Multi-Team: A Multi-attention, Multi-decoder Approach to Morphological Analysis.
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10427084" target="_blank" >RIV/00216208:11320/19:10427084 - isvavai.cz</a>
Result on the web
<a href="https://www.aclweb.org/anthology/W19-4206" target="_blank" >https://www.aclweb.org/anthology/W19-4206</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Multi-Team: A Multi-attention, Multi-decoder Approach to Morphological Analysis.
Original language description
This paper describes our submission to SIGMORPHON 2019 Task 2: Morphological analysis and lemmatization in context. Our model is a multi-task sequence to sequence neural network, which jointly learns morphological tagging and lemmatization. On the encoding side, we exploit character-level as well as contextual information. We introduce a multi-attention decoder to selectively focus on different parts of character and word sequences. To further improve the model, we train on multiple datasets simultaneously and use external embeddings for initialization. Our final model reaches an average morphological tagging F1 score of 94.54 and a lemma accuracy of 93.91 on the test data, ranking respectively 3rd and 6th out of 13 teams in the SIGMORPHON 2019 shared task.
Czech name
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Czech description
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Classification
Type
O - Miscellaneous
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů