Multi-Team: A Multi-attention, Multi-decoder Approach to Morphological Analysis

Ahmet Üstün, Rob van der Goot, Gosse Bouma, Gertjan van Noord
2019 Proceedings of the 16th Workshop on Computational Research in Phonetics, Phonology, and Morphology   unpublished
This paper describes our submission to SIG-MORPHON 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
more » ... multaneously 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 SIG-MORPHON 2019 shared task.
doi:10.18653/v1/w19-4206 fatcat:g37ndtu5x5hfjdr3eofzjc5awy