Papers with EN-FR
Machine Translationese: Effects of Algorithmic Bias on Linguistic Complexity in Machine Translation (2021.eacl-main)
Copied to clipboard
| Challenge: | Existing studies have shown that existing models amplify biases observed in training data. |
| Approach: | They propose to use MT and NLP to amplify biases observed in training data to investigate how bias amplification might affect language in a broader sense. |
| Outcome: | The proposed model amplifys biases observed in training data and could lead to an artificially impoverished language, the authors show. |
Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation (2021.acl-long)
Copied to clipboard
| Challenge: | Prior work treats all types of mismatches between source and target as noise . Consequently, it remains unclear how noisy parallel training samples impact NMT training. |
| Approach: | They propose a divergent-aware NMT framework that uses factors to help NMT recover from the degradation caused by naturally occurring divergences. |
| Outcome: | The proposed framework improves translation quality and model calibration on EN-FR tasks. |
The Learnability of the Annotated Input in NMT Replicating (Vanmassenhove and Way, 2018) with OpenNMT (2020.lrec-1)
Copied to clipboard
| Challenge: | reproducibility of experiments is a key issue in Neural Networks, which are fed with variable samples of training data. |
| Approach: | They reproduce some of the experiments related to neural network training for Machine Translation as reported in . they annotated a sample from the EN-FR and EN-DE Europarl with syntactic and semantic annotations to train neural networks with the Nematus Neural Machine Translation toolkit. |
| Outcome: | The results obtained were lower than the original paper, but on a more limited set of annotations. |