198 research outputs found
Contextual Refinement of Translations: Large Language Models for Sentence and Document-Level Post-Editing
Large Language Models (LLM's) have demonstrated considerable success in
various Natural Language Processing tasks, but they have yet to attain
state-of-the-art performance in Neural Machine Translation (NMT). Nevertheless,
their significant performance in tasks demanding a broad understanding and
contextual processing shows their potential for translation. To exploit these
abilities, we investigate using LLM's for MT and explore recent
parameter-efficient fine-tuning techniques. Surprisingly, our initial
experiments find that fine-tuning for translation purposes even led to
performance degradation. To overcome this, we propose an alternative approach:
adapting LLM's as Automatic Post-Editors (APE) rather than direct translators.
Building on the LLM's exceptional ability to process and generate lengthy
sequences, we also propose extending our approach to document-level
translation. We show that leveraging Low-Rank-Adapter fine-tuning for APE can
yield significant improvements across both sentence and document-level metrics
while generalizing to out-of-domain data. Most notably, we achieve a
state-of-the-art accuracy rate of 89\% on the ContraPro test set, which
specifically assesses the model's ability to resolve pronoun ambiguities when
translating from English to German. Lastly, we investigate a practical scenario
involving manual post-editing for document-level translation, where reference
context is made available. Here, we demonstrate that leveraging human
corrections can significantly reduce the number of edits required for
subsequent translations\footnote{Interactive Demo for integrating manual
feedback can be found
\href{https://huggingface.co/spaces/skoneru/contextual_refinement_ende}{here}
Discrimination, narratives and family history: an experiment with Jordanian host and Syrian refugee children
We measure the prevalence of discrimination between Jordanian host and Syrian refugee children attending school in Jordan. Using a simple sharing experiment, we find only little discrimination. Among the Jordanian children, however, we see that those who descended from Palestinian refugees do not discriminate at all, suggesting that a family history of refugee status can generate solidarity with new refugees. We also find that parents' narratives about the refugee crisis are correlated with the degree of discrimination, suggesting that discriminatory preferences are being transmitted through parental attitudes
Edinburgh's Statistical Machine Translation Systems for WMT16
This paper describes the University of Edinburgh’s
phrase-based and syntax-based
submissions to the shared translation tasks
of the ACL 2016 First Conference on Machine
Translation (WMT16). We submitted
five phrase-based and five syntaxbased
systems for the news task, plus one
phrase-based system for the biomedical
task
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