2019/10/16 by Valentin Macé, Macé, Valentin, Christophe Servan +1 · 25 citations
Computer Science · #Artificial intelligence #Coherence (philosophical gambling strategy) #Computation and Language (cs.CL) #Computer science #Context (archaeology) #Evaluation of machine translation #Example-based machine translation #FOS: Computer and information sciences #German #Language model #Linguistics #Machine translation #Machine translation software usability #Natural Language Processing Techniques #Natural language processing #Text Readability and Simplification #Topic Modeling #Transformer #Translation (biology) #Word (group theory) #cs.CL
paper · pdf · doi:10.48550/arxiv.1910.07481
published in arXiv (Cornell University) (Cornell University) · Accepted paper to IWSLT2019
arxiv created 2019/10/16 · openalex publication_date 2019/10/16 · arxiv updated 2019/10/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
In Machine Translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a simple yet promising approach to add contextual information in Neural Machine Translation. We present a method to add source context that capture the whole document with accurate boundaries, taking every word into account. We provide this additional information to a Transformer model and study the impact of our method on three language pairs. The proposed approach obtains promising results in the English-German, English-French and French-English document-level translation tasks. We observe interesting cross-sentential behaviors where the model learns to use document-level information to improve translation coherence.