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..
Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation
Authors: Chenze Shao, Yang Feng
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on major WMT benchmarks show that our method substantially improves the translation performance of CTC-based models. Our best model achieves 30.06 BLEU on WMT14 En-De with only one-iteration decoding, closing the gap between non-autoregressive and autoregressive models.2 |
| Researcher Affiliation | Academia | Chenze Shao1,2, Yang Feng1,2 1Key Laboratory of Intelligent Information Processing Institute of Computing Technology, Chinese Academy of Sciences 2University of Chinese Academy of Sciences |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Source code: https://github.com/ictnlp/NMLA-NAT. |
| Open Datasets | Yes | Datasets We evaluate our methods on the most widely used public benchmarks in previous NAT studies: WMT14 English$German (En$De, 4.5M sentence pairs) [5] and WMT16 English$Romanian (En$Ro, 0.6M sentence pairs) [6]. |
| 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 | We implement our models based on the open-source framework of fairseq [MIT License, 31]. |
| Experiment Setup | Yes | On WMT14 En$De, we use a dropout rate of 0.2 to train NAT models and use a dropout rate of 0.1 for finetuning. On WMT16 En$Ro, the dropout rate is 0.3 for both the pretraining and finetuning. We use the batch size 64K and train NAT models for 300K steps on WMT14 En$De and 150K steps on WMT16 En$Ro. During the finetuning, we train NAT models for 6K steps with the batch size 256K. All models are optimized with Adam [27] with β = (0.9, 0.98) and = 10 8. The learning rate warms up to 5 10 4 within 10K steps in the pretraining and warms up to e 10 4 within 500 steps in the finetuning. |