Morphological Inflection Generationwith Hard Monotonic Attention
Table 2: Results on the Wiktionary datasets
['[EMPTY]', 'DE-N', 'DE-V', 'ES-V', 'FI-NA', 'FI-V', 'FR-V', 'NL-V', 'Avg.']
This shows the robustness of our model also with large amounts of training examples, and the advantage the hard attention mechanism provides over the encoder-decoder approach of \newcitefaruquiTND15 which does not employ an attention mechanism. Our model is also significantly more accurate than the model of \newciteyu2016online, which shows the advantage of using independently learned alignments to guide the network’s attention from the beginning of the training process. While our soft-attention implementation outperformed the models of \newciteyu2016online and \newcitedurrettdenero2013, it still performed inferiorly to the hard attention model.