  We examine the role of character patterns in three tasks: morphological
analysis, lemmatization and copy. We use a modified version of the standard
sequence-to-sequence model, where the encoder is a pattern matching network.
Each pattern scores all possible N character long subwords (substrings) on the
source side, and the highest scoring subword's score is used to initialize the
decoder as well as the input to the attention mechanism. This method allows
learning which subwords of the input are important for generating the output.
By training the models on the same source but different target, we can compare
what subwords are important for different tasks and how they relate to each
other. We define a similarity metric, a generalized form of the Jaccard
similarity, and assign a similarity score to each pair of the three tasks that
work on the same source but may differ in target. We examine how these three
tasks are related to each other in 12 languages. Our code is publicly
available.
