2 citations · 2 across the 7 of their papers we have counts for
7 papers
STAR : Sentence Translation Alignment Rate for Document-to-Document Machine Translation
Yichen Dong, Hao Wang, Junhui Li +3
Large Language Models (LLMs) have enabled a shift from sentence-level to document-to-document (Doc2Doc) machine translation, promising improved global coherence. However, document-…
Two Intermediate Translations Are Better Than One: Fine-tuning LLMs for Document-level Translation Refinement
Yichen Dong, Xinglin Lyu, Junhui Li +4
Recent research has shown that large language models (LLMs) can enhance translation quality through self-refinement. In this paper, we build on this idea by extending the refinemen…
DoCIA: An Online Document-Level Context Incorporation Agent for Speech Translation
Xinglin Lyu, Wei Tang, Yuang Li +7
Document-level context is crucial for handling discourse challenges in text-to-text document-level machine translation (MT). Despite the increased discourse challenges introduced b…
Improving LLM-based Document-level Machine Translation with Multi-Knowledge Fusion
Bin Liu, Xinglin Lyu, Junhui Li +4
Recent studies in prompting large language model (LLM) for document-level machine translation (DMT) primarily focus on the inter-sentence context by flatting the source document in…
DeMPT: Decoding-enhanced Multi-phase Prompt Tuning for Making LLMs Be Better Context-aware Translators
Xinglin Lyu, Junhui Li, Yanqing Zhao +4
Generally, the decoder-only large language models (LLMs) are adapted to context-aware neural machine translation (NMT) in a concatenating way, where LLMs take the concatenation of…
Enhancing Document-level Translation of Large Language Model via Translation Mixed-instructions
Yachao Li, Junhui Li, Jing Jiang +1
Existing large language models (LLMs) for machine translation are typically fine-tuned on sentence-level translation instructions and achieve satisfactory performance at the senten…
P-Transformer: Towards Better Document-to-Document Neural Machine Translation
Yachao Li, Junhui Li, Jing Jiang +3
Directly training a document-to-document (Doc2Doc) neural machine translation (NMT) via Transformer from scratch, especially on small datasets usually fails to converge. Our dedica…