Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
SLTUNET: A Simple Unified Model for Sign Language Translation
Authors: Biao Zhang, Mathias Müller, Rico Sennrich
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show in experiments that SLTUNET achieves competitive and even state-of-the-art performance on PHOENIX-2014T and CSL-Daily when augmented with MT data and equipped with a set of optimization techniques. We further use the DGS Corpus for end-to-end SLT for the first time. It covers broader domains with a significantly larger vocabulary, which is more challenging and which we consider to allow for a more realistic assessment of the current state of SLT than the former two. Still, SLTUNET obtains improved results on the DGS Corpus. |
| Researcher Affiliation | Academia | 1 School of Informatics, University of Edinburgh 2 Department of Computational Linguistics, University of Zurich |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/bzhang Go/sltunet. |
| Open Datasets | Yes | We work on three SLT datasets: PHOENIX-2014T (Camgoz et al., 2018), CSL-Daily (Zhou et al., 2021), and DGS3-T. |
| Dataset Splits | Yes | The split contains 60,306, 967, and 1,575 samples in the train, dev, and test set, respectively (see Table 1 and Appendix A.2 for details). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like Moses and Sacre BLEU with citations but does not provide specific version numbers for them or other key software dependencies used in the experiments. |
| Experiment Setup | Yes | We experiment with Transformer (Vaswani et al., 2017) and start our analysis with a Baseline system optimized on Sign2Text alone with the following configurations: encoder and decoder layers of N S enc = 2, N P enc = 0 and Ndec = 2 respectively, model dimension of d = 512, feed-forward dimension of dff = 2048, attention head of h = 8, and no CTC regularization. ... We train all SLT models using Adam (β1 = 0.9, β2 = 0.998) (Kingma & Ba, 2015) with Noam learning rate schedule (Vaswani et al., 2017), a label smoothing of 0.1 and warmup step of 4K. We employ Xavier initialization to initialize model parameters with a gain of 1.0. |