  In this paper, we try to understand neural machine translation (NMT) via
simplifying NMT architectures and training encoder-free NMT models. In an
encoder-free model, the sums of word embeddings and positional embeddings
represent the source. The decoder is a standard Transformer or recurrent neural
network that directly attends to embeddings via attention mechanisms.
Experimental results show (1) that the attention mechanism in encoder-free
models acts as a strong feature extractor, (2) that the word embeddings in
encoder-free models are competitive to those in conventional models, (3) that
non-contextualized source representations lead to a big performance drop, and
(4) that encoder-free models have different effects on alignment quality for
German-English and Chinese-English.
