arXiv:1601.02502
Trans-gram, Fast Cross-lingual Word-embeddings
원문 초록
We introduce Trans-gram, a simple and computationally-efficient method to simultaneously learn and align wordembeddings for a variety of languages, using only monolingual data and a smaller set of sentence-aligned data. We use our new method to compute aligned wordembeddings for twenty-one languages using English as a pivot language. We show that some linguistic features are aligned across languages for which we do not have aligned data, even though those properties do not exist in the pivot language. We also achieve state of the art results on standard cross-lingual text classification and word translation tasks.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
BeaverTalk: Oregon State University's IWSLT 2025 Simultaneous Speech Translation System
Intrinsic vs. Extrinsic Evaluation of Czech Sentence Embeddings: Semantic Relevance Doesn't Help with MT Evaluation
End-to-End Speech Translation for Low-Resource Languages Using Weakly Labeled Data
ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT
Building a Functional Machine Translation Corpus for Kpelle
SlangDIT: Benchmarking LLMs in Interpretative Slang Translation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.