LemmaTag: Jointly Tagging and Lemmatizing for Morphologically Rich Languages with BRNNs

Daniel Kondratyuk, Tomáš Gavenčiak, Milan Straka, Jan Hajič

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Abstract
We present LemmaTag, a featureless neural network architecture that jointly generates part-of-speech tags and lemmas for sentences by using bidirectional RNNs with character-level and word-level embeddings. We demonstrate that both tasks benefit from sharing the encoding part of the network, predicting tag subcategories, and using the tagger output as an input to the lemmatizer. We evaluate our model across several languages with complex morphology, which surpasses state-of-the-art accuracy in both part-of-speech tagging and lemmatization in Czech, German, and Arabic.
Anthology ID:
D18-1532
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Editors:
Ellen Riloff, David Chiang, Julia Hockenmaier, Jun’ichi Tsujii
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4921–4928
Language:
URL:
https://aclanthology.org/D18-1532
DOI:
10.18653/v1/D18-1532
Bibkey:
Cite (ACL):
Daniel Kondratyuk, Tomáš Gavenčiak, Milan Straka, and Jan Hajič. 2018. LemmaTag: Jointly Tagging and Lemmatizing for Morphologically Rich Languages with BRNNs. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4921–4928, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
LemmaTag: Jointly Tagging and Lemmatizing for Morphologically Rich Languages with BRNNs (Kondratyuk et al., EMNLP 2018)
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PDF:
https://preview.aclanthology.org/teach-a-man-to-fish/D18-1532.pdf
Attachment:
 D18-1532.Attachment.zip
Code
 hyperparticle/LemmaTag
Data
Universal Dependencies
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