---
license: other
license_name: non-commercial-research
library_name: transformers
base_model: facebook/mbart-large-cc25
pipeline_tag: translation
tags:
  - sign-language
  - sign-language-production
  - text-to-gloss
  - retrieval
language:
  - de
  - zh
datasets:
  - LionelLow/SignSparK_data
---

# SignSparK-Retrieval

Text-to-gloss models and isolated-sign annotations for the retrieval pipeline of
[SignSparK](https://github.com/JianHe0628/SignSparK): text → glosses → retrieved signs →
stitched SignSparK LMDB → SignSparK fills the transitions.

Code: [JianHe0628/SignSparK_Retrieval](https://github.com/JianHe0628/SignSparK_Retrieval).
Pose data: [LionelLow/SignSparK_data](https://huggingface.co/datasets/LionelLow/SignSparK_data).

## Contents

| Path | What |
|---|---|
| `t2g/<dataset>/` | fine-tuned text-to-gloss model (Hugging Face MBart export) |
| `annotations/<prefix>.{train,dev,test}` | isolated-sign spans, used to build the sign dictionaries |
| `annotations/phoenix_iso_signers.tsv` | PHOENIX-2014T video → signer, for signer-aware retrieval |
| `predictions/<dataset>/{dev,test}_pres.txt` | the paper's original T2G outputs |
| `base/<dataset>/` | vocabulary-trimmed mbart-large-cc25, only needed to train T2G |

`<dataset>` is `CSL-Daily` or `PHOENIX-2014T`; `<prefix>` is `csl_iso` or `phoenix_iso`.



## Text-to-gloss

Gloss-level sacreBLEU and WER (%) against the raw LMDB glosses.

| Model | dev BLEU-4 | dev WER | test BLEU-4 | test WER |
|---|---|---|---|---|
| `t2g/PHOENIX-2014T` | 24.53 | 55.02 | 21.01 | 58.92 |
| `t2g/CSL-Daily` | 30.05 | 49.18 | 29.48 | 49.49 |


Our released T2G models have been retrained, but score similarly to the original paper's values. Note that our reproduced retrieval and SignSparK results are in the
[code README](https://github.com/JianHe0628/SignSparK_Retrieval#results).

## Annotations

`annotations/<prefix>.<split>` is a pickled list with one dict per sign occurrence:
`{"video_file", "label", "start", "end", "seq_len"}`. `start`/`end` are frame indices into
the SignSparK LMDB clip of `video_file` (`end` exclusive).

## License

The models are fine-tuned from [mbart-large-cc25](https://huggingface.co/facebook/mbart-large-cc25)
(vocabulary-trimmed as in [GFSLT-VLP](https://github.com/zhoubenjia/GFSLT-VLP)). The models,
annotations and predictions are derived from PHOENIX-2014T and CSL-Daily and are provided
for **non-commercial research use only**, under the terms of those datasets.

## Citation

```bibtex
@inproceedings{low2026signspark,
  title={SignSparK: Efficient Multilingual Sign Language Production via Sparse Keyframe Learning},
  author={Low, Jianhe and Symeonidis-Herzig, Alexandre and Ivashechkin, Maksym and Sincan, Ozge Mercanoglu and Bowden, Richard},
  booktitle={European Conference on Computer Vision},
  pages={648--670},
  year={2026},
  organization={Springer}
}
```