Morphological analysis using a sequence decoder
Table 2: Percentage of tags in the test data that have been observed fewer than 5 times in the training data for four languages and two training sizes (100 and 1000 sentences).
['[BOLD] Language', '[BOLD] 100sent', '[BOLD] 1000sent']
Morse uses a character based encoder that turns the relevant features of the word and its context into fixed size vector representations similar to Heigold et al. Our main contribution is the sequence decoder that generates the characters of the lemma and/or morphological features sequentially one at a time. This is similar to the way rule based systems such as finite state transducers output morphological analyses. One advantage of generating features one at a time (e.g. +Acc) rather than as a combined tag (e.g. +Noun+A3sg+Pnon+Acc) is sample efficiency. In low resource experiments, we show that our sequence decoder significantly outperforms a variant that is trained to output full tags similar to Heigold et al.