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..
Improving Stylized Neural Machine Translation with Iterative Dual Knowledge Transfer
Authors: Xuanxuan Wu, Jian Liu, Xinjie Li, Jinan Xu, Yufeng Chen, Yujie Zhang, Hui Huang
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiment results demonstrate the effectiveness of our method, achieving an improvement over the existing best model by 5 BLEU points on MTFC dataset. We conduct experiments on two benchmark datasets, MTFC and GYAFC, and achieves state-of-the-art results in producing stylized translation sentences based on both automatic and human evaluation. |
| Researcher Affiliation | Collaboration | 1Beijing Jiaotong University, Beijing, China 2Global Tone Communication Technology Co., Ltd., Beijing, China |
| Pseudocode | Yes | Algorithm 1 Iterative Dual Knowledge Transfer for Improving Stylized NMT |
| Open Source Code | Yes | Code and data are available at https://github.com/mt887/IDKT |
| Open Datasets | Yes | We use two datasets to evaluate our proposed method, the size of each training dataset is presented in Table 2. MTFC. Machine Translation Formality Corpus (MTFC)... GYAFC. We use Grammarly s Yahoo Corpus Dataset (GYAFC)... Code and data are available at https://github.com/mt887/IDKT |
| Dataset Splits | Yes | The size of each training dataset is presented in Table 2. Dataset Train Valid Test GYAFC (E&M) 52k 2877 1416 GYAFC (F&R) 52k 2788 1432 MTFC 14280k 2877 1416 |
| Hardware Specification | Yes | We train our models with Adam [Kingma and Ba, 2015] optimizer using β1=0.9 β2=0.98 on 2 NVIDIA 2080Ti GPUs. |
| Software Dependencies | No | The paper mentions software tools like Fairseq, BART-large, and BERT, but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The dimensionality of all input and output layers is 1024, and that of FFN layer is 4096. Both the encoder and decoder have 6 layers with 8 attention heads. We train our models with Adam [Kingma and Ba, 2015] optimizer using β1=0.9 β2=0.98. |