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Found 17 papers, 1 papers with code
An Exploratory Study on Multilingual Quality Estimation
Predicting the quality of machine translation has traditionally been addressed with language-specific models, under the assumption that the quality label distribution or linguistic features exhibit traits that are not shared across languages.
Are we Estimating or Guesstimating Translation Quality?
Recent advances in pre-trained multilingual language models lead to state-of-the-art results on the task of quality estimation (QE) for machine translation.
Multi-Hypothesis Machine Translation Evaluation
Reliably evaluating Machine Translation (MT) through automated metrics is a long-standing problem.
The FLORES Evaluation Datasets for Low-Resource Machine Translation: Nepali--English and Sinhala--English
For machine translation, a vast majority of language pairs in the world are considered low-resource because they have little parallel data available.
Findings of the WMT 2019 Shared Task on Parallel Corpus Filtering for Low-Resource Conditions
Following the WMT 2018 Shared Task on Parallel Corpus Filtering, we posed the challenge of assigning sentence-level quality scores for very noisy corpora of sentence pairs crawled from the web, with the goal of sub-selecting 2{\%} and 10{\%} of the highest-quality data to be used to train machine translation systems.
Machine Translation Evaluation for Arabic using Morphologically-enriched Embeddings
Evaluation of machine translation (MT) into morphologically rich languages (MRL) has not been well studied despite posing many challenges.
