Morphological Analysis Using a Sequence Decoder

Ekin Akyürek, Erenay Dayanık, Deniz Yuret


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 https://github.com/ai-ku/Morse.jl for a Morse implementation in Julia/Knet (Yuret, 2016) and https://github.com/ai-ku/TrMor2018 for the new Turkish data set.
Anthology ID:
Q19-1036
Volume:
Transactions of the Association for Computational Linguistics, Volume 7
Month:
Year:
2019
Address:
Cambridge, MA
Editors:
Lillian Lee, Mark Johnson, Brian Roark, Ani Nenkova
Venue:
TACL
SIG:
Publisher:
MIT Press
Note:
Pages:
567–579
Language:
URL:
https://aclanthology.org/Q19-1036
DOI:
10.1162/tacl_a_00286
Bibkey:
Cite (ACL):
Ekin Akyürek, Erenay Dayanık, and Deniz Yuret. 2019. Morphological Analysis Using a Sequence Decoder. Transactions of the Association for Computational Linguistics, 7:567–579.
Cite (Informal):
Morphological Analysis Using a Sequence Decoder (Akyürek et al., TACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/Q19-1036.pdf
Code
 ai-ku/TrMor2018 +  additional community code
Data
TrMor2018Universal Dependencies