A popular natural language processing task decades ago, word alignment has been dominated until recently by GIZA++, a statistical method based on the 30-year-old IBM models. Though recent years have finally seen Giza++ performance bested, the new met
hods primarily rely on large machine translation models, massively multilingual language models, or supervision from Giza++ alignments itself. We introduce Embedding-Enhanced Giza++, and outperform Giza++ without any of the aforementioned factors. Taking advantage of monolingual embedding space geometry of the source and target language only, we exceed Giza++s performance in every tested scenario for three languages. In the lowest-resource scenario of only 500 lines of bitext, we improve performance over Giza++ by 10.9 AER. Our method scales monotonically outperforming Giza++ for all tested scenarios between 500 and 1.9 million lines of bitext. Our code will be made publicly available.
Despite the reported success of unsupervised machine translation (MT), the field has yet to examine the conditions under which these methods succeed, and where they fail. We conduct an extensive empirical evaluation of unsupervised MT using dissimila
r language pairs, dissimilar domains, diverse datasets, and authentic low-resource languages. We find that performance rapidly deteriorates when source and target corpora are from different domains, and that random word embedding initialization can dramatically affect downstream translation performance. We additionally find that unsupervised MT performance declines when source and target languages use different scripts, and observe very poor performance on authentic low-resource language pairs. We advocate for extensive empirical evaluation of unsupervised MT systems to highlight failure points and encourage continued research on the most promising paradigms.
