TL;DR
This study improves the structural accuracy of translating long and complex sentences, providing a technical path reference for the construction of highly reliable machine translation systems.
English grammatical structure parsing is a core task in natural language processing, playing a crucial role in machine translation, information extraction, and other applications. This paper proposes an innovative model, TG-Parser, that combines a Transformer and a graph convolutional network (GCN). It significantly im...
Machine translation for local languages remains challenging due to the scarcity of data. This challenge also occurs with languages that have dialects. In this study, we focus on the translation of Buginese dialects, an underexplored area in neural machine translation research. To support this investigation, we introduc...
This dissertation presents an approach to MT that addresses the lack of symmetry by exploiting symbolic and statistical target language resources in source-poor/target-rich language pairs, called Generation-Heavy Hybrid Machine Translation (GHMT).
Cross-domain and source-target mismatch tests show that the MCA-LLM-QEER framework improves robustness to domain shift and fluent-butunfaithful translations and support a unified reference-free QE design in which global quality prediction and fine-grained error diagnosis are learned from complementary bilingual evidenc...
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