234 citations · 563 across the 19 of their papers we have counts for
6 papers · 1 filter
Updating Pre-trained Word Vectors and Text Classifiers using Monolingual Alignment
Piotr Bojanowski, Onur Celebi, Tomas Mikolov +2
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…
Misspelling Oblivious Word Embeddings
Bora Edizel, Aleksandra Piktus, Piotr Bojanowski +3
In this paper we present a method to learn word embeddings that are resilient to misspellings. Existing word embeddings have limited applicability to malformed texts, which contain…
Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion
Armand Joulin, Piotr Bojanowski, Tomas Mikolov +2
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 le…
Colorless green recurrent networks dream hierarchically
Kristina Gulordava, Piotr Bojanowski, Edouard Grave +2
Recurrent neural networks (RNNs) have achieved impressive results in a variety of linguistic processing tasks, suggesting that they can induce non-trivial properties of language. W…
Learning Word Vectors for 157 Languages
Edouard Grave, Piotr Bojanowski, Prakhar Gupta +2
Distributed word representations, or word vectors, have recently been applied to many tasks in natural language processing, leading to state-of-the-art performance. A key ingredien…
Advances in Pre-Training Distributed Word Representations
Tomas Mikolov, Edouard Grave, Piotr Bojanowski +2
Many Natural Language Processing applications nowadays rely on pre-trained word representations estimated from large text corpora such as news collections, Wikipedia and Web Crawl.…