arXiv:2401.17827
Neural Machine Translation for Malayalam Paraphrase Generation
원문 초록
This study explores four methods of generating paraphrases in Malayalam, utilizing resources available for English paraphrasing and pre-trained Neural Machine Translation (NMT) models. We evaluate the resulting paraphrases using both automated metrics, such as BLEU, METEOR, and cosine similarity, as well as human annotation. Our findings suggest that automated evaluation measures may not be fully appropriate for Malayalam, as they do not consistently align with human judgment. This discrepancy underscores the need for more nuanced paraphrase evaluation approaches especially for highly agglutinative languages.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Do Not Change Me: On Transferring Entities Without Modification in Neural Machine Translation -- a Multilingual Perspective
Calibrating Translation Decoding with Quality Estimation on LLMs
Beyond Decoder-only: Large Language Models Can be Good Encoders for Machine Translation
ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits
THOR-MoE: Hierarchical Task-Guided and Context-Responsive Routing for Neural Machine Translation
토론
이 논문과 연결된 포럼 글이 아직 없습니다.