%0 Conference Proceedings %T Neural Machine Translation without Embeddings %A Shaham, Uri %A Levy, Omer %Y Toutanova, Kristina %Y Rumshisky, Anna %Y Zettlemoyer, Luke %Y Hakkani-Tur, Dilek %Y Beltagy, Iz %Y Bethard, Steven %Y Cotterell, Ryan %Y Chakraborty, Tanmoy %Y Zhou, Yichao %S Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies %D 2021 %8 June %I Association for Computational Linguistics %C Online %F shaham-levy-2021-neural %X Many NLP models operate over sequences of subword tokens produced by hand-crafted tokenization rules and heuristic subword induction algorithms. A simple universal alternative is to represent every computerized text as a sequence of bytes via UTF-8, obviating the need for an embedding layer since there are fewer token types (256) than dimensions. Surprisingly, replacing the ubiquitous embedding layer with one-hot representations of each byte does not hurt performance; experiments on byte-to-byte machine translation from English to 10 different languages show a consistent improvement in BLEU, rivaling character-level and even standard subword-level models. A deeper investigation reveals that the combination of embeddingless models with decoder-input dropout amounts to token dropout, which benefits byte-to-byte models in particular. %R 10.18653/v1/2021.naacl-main.17 %U https://aclanthology.org/2021.naacl-main.17 %U https://doi.org/10.18653/v1/2021.naacl-main.17 %P 181-186