Morfessor EM plus Prune: Improved Subword Segmentation with Expectation Maximization and Pruning

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Date
2020
Major/Subject
Mcode
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Language
en
Pages
10
3944-3953
Series
Proceedings of The 12th Language Resources and Evaluation Conference
Abstract
Data-driven segmentation of words into subword units has been used in various natural language processing applications such as automatic speech recognition and statistical machine translation for almost 20 years. Recently it has became more widely adopted, as models based on deep neural networks often benefit from subword units even for morphologically simpler languages. In this paper, we discuss and compare training algorithms for a unigram subword model, based on the Expectation Maximization algorithm and lexicon pruning. Using English, Finnish, North Sami, and Turkish data sets, we show that this approach is able to find better solutions to the optimization problem defined by the Morfessor Baseline model than its original recursive training algorithm. The improved optimization also leads to higher morphological segmentation accuracy when compared to a linguistic gold standard. We publish implementations of the new algorithms in the widely-used Morfessor software package.
Description
| openaire: EC/H2020/780069/EU//MeMAD
Keywords
Morphology, Statistical and Machine Learning Methods, Language Modelling, Unsupervised learning, Tools, Less-Resourced/Endangered Languages
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Gronroos , S-A , Virpioja , S & Kurimo , M 2020 , Morfessor EM plus Prune: Improved Subword Segmentation with Expectation Maximization and Pruning . in N Calzolari , F Bechet , P Blache , K Choukri , C Cieri , T Declerck , S Goggi , H Isahara , B Maegaard , J Mariani , H Mazo , A Moreno , J Odijk & S Piperidis (eds) , Proceedings of The 12th Language Resources and Evaluation Conference . European Language Resources Association (ELRA) , pp. 3944-3953 , International Conference on Language Resources and Evaluation , Marseille , France , 11/05/2020 . < https://aclanthology.org/2020.lrec-1.486.pdf >