Bagdat Myrzakhmetov


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Manual vs Automatic Bitext Extraction
Aibek Makazhanov | Bagdat Myrzakhmetov | Zhenisbek Assylbekov
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)


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Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones
Zhenisbek Assylbekov | Rustem Takhanov | Bagdat Myrzakhmetov | Jonathan N. Washington
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Syllabification does not seem to improve word-level RNN language modeling quality when compared to character-based segmentation. However, our best syllable-aware language model, achieving performance comparable to the competitive character-aware model, has 18%-33% fewer parameters and is trained 1.2-2.2 times faster.