FAME-MT Dataset: Formality Awareness Made Easy for Machine Translation Purposes
원문 초록
People use language for various purposes. Apart from sharing information, individuals may use it to express emotions or to show respect for another person. In this paper, we focus on the formality level of machine-generated translations and present FAME-MT -- a dataset consisting of 11.2 million translations between 15 European source languages and 8 European target languages classified to formal and informal classes according to target sentence formality. This dataset can be used to fine-tune machine translation models to ensure a given formality level for each European target language considered. We describe the dataset creation procedure, the analysis of the dataset's quality showing that FAME-MT is a reliable source of language register information, and we present a publicly available proof-of-concept machine translation model that uses the dataset to steer the formality level of the translation. Currently, it is the largest dataset of formality annotations, with examples expressed in 112 European language pairs. The dataset is published online: https://github.com/laniqo-public/fame-mt/ .
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Intrinsic vs. Extrinsic Evaluation of Czech Sentence Embeddings: Semantic Relevance Doesn't Help with MT Evaluation
ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT
Building a Functional Machine Translation Corpus for Kpelle
Combining the Best of Both Worlds: A Method for Hybrid NMT and LLM Translation
Same evaluation, more tokens: On the effect of input length for machine translation evaluation using Large Language Models
CycleDistill: Bootstrapping Machine Translation using LLMs with Cyclical Distillation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.