A Bilingual Parallel Corpus with Discourse Annotations
원문 초록
Machine translation (MT) has almost achieved human parity at sentence-level translation. In response, the MT community has, in part, shifted its focus to document-level translation. However, the development of document-level MT systems is hampered by the lack of parallel document corpora. This paper describes BWB, a large parallel corpus first introduced in Jiang et al. (2022), along with an annotated test set. The BWB corpus consists of Chinese novels translated by experts into English, and the annotated test set is designed to probe the ability of machine translation systems to model various discourse phenomena. Our resource is freely available, and we hope it will serve as a guide and inspiration for more work in document-level machine translation.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Document-Level Text Generation with Minimum Bayes Risk Decoding using Optimal Transport
GRAFT: A Graph-based Flow-aware Agentic Framework for Document-level Machine Translation
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
토론
이 논문과 연결된 포럼 글이 아직 없습니다.