Online Segment to Segment Neural Transduction
Table 4: Comparison of the performance of our model (biSSNT+) against the previous state-of-the-art on each morphological inflection dataset.
['Dataset', 'DDN13', 'NCK15', 'FTND16', 'biSSNT+']
DDN13 and NCK15 denote the models of \newcitedurrett2013supervised and \newcitenicolai2015inflection, respectively. Both models tackle the task by feature engineering. FTND16 It can be considered as a special case of our model by forcing a fixed diagonal alignment between input and output sequences. Our model achieves comparable results to these models on all the datasets. Notably it outperforms other models on the Finnish noun and adjective, and verbs datasets, whose stems and inflected forms are the longest.