Rustem Takhanov


2018

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Reusing Weights in Subword-Aware Neural Language Models
Zhenisbek Assylbekov | Rustem Takhanov
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

We propose several ways of reusing subword embeddings and other weights in subword-aware neural language models. The proposed techniques do not benefit a competitive character-aware model, but some of them improve the performance of syllable- and morpheme-aware models while showing significant reductions in model sizes. We discover a simple hands-on principle: in a multi-layer input embedding model, layers should be tied consecutively bottom-up if reused at output. Our best morpheme-aware model with properly reused weights beats the competitive word-level model by a large margin across multiple languages and has 20%-87% fewer parameters.

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Reproducing and Regularizing the SCRN Model
Olzhas Kabdolov | Zhenisbek Assylbekov | Rustem Takhanov
Proceedings of the 27th International Conference on Computational Linguistics

We reproduce the Structurally Constrained Recurrent Network (SCRN) model, and then regularize it using the existing widespread techniques, such as naive dropout, variational dropout, and weight tying. We show that when regularized and optimized appropriately the SCRN model can achieve performance comparable with the ubiquitous LSTM model in language modeling task on English data, while outperforming it on non-English data.

2017

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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.