模型:
TransQuest/siamesetransquest-da-multilingual
质量估计(QE)的目标是在没有参考翻译的情况下评估翻译的质量。高精度的QE可以轻松应用于多种语言对,这在许多商业翻译工作流中是缺失的一环,因为它们有许多潜在的用途。它们可用于在提供多个翻译引擎时选择最佳翻译,或者可以向最终用户提供有关自动翻译内容的可靠性的信息。此外,QE系统可用于决定是否可以在给定的上下文中发布翻译,或者是否需要在发布之前进行人工后编辑或由人工重新翻译。质量估计可以在不同的级别上进行:文档级别,句子级别和单词级别。
通过TransQuest,我们将我们在翻译质量估计方面的研究开源,并且在句子级直接评估质量估计共享任务中获得了胜利。TransQuest在性能上超过了当前的开源质量估计框架,如 OpenKiwi 和 DeepQuest 。
pip install transquest
git clone https://github.com/TharinduDR/TransQuest.git cd TransQuest pip install -r requirements.txt
import torch from transquest.algo.sentence_level.siamesetransquest.run_model import SiameseTransQuestModel model = SiameseTransQuestModel("TransQuest/siamesetransquest-da-multilingual") predictions = model.predict([["Reducerea acestor conflicte este importantă pentru conservare.", "Reducing these conflicts is not important for preservation."]]) print(predictions)
更多详细信息请参阅文档。
如果您使用的是单词级别的架构,请考虑引用此论文,该论文已被 ACL 2021 接受。
@InProceedings{ranasinghe2021, author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan}, title = {An Exploratory Analysis of Multilingual Word Level Quality Estimation with Cross-Lingual Transformers}, booktitle = {Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics}, year = {2021} }
如果您使用的是句子级别的架构,请考虑引用以下论文,这些论文已在 COLING 2020 和 WMT 2020 的EMNLP 2020会议上发表。
@InProceedings{transquest:2020a, author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan}, title = {TransQuest: Translation Quality Estimation with Cross-lingual Transformers}, booktitle = {Proceedings of the 28th International Conference on Computational Linguistics}, year = {2020} }
@InProceedings{transquest:2020b, author = {Ranasinghe, Tharindu and Orasan, Constantin and Mitkov, Ruslan}, title = {TransQuest at WMT2020: Sentence-Level Direct Assessment}, booktitle = {Proceedings of the Fifth Conference on Machine Translation}, year = {2020} }