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This paper describes the Notre Dame Natural Language Processing Group's(NDNLP) submission to the WNGT 2019 shared task (Hayashi et al., 2019). Weinvestigated the impact of auto-sizing (Murray and Chiang, 2015; Murray et al.,2019) to the Transformer network (Vaswani et al., 2017) with the goal ofsubstantially reducing the number of parameters in the model. Our method wasable to eliminate more than 25% of the model's parameters while suffering adecrease of only 1.1 BLEU.

Efficiency through Auto-Sizing: Notre Dame NLP's Submission to the WNGT 2019 Efficiency Task