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Found 9 papers, 1 papers with code
A Review of Discourse-level Machine Translation
Machine translation (MT) models usually translate a text at sentence level by considering isolated sentences, which is based on a strict assumption that the sentences in a text are independent of one another.
Comparison of the effects of attention mechanism on translation tasks of different lengths of ambiguous words
This paper focuses on the performance of encoder decoder attention mechanism in word sense disambiguation task with different text length, trying to find out the influence of context marker on attention mechanism in word sense disambiguation task.
Gradable ChatGPT Translation Evaluation
ChatGPT, as a language model based on large-scale pre-training, has exerted a profound influence on the domain of machine translation.
Step fusion: Local and global mutual guidance
Feature alignment is the primary means of fusing multimodal data.
Ranked #1 on
Arrhythmia Detection
on MIT-BIH Arrhythmia Database
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.
Semi-automatic Simultaneous Interpreting Quality Evaluation
Increasing interpreting needs a more objective and automatic measurement.
Automatic Construction of Discourse Corpora for Dialogue Translation
Then tags such as speaker and discourse boundary from the script data are projected to its subtitle data via an information retrieval approach in order to map monolingual discourse to bilingual texts.
A Novel Approach to Dropped Pronoun Translation
Finally, we integrate the above outputs into our translation system to recall missing pronouns by both extracting rules from the DP-labelled training data and translating the DP-generated input sentences.
