Skip Nav Destination
Close Modal
Update search
NARROW
Format
Journal
Date
Availability
1-1 of 1
Erenay Dayanık
Close
Follow your search
Access your saved searches in your account
Would you like to receive an alert when new items match your search?
Sort by
Journal Articles
Publisher: Journals Gateway
Transactions of the Association for Computational Linguistics (2019) 7: 567–579.
Published: 01 September 2019
FIGURES
Abstract
View article
PDF
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. 1 See 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.