Denoising Pre-training for Machine Translation Quality Estimation with Curriculum Learning
Authors: Xiang Geng, Yu Zhang, Jiahuan Li, Shujian Huang, Hao Yang, Shimin Tao, Yimeng Chen, Ning Xie, Jiajun Chen
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on various benchmarks reveal that CLQE outperforms Direct QE and other strong baselines. |
| Researcher Affiliation | Collaboration | 1 National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing, China 2 Huawei Translation Services Center, Beijing, China {gx, zhangy, lijh}@smail.nju.edu.cn, {huangsj, chenjj}@nju.edu.cn, {yanghao30, taoshimin, chenyimeng, nicolas.xie}@huawei.com |
| Pseudocode | Yes | Algorithm 1: Denoising pre-training for machine translation quality estimation. |
| Open Source Code | Yes | We make our CLQE code available (https://github.com/ NJUNLP/njuqe). ... We provide our implementation online.5 |
| Open Datasets | Yes | We employ WMT19 and WMT20/WMT214 QE dataset for English-German (EN-DE) and English-Chinese (EN-ZH) direction respectively. ... https://www.statmt.org/wmt##, ## can be 19, 20, 21. |
| Dataset Splits | Yes | The size of training, development, and test sets are 13K/1K/1K, 7K/1K/1K, and 8K/1K/1K for WMT19, 20, and 21 QE tasks, respectively. ...the pre-trained model is selected with the pseudo validation set for further fine-tuning. |
| Hardware Specification | Yes | All experiments are performed on NVIDIA V100 GPUs. |
| Software Dependencies | No | The paper mentions using Fairseq(-py) but does not provide a specific version number. Other mentioned software or models (e.g., XLM-R, GPT-2) do not include version details for their implementations. |
| Experiment Setup | Yes | We set the initial competence c0 = 0.05 and total duration of curriculum learning T = 5 epochs. Other details can be found in supplementary materials. |