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Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs
To foster progress in this domain, we hold a new shared task at WMT 2023, the first edition of the Discourse-Level Literary Translation.
How Does Pretraining Improve Discourse-Aware Translation?
To bridge this gap, we introduce a probing task to interpret the ability of PLMs to capture discourse relation knowledge.
A Survey on Zero Pronoun Translation
Zero pronouns (ZPs) are frequently omitted in pro-drop languages (e. g. Chinese, Hungarian, and Hindi), but should be recalled in non-pro-drop languages (e. g. English).
A Paradigm Shift: The Future of Machine Translation Lies with Large Language Models
We conclude by emphasizing the critical role of LLMs in guiding the future evolution of MT and offer a roadmap for future exploration in the sector.
Corpora for Document-Level Neural Machine Translation
Instead of translating sentences in isolation, document-level machine translation aims to capture discourse dependencies across sentences by considering a document as a whole.
Chinese-Portuguese Machine Translation: A Study on Building Parallel Corpora from Comparable Texts
The approach we used in this paper also shows a good example on how to boost performance of MT systems for low-resource language pairs.
