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://preview.aclanthology.org/icon-24-ingestion/Q19-1036/
- DOI:
- 10.1162/tacl_a_00286
- 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)
- PDF:
- https://preview.aclanthology.org/icon-24-ingestion/Q19-1036.pdf
- Code
- ai-ku/TrMor2018 + additional community code
- Data
- TrMor2018, Universal Dependencies