Selecting Artificially-Generated Sentences for Fine-Tuning Neural Machine Translation
Neural Machine Translation (NMT) models tend to achieve best performance when larger sets of parallel sentences are provided for training. For this reason, augmenting the training set with artificially-generated sentence pairs can boost performance. Nonetheless, the performance can also be improved with a small number of sentences if they are in the same domain as the test set. Accordingly, we want to explore the use of artificially-generated sentences along with data-selection algorithms to improve German-to-English NMT models trained solely with authentic data. In this work, we show how artificially-generated sentences can be more beneficial than authentic pairs, and demonstrate their advantages when used in combination with data-selection algorithms.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationNMTSentenceTranslationSimilar Papers 제목 키워드 기반
Not Enough Data? Deep Learning to the Rescue!
Based on recent advances in natural language modeling and those in text generation capabilities, we propose a novel data augmentation method for text classification tasks. We use a powerful pre-trained neural network mod…
Data AugmentationDeep LearningGeneral ClassificationLAMBADA+5Neural Grammatical Error Correction for Romanian
Resources for Grammatical Error Correction (GEC) in non-English languages are scarce, while available spellcheckers in these languages are mostly limited to simple corrections and rules. In this paper we introduce a firs…
Grammatical Error CorrectionBanglaNirTox: A Large-scale Parallel Corpus for Explainable AI in Bengali Text Detoxification
Toxic language in Bengali remains prevalent, especially in online environments, with few effective precautions against it. Although text detoxification has seen progress in high-resource languages, Bengali remains undere…
Artificial Error Generation with Machine Translation and Syntactic Patterns
Shortage of available training data is holding back progress in the area of automated error detection. This paper investigates two alternative methods for artificially generating writing errors, in order to create additi…
Grammatical Error DetectionMachine TranslationTranslationSemEval-2022 Task 3: PreTENS-Evaluating Neural Networks on Presuppositional Semantic Knowledge
We report the results of the SemEval 2022 Task 3, PreTENS, on evaluation the acceptability of simple sentences containing constructions whose two arguments are presupposed to be or not to be in an ordered taxonomic relat…
Data Augmentation