Papers by Zhenisbek Assylbekov
Speeding Up Entmax (2022.findings-naacl)
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| Challenge: | Recent studies suggest that sparsity is a problem when the trained model is used for inference. |
| Approach: | They propose an alternative to softmax that produces a dense probability distribution but is slower than softmax. |
| Outcome: | The proposed method keeps its virtuous characteristics but is slower than softmax and achieves on par or better performance in machine translation task. |
Reusing Weights in Subword-Aware Neural Language Models (N18-1)
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| Challenge: | a statistical language model assigns a probability to a sequence of words . data sparsity is a major problem in building traditional n-gram language models . |
| Approach: | They propose several ways to reuse subword embeddings and other weights in subword-aware neural language models. |
| Outcome: | The proposed techniques do not benefit a competitive character-aware model . but they show significant reductions in model sizes and performance. |
Reproducing and Regularizing the SCRN Model (C18-1)
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| Challenge: | Recurrent neural networks (RNNs) have demonstrated tremendous success in sequence modeling . naive dropout, variational dropout and weight tying are common techniques used to regularize the SCRN model . |
| Approach: | They propose a Structurally Constrained Recurrent Network (SCRN) model and regularize it using existing techniques. |
| Outcome: | The proposed model outperforms the LSTM model on non-English data while being much simpler. |
Manual vs Automatic Bitext Extraction (L18-1)
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| Challenge: | targeted, site-specific crawling results in cleaner bitexts with a higher ratio of parallel sentences . general crawlers combined with boilerplate removal tools tend to retrieve shorter texts . |
| Approach: | They compare manual and automatic approaches to extracting bitexts from the Web . they use targeted site-specific crawling to extract cleaner bitext sentences . |
| Outcome: | The proposed methods extract more parallel sentences from the Web than manual methods. |