub repository upon acceptance.

Paper Overview

Field: Computer Vision (CV)
Authors: Yiyang Jiang, Li Zhang, Xiao-Yong Wei
Published: 2025-04-17
arXiv: 2504.13083

Abstract

Many Sign Language Translation (SLT) systems quietly assume that brief chunks of signing map directly to spoken-language words. That assumption breaks down because signers often create meaning on the fly using context, space, and movement. The authors revisit SLT and argue that it is mainly a cross-modal reasoning task, not just a straightforward video-to-text conversion.

The paper introduces a reasoning-driven SLT framework that uses an ordered sequence of latent thoughts as an explicit middle layer between the video and the generated text. These latent thoughts gradually extract and organize meaning over time.

On top of this, the method employs a plan-then-ground decoding approach: the model first decides what it wants to say, and then looks back at the video to find the evidence. This separation improves coherence and faithfulness.

The authors also build and release a new large-scale gloss-free SLT dataset with stronger contextual dependence and more realistic meaning.

Key Findings

SLT is reframed as a cross-modal reasoning task rather than direct video-to-text conversion.
Latent thoughts serve as an explicit intermediate reasoning layer, capturing meaning that emerges from context, space, and movement.
Plan-then-ground decoding separates content planning from visual grounding, improving coherence and faithfulness of translations.
A new large-scale gloss-free SLT dataset is introduced.
Experiments on multiple benchmarks consistently outperform existing gloss-free methods.

Code and data will be released at: https://github.com/fletcherjiang/SignThought

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