What Works When in Context-aware Neural Machine Translation?
Egileak: Gorka Labaka
Data: 12.06.2023
Abstract
Document-level Machine Translation has emerged as a promising means to enhance automated translation quality, but it is currently unclear how effectively context-aware models use the available context during translation. This paper aims to provide insight into the current state of models based on input concatenation, with an in-depth evaluation on English–German and English–French standard datasets. We notably evaluate the impact of data bias, antecedent part-of-speech, context complexity, and the syntactic function of the elements involved in discourse phenomena. Our experimental results indicate that the selected models do improve the overall translation in context, with varying sensitivity to the different factors we examined. We notably show that the selected context-aware models operate markedly better on regular syntactic configurations involving subject antecedents and pronouns, with degraded performance as the configurations become more dissimilar.
BIB_text
title = {What Works When in Context-aware Neural Machine Translation?},
pages = {147-156},
keywds = {
Computational linguistics; Computer aided language translation; Neural machine translation
}
abstract = {
Document-level Machine Translation has emerged as a promising means to enhance automated translation quality, but it is currently unclear how effectively context-aware models use the available context during translation. This paper aims to provide insight into the current state of models based on input concatenation, with an in-depth evaluation on English–German and English–French standard datasets. We notably evaluate the impact of data bias, antecedent part-of-speech, context complexity, and the syntactic function of the elements involved in discourse phenomena. Our experimental results indicate that the selected models do improve the overall translation in context, with varying sensitivity to the different factors we examined. We notably show that the selected context-aware models operate markedly better on regular syntactic configurations involving subject antecedents and pronouns, with degraded performance as the configurations become more dissimilar.
}
isbn = {978-952032947-1},
date = {2023-06-12},
}
