@article{akyurek-etal-2019-morphological,
    title = "Morphological Analysis Using a Sequence Decoder",
    author = {Aky{\"u}rek, Ekin  and
      Dayan{\i}k, Erenay  and
      Yuret, Deniz},
    editor = "Lee, Lillian  and
      Johnson, Mark  and
      Roark, Brian  and
      Nenkova, Ani",
    journal = "Transactions of the Association for Computational Linguistics",
    volume = "7",
    year = "2019",
    address = "Cambridge, MA",
    publisher = "MIT Press",
    url = "https://preview.aclanthology.org/fix-sig-urls/Q19-1036/",
    doi = "10.1162/tacl_a_00286",
    pages = "567--579",
    abstract = "We introduce Morse, a recurrent encoder-decoder model that produces morphological analyses of each word in a sentence. The encoder turns the relevant information about the word and its context into a fixed size vector representation and the decoder generates the sequence of characters for the lemma followed by a sequence of individual morphological features. We show that generating morphological features individually rather than as a combined tag allows the model to handle rare or unseen tags and to outperform whole-tag models. In addition, generating morphological features as a sequence rather than, for example, an unordered set allows our model to produce an arbitrary number of features that represent multiple inflectional groups in morphologically complex languages. We obtain state-of-the-art results in nine languages of different morphological complexity under low-resource, high-resource, and transfer learning settings. We also introduce TrMor2018, a new high-accuracy Turkish morphology data set. Our Morse implementation and the TrMor2018 data set are available online to support future research.1See \url{https://github.com/ai-ku/Morse.jl} for a Morse implementation in Julia/Knet (Yuret, 2016) and \url{https://github.com/ai-ku/TrMor2018} for the new Turkish data set."
}