OSU Multimodal Machine Translation System Report
원문 초록
This paper describes Oregon State University's submissions to the shared WMT'17 task "multimodal translation task I". In this task, all the sentence pairs are image captions in different languages. The key difference between this task and conventional machine translation is that we have corresponding images as additional information for each sentence pair. In this paper, we introduce a simple but effective system which takes an image shared between different languages, feeding it into the both encoding and decoding side. We report our system's performance for English-French and English-German with Flickr30K (in-domain) and MSCOCO (out-of-domain) datasets. Our system achieves the best performance in TER for English-German for MSCOCO dataset.
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
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
CaMMT: Benchmarking Culturally Aware Multimodal Machine Translation
Multimodal Machine Translation with Visual Scene Graph Pruning
Building a Functional Machine Translation Corpus for Kpelle
토론
이 논문과 연결된 포럼 글이 아직 없습니다.