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..
Rephrasing the Reference for Non-autoregressive Machine Translation
Authors: Chenze Shao, Jinchao Zhang, Jie Zhou, Yang Feng
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on major WMT benchmarks and NAT baselines show that our approach consistently improves the translation quality of NAT. |
| Researcher Affiliation | Collaboration | Chenze Shao1,2, Jinchao Zhang3, Jie Zhou3, Yang Feng1,2 1 Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences 2 University of Chinese Academy of Sciences 3 Pattern Recognition Center, We Chat AI, Tencent Inc, China EMAIL, EMAIL |
| Pseudocode | No | No pseudocode or algorithm blocks found in the paper. |
| Open Source Code | Yes | Reproducible code: https://github.com/ictnlp/Rephraser-NAT. |
| Open Datasets | Yes | We conducted experiments on major benchmarking datasets in previous NAT studies: WMT14 English German (En De, 4.5M sentence pairs) and WMT16 English Romanian (En Ro, 0.6M sentence pairs). |
| Dataset Splits | Yes | For WMT14 En De, the validation set is newstest2013 and the test set is newstest2014. For WMT16 En Ro, the validation set is newsdev-2016 and the test set is newstest-2016. |
| Hardware Specification | Yes | We use the Ge Force RTX 3090 GPU to train models and measure the translation latency. |
| Software Dependencies | No | The paper mentions optimizers (Adam) and techniques (BPE) but does not provide specific software names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We set αmax to 0.75, αmin to 0.5, the sampling times K to 2, and the depth of rephraser Nr to 2 across all datasets. For CTC, the length for decoder inputs is 3 as long as the source length. All models are optimized with Adam (Kingma and Ba 2014) with β = (0.9, 0.98) and ϵ = 10 8. For vanilla NAT and CTC, each batch contains approximately 64K source words. For CMLM, (...) we use the batch size 128K and use 5 length candidates for inference. All models are pre-trained for 300K steps and fine-tuned for 30K steps. During the fine-tuning, we measure validation BLEU for every 500 steps and average the 5 best checkpoints to obtain the final model. |