oaarnikoivu/robust-nmt

Code for my MSc Dissertation titled: "Robustness of Machine Translation for Low-Resource Languages." # Robustness of Machine Translation for Low-Resource Languages Report ## Abstract It is becoming increasingly common for researchers and practitioners to rely on methods within the field of Neural Machine Translation (NMT) that require the use of an extensive amount of auxiliary data. This is especially true for low-resource NMT where the availability of large-scale corpora is limited. As a result, the field of low-resource NMT without the use of supplementary data has received less attention. This work challenges the idea that modern NMT systems are poorly equipped for low-resource NMT by examining a variety of different systems and techniques in simulated Finnish-English low-resource conditions. This project shows that under certain low-resource conditions, the performance of the Transformer can be considerably improved via simple model compression and regularization techniques. In medium-resource settings, it is shown that an optimized Transformer is competitive with language model fine-tuning, in both in-domain and out-of-domain conditions. As an attempt to further improve robustness towards samples distant from the training distribution, this work explores subword regularization using BPE-Dropout, and defensive distillation. It is found that an optimized Transformer is superior in comparison to subword regularization, whereas defensive distillation improves domain robustness on domains that are the most distant from the original training distribution. A small manual evaluation is implemented where the goal is to assess the robustness of each system and technique towards adequacy and fluency. The results show that under some low-resource conditions, translations generated by most systems are in fact grammatical, however, highly inadequate. ## Install required librarires ./scripts/install_libraries.sh ## Download data ./scripts/download_data.sh data ## Transformer preprocessing ### Truecaser learned on full in-domain Europarl corpus ./scripts/transforme …