Improving Jejueo-Korean Translation With Cross-Lingual Pretraining Using Japanese and Korean
Jejueo is a critically endangered language spoken on Jeju Island and is closely related to but mutually unintelligible with Korean. Parallel data between Jejueo and Korean is scarce, and translation between the two languages requires more attention, as current neural machine translation systems typically rely on large amounts of parallel training data. While low-resource machine translation has been shown to benefit from using additional monolingual data during the pretraining process, not as much research has been done on how to select languages other than the source and target languages for use during pretraining. We show that using large amounts of Korean and Japanese data during the pretraining process improves translation by 2.16 BLEU points for translation in the Jejueo → Korean direction and 1.34 BLEU points for translation in the Korean → Jejueo direction compared to the baseline.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationTranslationSimilar Papers 제목 키워드 기반
Jejueo Datasets for Machine Translation and Speech Synthesis
Jejueo was classified as critically endangered by UNESCO in 2010. Although diverse efforts to revitalize it have been made, there have been few computational approaches. Motivated by this, we construct two new Jejueo dat…
Machine TranslationSpeech SynthesisTranslationDiscovering Lexical Gaps Using Embeddings from Multilingual LLMs
Lexical gaps are words that do not exist in certain languages. They pose challenges for building multilingual lexical resources, for machine translation, and for cross-lingual transfer. Existing lexical gap detection rel…
Cross-Lingual TransferSemantic SimilarityMachine TranslationKORMo: Korean Open Reasoning Model for Everyone
This work presents the first large-scale investigation into constructing a fully open bilingual large language model (LLM) for a non-English language, specifically Korean, trained predominantly on synthetic data. We intr…
UA-KO at SemEval-2022 Task 11: Data Augmentation and Ensembles for Korean Named Entity Recognition
This paper presents the approaches and systems of the UA-KO team for the Korean portion of SemEval-2022 Task 11 on Multilingual Complex Named Entity Recognition.We fine-tuned Korean and multilingual BERT and RoBERTA mode…
Data Augmentationnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean
Large language models (LLMs) use pretraining to predict the subsequent word; however, their expansion requires significant computing resources. Numerous big tech companies and research institutes have developed multiling…