Sammanfattning
In our submission to the SIGMORPHON 2022 Shared Task on Morpheme Segmentation, we study whether an unsupervised morphological segmentation method, Morfessor, can help in a supervised setting. Previous research has shown the effectiveness of the approach in semisupervised settings with small amounts of labeled data. The current tasks vary in data size: the amount of word-level annotated training data is much larger, but the amount of sentencelevel annotated training data remains small. Our approach is to pre-segment the input data for a neural sequence-to-sequence model with the unsupervised method. As the unsupervised method can be trained with raw text data, we use Wikipedia to increase the amount of training data. In addition, we train multilingual models for the sentence-level task. The results for the Morfessor-enriched features are mixed, showing benefit for all three sentencelevel tasks but only some of the word-level tasks. The multilingual training yields considerable improvements over the monolingual sentence-level models, but it negates the effect of the enriched features.
Originalspråk | engelska |
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Titel på värdpublikation | Proceedings of the 19th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology |
Redaktörer | Garrett Nicolai, Eleanor Chodroff |
Antal sidor | 8 |
Utgivningsort | Stroudsburg |
Förlag | The Association for Computational Linguistics |
Utgivningsdatum | juni 2022 |
Sidor | 144-151 |
ISBN (elektroniskt) | 978-1-955917-82-7 |
DOI | |
Status | Publicerad - juni 2022 |
MoE-publikationstyp | A4 Artikel i en konferenspublikation |
Evenemang | Workshop on Computational Research in Phonetics, Phonology, and Morphology - Seattle, Förenta Staterna (USA) Varaktighet: 14 juli 2022 → 14 juli 2022 Konferensnummer: 19 |
Vetenskapsgrenar
- 113 Data- och informationsvetenskap