Robust Neural Machine Translation

This thesis aims for general robust Neural Machine Translation (NMT) that is agnostic to the test domain. NMT has achieved high quality on benchmarks with closed datasets such as WMT and NIST but can fail when the translation input contains noise due to, for example, mismatched domains or spelling errors. The standard solution is to apply domain adaptation or data augmentation to build a domain-dependent system. However, in real life, the input noise varies in a wide range of domains and types, which is unknown in the training phase. This thesis introduces five general approaches to improve NMT accuracy and robustness, where three of them are invariant to models, test domains, and noise types. First, we describe a novel unsupervised text normalization framework Lex-Var, to reduce the lexical variations for NMT. Then, we apply the phonetic encoding as auxiliary linguistic information and obtained very significant (5 BLEU point) improvement in translation quality and robustness. Furthermore, we introduce the random clustering encoding method based on our hypothesis of Semantic Diversity by Phonetics and generalizes to all languages. We also discussed two domain adaptation models for the known test domain. Finally, we provide a measurement of translation robustness based on the consistency of translation accuracy among samples and use it to evaluate our other methods. All these approaches are verified with extensive experiments across different languages and achieved significant and consistent improvements in translation quality and robustness over the state-of-the-art NMT

Bagging by Design (on the Suboptimality of Bagging)

Bagging (Breiman 1996) and its variants is one of the most popular methods in aggregating classifiers and regressors. Originally, its analysis assumed that the bootstraps are built from an unlimited, independent source of samples, therefore we call this form of bagging ideal-bagging. However in the real world, base predictors are trained on data subsampled from a limited number of training samples and thus they behave very differently. We analyze the effect of intersections between bootstraps, obtained by subsampling, to train different base predictors. Most importantly, we provide an alternative subsampling method called design-bagging based on a new construction of combinatorial designs, and prove it universally better than bagging. Methodologically, we succeed at this level of generality because we compare the prediction accuracy of bagging and design-bagging relative to the accuracy ideal-bagging. This finds potential applications in more involved bagging-based methods. Our analytical results are backed up by experiments on classification and regression settings