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Found 7 papers, 3 papers with code
Findings of the WMT 2020 Shared Task on Quality Estimation
We report the results of the WMT20 shared task on Quality Estimation, where the challenge is to predict the quality of the output of neural machine translation systems at the word, sentence and document levels.
Predicting Attention Sparsity in Transformers
Transformers' quadratic complexity with respect to the input sequence length has motivated a body of work on efficient sparse approximations to softmax.
MLQE-PE: A Multilingual Quality Estimation and Post-Editing Dataset
We present MLQE-PE, a new dataset for Machine Translation (MT) Quality Estimation (QE) and Automatic Post-Editing (APE).
Revisiting Higher-Order Dependency Parsers
Neural encoders have allowed dependency parsers to shift from higher-order structured models to simpler first-order ones, making decoding faster and still achieving better accuracy than non-neural parsers.
Findings of the WMT 2019 Shared Tasks on Quality Estimation
We report the results of the WMT19 shared task on Quality Estimation, i. e. the task of predicting the quality of the output of machine translation systems given just the source text and the hypothesis translations.
Portuguese Word Embeddings: Evaluating on Word Analogies and Natural Language Tasks
Word embeddings have been found to provide meaningful representations for words in an efficient way; therefore, they have become common in Natural Language Processing sys- tems.
