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..
Divergence-Guided Simultaneous Speech Translation
Authors: Xinjie Chen, Kai Fan, Wei Luo, Linlin Zhang, Libo Zhao, Xinggao Liu, Zhongqiang Huang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on multiple translation directions of the Mu ST-C benchmark demonstrate that our approach achieves a better trade-off between translation quality and latency compared to existing methods. |
| Researcher Affiliation | Collaboration | 1Zhejiang University 2Alibaba DAMO Academy 3South China University of Technology |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/cxjfluffy/Di G-SST. |
| Open Datasets | Yes | We conduct experiments on the widely used Mu ST-C V1 corpus: English {German, Spanish, French} (En {De, Es, Fr}) (Gangi et al. 2019), detailed in Table 1. |
| Dataset Splits | Yes | Table 1: The statistics (sentences) of three language pairs in Mu ST-C. Split En-De En-Es En-Er Train 234K 270K 280K Dev 1423 1316 1412 Tst-COMMON 2641 2502 2632 |
| Hardware Specification | Yes | Training was conducted on 4 V100 GPUs, each with a batch size of 3.2M audio frames. |
| Software Dependencies | No | The paper mentions software tools like 'wav2vec 2.0', 'sacreBLEU', 'simuleval toolkit', 'Sentence Piece', and 'Montreal Forced Aligner', but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Both the translation encoder and decoder employ 6 transformer layers, each with dimensions of 512 and 8 attention heads... Training was conducted on 4 V100 GPUs, each with a batch size of 3.2M audio frames. The translation model was trained for up to 40 epochs with early stopping after 20 non-improving epochs, followed by a 10-epoch policy module training with the translation model frozen. |