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Found 7 papers, 6 papers with code
Building Accurate Translation-Tailored LLMs with Language Aware Instruction Tuning
In this work, we design a two-stage fine-tuning algorithm to improve the instruction-following ability (especially the translation direction) of LLMs.
Unlikelihood Tuning on Negative Samples Amazingly Improves Zero-Shot Translation
Zero-shot translation (ZST), which is generally based on a multilingual neural machine translation model, aims to translate between unseen language pairs in training data.
Unsupervised Dense Retrieval with Relevance-Aware Contrastive Pre-Training
Dense retrievers have achieved impressive performance, but their demand for abundant training data limits their application scenarios.
Prompt-Learning for Cross-Lingual Relation Extraction
Experiments on WMT17-EnZh XRE also show the effectiveness of our Prompt-XRE against other competitive baselines.
Vega-MT: The JD Explore Academy Translation System for WMT22
As for model sizes, we scale the Transformer-Big up to the extremely large model that owns nearly 4. 7 Billion parameters, to fully enhance the model capacity for our Vega-MT.
Ranked #1 on
Machine Translation
on WMT 2022 English-Russian
On the Complementarity between Pre-Training and Random-Initialization for Resource-Rich Machine Translation
Pre-Training (PT) of text representations has been successfully applied to low-resource Neural Machine Translation (NMT).
Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation and Understanding
For multilingual sequence-to-sequence pretrained language models (multilingual Seq2Seq PLMs), e. g. mBART, the self-supervised pretraining task is trained on a wide range of monolingual languages, e. g. 25 languages from CommonCrawl, while the downstream cross-lingual tasks generally progress on a bilingual language subset, e. g. English-German, making there exists the data discrepancy, namely domain discrepancy, and cross-lingual learning objective discrepancy, namely task discrepancy, between the pretraining and finetuning stages.
