  Subword segmenters like BPE operate as a preprocessing step in neural machine
translation and other (conditional) language models. They are applied to
datasets before training, so translation or text generation quality relies on
the quality of segmentations. We propose a departure from this paradigm, called
subword segmental machine translation (SSMT). SSMT unifies subword segmentation
and MT in a single trainable model. It learns to segment target sentence words
while jointly learning to generate target sentences. To use SSMT during
inference we propose dynamic decoding, a text generation algorithm that adapts
segmentations as it generates translations. Experiments across 6 translation
directions show that SSMT improves chrF scores for morphologically rich
agglutinative languages. Gains are strongest in the very low-resource scenario.
SSMT also learns subwords that are closer to morphemes compared to baselines
and proves more robust on a test set constructed for evaluating morphological
compositional generalisation.
