NOVOTNÝ, Vít, Michal ŠTEFÁNIK, Eniafe Festus AYETIRAN, Petr SOJKA and Radim ŘEHŮŘEK. When FastText Pays Attention: Efficient Estimation of Word Representations using Constrained Positional Weighting. Journal of Universal Computer Science. New York, USA: J.UCS Consortium, 2022, vol. 28, No 2, p. 181-201. ISSN 0948-695X. Available from: https://dx.doi.org/10.3897/jucs.69619. |
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@article{1762317, author = {Novotný, Vít and Štefánik, Michal and Ayetiran, Eniafe Festus and Sojka, Petr and Řehůřek, Radim}, article_location = {New York, USA}, article_number = {2}, doi = {http://dx.doi.org/10.3897/jucs.69619}, keywords = {Word embeddings; fastText; attention}, language = {eng}, issn = {0948-695X}, journal = {Journal of Universal Computer Science}, title = {When FastText Pays Attention: Efficient Estimation of Word Representations using Constrained Positional Weighting}, url = {https://arxiv.org/abs/2104.09691}, volume = {28}, year = {2022} }
TY - JOUR ID - 1762317 AU - Novotný, Vít - Štefánik, Michal - Ayetiran, Eniafe Festus - Sojka, Petr - Řehůřek, Radim PY - 2022 TI - When FastText Pays Attention: Efficient Estimation of Word Representations using Constrained Positional Weighting JF - Journal of Universal Computer Science VL - 28 IS - 2 SP - 181-201 EP - 181-201 PB - J.UCS Consortium SN - 0948695X KW - Word embeddings KW - fastText KW - attention UR - https://arxiv.org/abs/2104.09691 N2 - In 2018, Mikolov et al. introduced the positional language model, which has characteristics of attention-based neural machine translation models and which achieved state-of-the-art performance on the intrinsic word analogy task. However, the positional model is not practically fast and it has never been evaluated on qualitative criteria or extrinsic tasks. We propose a constrained positional model, which adapts the sparse attention mechanism from neural machine translation to improve the speed of the positional model. We evaluate the positional and constrained positional models on three novel qualitative criteria and on language modeling. We show that the positional and constrained positional models contain interpretable information about the grammatical properties of words and outperform other shallow models on language modeling. We also show that our constrained model outperforms the positional model on language modeling and trains twice as fast. ER -
NOVOTNÝ, Vít, Michal ŠTEFÁNIK, Eniafe Festus AYETIRAN, Petr SOJKA and Radim ŘEHŮŘEK. When FastText Pays Attention: Efficient Estimation of Word Representations using Constrained Positional Weighting. \textit{Journal of Universal Computer Science}. New York, USA: J.UCS Consortium, 2022, vol.~28, No~2, p.~181-201. ISSN~0948-695X. Available from: https://dx.doi.org/10.3897/jucs.69619.
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