Geodesic
Robust word vectors: context-informed embeddings for noisy texts
Zapiski Nauchnykh Seminarov POMI, Investigations on applied mathematics and informatics. Part I, Tome 499 (2021), pp. 248-266

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We suggest a new language-independent architecture of robust word vectors (RoVe). It is designed to alleviate the issue of typos and misspellings, common in almost any user-generated content, which hinder automatic text processing. Our model is morphologically motivated, which allows it to deal with unseen word forms in morphologically rich languages. We present the results on a number of natural language processing (NLP) tasks and languages for a variety of related architectures and show that the proposed architecture is robust to typos. 
@article{ZNSL_2021_499_a13,
     author = {T. Khakhulin and V. Logacheva and V. Malykh},
     title = {Robust word vectors: context-informed embeddings for noisy texts},
     journal = {Zapiski Nauchnykh Seminarov POMI},
     pages = {248--266},
     publisher = {mathdoc},
     volume = {499},
     year = {2021},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/ZNSL_2021_499_a13/}
}
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T. Khakhulin; V. Logacheva; V. Malykh. Robust word vectors: context-informed embeddings for noisy texts. Zapiski Nauchnykh Seminarov POMI, Investigations on applied mathematics and informatics. Part I, Tome 499 (2021), pp. 248-266. http://geodesic.mathdoc.fr/item/ZNSL_2021_499_a13/