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Full-Text Articles in Social and Behavioral Sciences
Mitigating Gender Bias In Neural Machine Translation Using Counterfactual Data, Alan Wong
Mitigating Gender Bias In Neural Machine Translation Using Counterfactual Data, Alan Wong
Dissertations, Theses, and Capstone Projects
Recent advances in deep learning have greatly improved the ability of researchers to develop effective machine translation systems. In particular, the application of modern neural architectures, such as the Transformer, has achieved state-of-the-art BLEU scores in many translation tasks. However, it has been found that even state-of-the-art neural machine translation models can suffer from certain implicit biases, such as gender bias (Lu et al., 2019). In response to this issue, researchers have proposed various potential solutions: some have proposed approaches that inject missing gender information into models, while others have attempted modifying the training data itself. We focus on mitigating …
Does The Word "Chien" Bark? Representation Learning In Neural Machine Translation Encoders, Emily Campbell
Does The Word "Chien" Bark? Representation Learning In Neural Machine Translation Encoders, Emily Campbell
Dissertations, Theses, and Capstone Projects
This thesis presents experiments with using representation learning to explore how neural networks learn. Neural networks which take text as input create internal representations of the text during their training. Recent work has found that these representations can be used to perform other downstream linguistic tasks, such as part-of-speech (POS) tagging. This demonstrates that the neural networks are learning linguistic information and storing this information in the representations. We focus on the representations created by neural machine translation (NMT) models and whether they can be used in POS tagging. We train 5 NMT models including an auto-encoder. We extract the …