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The goal of this work is to design a machine translation (MT) system for alow-resource family of dialects, collectively known as Swiss German, which arewidely spoken in Switzerland but seldom written. We collected a significantnumber of parallel written resources to start with, up to a total of about 60kwords. Moreover, we identified several other promising data sources for SwissGerman. Then, we designed and compared three strategies for normalizing SwissGerman input in order to address the regional diversity. We found thatcharacter-based neural MT was the best solution for text normalization. Incombination with phrase-based statistical MT, our solution reached 36% BLEUscore when translating from the Bernese dialect. This value, however, decreasesas the testing data becomes more remote from the training one, geographicallyand topically. These resources and normalization techniques are a first steptowards full MT of Swiss German dialects.

Machine Translation of Low-Resource Spoken Dialects: Strategies for Normalizing Swiss German