DiscoSign handles sign language glosses better than standard LLMs by tracking spatial

Most text-to-gloss translation fails because it treats every sentence as an isolated unit. In actual sign language, a signer assigns a specific point in space to a person or object and refers back to that same point throughout the conversation. Standard LLMs usually lose this "spatial memory," resulting in glosses that are grammatically correct per sentence but nonsensical in a continuous discourse. DiscoSign attempts to fix this by adding a discourse-aware layer to the translation pipeline.

How the discourse-aware framework actually works

The system doesn't just prompt a model to translate; it uses a modular approach to handle three specific linguistic hurdles that usually break basic translation scripts.

1. Spatial Coreference Resolution
Instead of translating "the man" every time it appears, the system identifies the entity and maintains a spatial anchor. This prevents the "forgetting" effect where a model might gloss a subject in the first sentence but fail to link it to the same spatial location in the third.

2. Question-Answer Clauses (QACs)
Sign languages often use pseudoclefts where a question is posed and answered within a single spatial shift. DiscoSign identifies these structures to ensure the gloss reflects the rhetorical shift rather than a literal word-for-word translation.

3. Ellipsis and Redundancy
The model filters out redundant words that are common in spoken English but omitted in sign language, preventing the gloss from feeling "cluttered" or unnatural to a native signer.

Performance against baseline LLMs

When comparing this to a vanilla GPT-4 or Claude 3.5 Sonnet prompt for glossing, the difference is most apparent in multi-sentence paragraphs.

• Consistency: Standard LLMs often fluctuate in how they gloss the same noun across a paragraph. DiscoSign maintains a consistent gloss mapping.
• Naturalness: By handling QACs, the output avoids the "robotic" feel of direct translation.
• Accuracy: In tests involving complex discourse, the modular framework reduces the frequency of "hallucinated" glosses that don't exist in the target sign language vocabulary.

Implementing a discourse-aware prompt

If you don't have the full DiscoSign framework and are trying to mimic this behavior with a standard LLM, you can't just ask for a translation. You need to force the model to track entities. I found that adding a "spatial registry" block to the prompt helps.

{
  "spatial_registry": {
    "entity_1": "Location_A",
    "entity_2": "Location_B"
  },
  "instruction": "Translate the following text to gloss. Maintain the spatial registry across all sentences. If an entity is mentioned, refer to its assigned location."
}

When to avoid this approach

This level of complexity is overkill for single-sentence translations. If you are just translating "Where is the bathroom?" the overhead of discourse tracking is useless. Use this specifically for narratives, long-form storytelling, or technical explanations where the same subjects are mentioned repeatedly over 3+ sentences. The main risk is "over-correction," where the model might omit a necessary noun because it thinks the spatial reference is enough, making the gloss ambiguous to someone not familiar with the specific spatial layout of the conversation.