Package: transquest
TransQuest: Translation Quality Estimation with Cross-lingual TransformersThe goal of quality estimation (QE) is to evaluate the quality of a translation without having access to a reference translation. High-accuracy QE that can be easily deployed for a number of language pairs is the missing piece in many commercial translation workflows as they have numerous potential uses. They can be employed to select the best translation when several translation engines are available or can inform the end user about the reliability of automatically translated content. In addition, QE systems can be used to decide whether a translation can be published as it is in a given context, or whether it requires human post-editing before publishing or translation from scratch by a human. The quality estimation can be done at different levels: document level, sentence level and word level.With TransQuest, we have opensourced our research in translation quality estimation which also won the sentence-level direct assessment quality estimation shared task inWMT 2020. TransQuest outperforms current open-source quality estimation frameworks such asOpenKiwiandDeepQuest.FeaturesSentence-level translation quality estimation on both aspects: predicting post editing efforts and direct assessment.Word-level translation quality estimation capable of predicting quality of source words, target words and target gaps.Perform significantly better than current state-of-the-art quality estimation methods like DeepQuest and OpenKiwi in all the languages experimented.Pre-trained quality estimation models for fifteen language pairs.Table of ContentsInstallation- Install TransQuest locally using pip.Architectures- Checkout the architectures implemented in TransQuestSentence-level Architectures- We have released two architectures; MonoTransQuest and SiameseTransQuest to perform sentence level quality estimation.Word-level Architecture- We have released MicroTransQuest to perform word level quality estimation.Examples- We have provided several examples on how to use TransQuest in recent WMT quality estimation shared tasks.Sentence-level ExamplesWord-level ExamplesPre-trained Models- We have provided pretrained quality estimation models for fifteen language pairs covering both sentence-level and word-levelSentence-level ModelsWord-level ModelsContact- Contact us for any issues with TransQuestResourcesResearch Seminardone on 1st of October 2020 inRGCLand theslides.CitationsIf you are using the package, please consider citing this paper which is accepted toCOLING 2020@InProceedings{transquest:2020a,author={Ranasinghe,TharinduandOrasan,ConstantinandMitkov,Ruslan},title={TransQuest:TranslationQualityEstimationwithCross-lingualTransformers},booktitle={Proceedingsofthe28thInternationalConferenceonComputationalLinguistics},year={2020}}If you are using the task specific fine tuning, please consider citing this which is accepted toWMT 2020at EMNLP 2020.@InProceedings{transquest:2020b,author={Ranasinghe,TharinduandOrasan,ConstantinandMitkov,Ruslan},title={TransQuestatWMT2020:Sentence-LevelDirectAssessment},booktitle={ProceedingsoftheFifthConferenceonMachineTranslation},year={2020}}