arXiv:2104.12128
공식 코드 · Papers with Code
Automatic Post-Editing for Vietnamese
원문 초록
Automatic post-editing (APE) is an important remedy for reducing errors of raw translated texts that are produced by machine translation (MT) systems or software-aided translation. In this paper, we present a systematic approach to tackle the APE task for Vietnamese. Specifically, we construct the first large-scale dataset of 5M Vietnamese translated and corrected sentence pairs. We then apply strong neural MT models to handle the APE task, using our constructed dataset. Experimental results from both automatic and human evaluations show the effectiveness of the neural MT models in handling the Vietnamese APE task.
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.