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Learning Word Vectors for 157 Languages

Publications associées (46)

CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model

Continuous Bag of Words (CBOW) is a powerful text embedding method. Due to its strong capabilities to encode word content, CBOW embeddings perform well on a wide range of downstream tasks while being efficient to compute. However, CBOW is not capable of ca ...

2019

CBOW Is Not All You Need: Combining CBOW with the Compositional Matrix Space Model

Jan Frederik Jonas Florian Mai

Idiap2019

Transformer-Based Multi-lingual Sentence Embeddings

In this thesis, we present a transformers-based multi-lingual embedding model to represent sentences in different languages in a common space. To do so, our system uses the structure of a simplified transformer with a shared byte-pair encoding vocabulary f ...

2019

Unsupervised Learning of Representations for Lexical Entailment Detection

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Detecting lexical entailment plays a fundamental role in a variety of natural language processing tasks and is key to language understanding. Unsupervised methods still play an important role due to the lack of coverage of lexical databases in some domains ...

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Text Similarity in Vector Space Models: A Comparative Study

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Automatic measurement of semantic text similarity is an important task in natural language processing. In this paper, we evaluate the performance of different vector space models to perform this task. We address the real-world problem of modeling patent-to ...

2018

Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

Martin Jaggi, Claudiu-Cristian Musat, Kamil Bennani-Smires

Keyphrase extraction is the task of automatically selecting a small set of phrases that best describe a given free text document. Keyphrases can be used for indexing, searching, aggregating and summarizing text documents, serving many automatic as well as ...

2018

Multilingual bottleneck features for subword modeling in zero-resource languages

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How can we effectively develop speech technology for languages where no transcribed data is available? Many existing approaches use no annotated resources at all, yet it makes sense to leverage information from large annotated corpora in other languages, f ...

2018

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

Martin Jaggi, Matteo Pagliardini, Prakhar Gupta

The recent tremendous success of unsupervised word embeddings in a multitude of applications raises the obvious question if similar methods could be derived to improve embeddings (i.e. semantic representations) of word sequences as well. We present a simpl ...

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Building Word Embeddings for Solving Natural Language Processing

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Word embedding is a feature learning technique which aims at mapping words from a vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large corpora of unlabeled text, such continuous space representations can be computed for c ...

École Polytechnique Fédérale de Lausanne2016

Word Embeddings for Natural Language Processing

Rémi Philippe Lebret

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