Piotr Bojanowski
7 papers in the PaperMetrix corpus
Papers by this author
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Misspelling Oblivious Word Embeddings
2019
Aleksandra Piktus, Necati Bora Edizel, Piotr Bojanowski, Edouard Grave, Rui Ferreira, Fabrizio Silvestri. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long …
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Updating Pre-trained Word Vectors and Text Classifiers using Monolingual Alignment
2019 · arXiv (Cornell University)
In this paper, we focus on the problem of adapting word vector-based models to new textual data. Given a model pre-trained on large reference data, how can we adapt it to a smaller piece of …
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Bag of Tricks for Efficient Text Classification
2017
Armand Joulin, Edouard Grave, Piotr Bojanowski, Tomas Mikolov. Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers. 2017.
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Enriching Word Vectors with Subword Information
2017 · Transactions of the Association for Computational Linguistics
Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the morphology of words, by assigning a distinct vector to each word. …
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Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion
2018
Continuous word representations learned separately on distinct languages can be aligned so that their words become comparable in a common space. Existing works typically solve a quadratic problem to learn a orthogonal matrix aligning a …
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Adaptive Attention Span in Transformers
2019
We propose a novel self-attention mechanism that can learn its optimal attention span. This allows us to extend significantly the maximum context size used in Transformer, while maintaining control over their memory footprint and computational …
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Enriching Word Vectors with Subword Information
2016 · arXiv (Cornell University)
Continuous word representations, trained on large unlabeled corpora are useful for many natural language processing tasks. Popular models that learn such representations ignore the morphology of words, by assigning a distinct vector to each word. …