Online Segment to Segment Neural Transduction
Table 3: Average accuracy over all the morphological inflection datasets. The baseline results for Seq2Seq variants are taken from [Faruqui et al.2016].
['[BOLD] Model', '[BOLD] Avg. accuracy']
The model with the best previous average result — denoted as adapted-seq2seq (FTND16) Our biSSNT+ model outperforms the vanilla encoder-decoder by a large margin and almost matches the state-of-the-art result on this task. As mentioned earlier, a characteristic of these datasets is that the stems and their corresponding inflected forms mostly overlap. Compare to the vanilla encoder-decoder, our model is better at copying and finding correspondences between prefix, stem and suffix segments.