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Subwords-Only Alternatives to fastText for Morphologically Rich Languages

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Abstract

In this work, we present purely subword-based alternatives to fastText word embedding algorithm The alternatives are modifications of the original fastText model, but rely on subword information only, eliminating the reliance on word-level vectors and at the same time helping to dramatically reduce the size of embeddings. Proposed models differ in their subword information extraction method: character n-grams, suffixes, and the byte-pair encoding units. We test the models in the task of morphological analysis and lemmatization for 3 morphologically rich languages: Finnish, Russian, and German. The results are compared with other recent subword-based models, demonstrating consistently higher results.

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Notes

  1. http://morpho.aalto.fi/projects/morpho/

  2. https://github.com/google/sentencepiece

  3. https://github.com/ispras-texterra/babylondigger

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ACKNOWLEDGMENTS

We thank Vladimir Mayorov and Sergey Korolyov for assisting with the development and experiments, and Denis Turdakov, Ivan Andrianov, Hrant Khachatryan, Vladislav Trifonov for feedback and insightful discussions.

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Authors and Affiliations

  1. Ivannikov Laboratory for System Programming at Russian-Armenian University, 0051, Yerevan, Armenia

    Tsolak Ghukasyan, Yeva Yeshilbashyan & Karen Avetisyan

Authors
  1. Tsolak Ghukasyan
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  2. Yeva Yeshilbashyan
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  3. Karen Avetisyan
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Corresponding authors

Correspondence to Tsolak Ghukasyan, Yeva Yeshilbashyan or Karen Avetisyan.

Appendices

APPENDIX A

COMPARISON OF VOCABULARIES FOR ENGLISH AND MORPHOLOGICALLY RICH LANGUAGES

Table 1. Form-Lemma ratios in UD v2.5 datasets [25] for Russian, German and Finnish compared to English
Full size table

APPENDIX B

ILLUSTRATION OF ORIGINAL FASTTEXT AND THE PROPOSED ALTERNATIVES

Fig. 1.
figure 1

Computation of the word embedding in fastText, no-fastText, so-fastText, and BPE-fastText models.

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APPENDIX C

ILLUSTRATION OF THE NEURAL NETWORK FOR MORPHOLOGICAL ANALYSIS AND LEMMATIZATION

Fig. 2.
figure 2

Architecture of the analyzer’s neural network.

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APPENDIX D

COMPARISON OF PROPOSED MODELS AND SIMILAR SUBWORDS-ONLY APPROACHES

Table 2. The accuracy of morphological analysis and lemmatization of proposed fastText alternatives vs morpheme-based approaches
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APPENDIX E

COMPARISON OF FASTTEXT AND PROPOSED MODELS

Table 3. The accuracy of morphological analysis and lemmatization of proposed alternatives vs fastText
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Table 4. Comparison of model sizes and parameter counts of proposed alternatives vs fastText
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APPENDIX F

NEURAL NETWORK-BASED ANALYZER’S HYPERPARAMETERS

Table 5. Tested BabylonDigger hyperparameters for the analyzer’s neural network (search was performed in greedy fashion). Bolded values were used in the final evaluation*
Full size table

APPENDIX G

THE PERFORMANCE OF BPE-BASED MODELS

Table 6. The performance of BPE-fastText embeddings based on subwords vocabulary size (vs.)
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Table 7. The performance of BPE-based models for the Russian language
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Table 8. The performance of BPE-based models for the Finnish language
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Table 9. The performance of BPE-based models for the German language.
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APPENDIX H

THE PERFORMANCE OF BPE-BASED MODELS BASED ON VOCABULARY SIZE AND LANGUAGE

Fig. 3.
figure 3

Models’ accuracy of predicting UPOS, FEATS, and LEMMA based on BPE vocabulary size.

Full size image

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Ghukasyan, T., Yeshilbashyan, Y. & Avetisyan, K. Subwords-Only Alternatives to fastText for Morphologically Rich Languages. Program Comput Soft 47, 56–66 (2021). https://doi.org/10.1134/S0361768821010059

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