The first shared task on MT robustness received 23 submissions showing up to +22.33 BLEU gains on noisy Reddit data, with strong human-BLEU correlation.
Six Challenges for Neural Machine Translation
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
abstract
We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. We show both deficiencies and improvements over the quality of phrase-based statistical machine translation.
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
Survey organizes LLM trustworthiness into seven categories and 29 sub-categories, measures eight sub-categories on popular models, and finds that more aligned models generally score higher but with varying effectiveness.
Systematic review of Semantic Web-enhanced machine translation approaches finds they can improve output quality but the integration remains in its infancy.
citing papers explorer
-
Findings of the First Shared Task on Machine Translation Robustness
The first shared task on MT robustness received 23 submissions showing up to +22.33 BLEU gains on noisy Reddit data, with strong human-BLEU correlation.
-
Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment
Survey organizes LLM trustworthiness into seven categories and 29 sub-categories, measures eight sub-categories on popular models, and finds that more aligned models generally score higher but with varying effectiveness.
-
Semantic Web for Machine Translation: Challenges and Directions
Systematic review of Semantic Web-enhanced machine translation approaches finds they can improve output quality but the integration remains in its infancy.