Publications related to Sentence embedding

Better Word Embeddings by Disentangling Contextual n-Gram Information

Martin Jaggi, Matteo Pagliardini, Prakhar Gupta

Pre-trained word vectors are ubiquitous in Natural Language Processing applications. In this paper, we show how training word embeddings jointly with bigram and even trigram embeddings, results in improved unigram embeddings. We claim that training word em ...

2019

Crosslingual Document Embedding as Reduced-Rank Ridge Regression

Martin Jaggi, Robert West, Martin Josifoski, Ivan Paskov

There has recently been much interest in extending vector-based word representations to multiple languages, such that words can be compared across languages. In this paper, we shift the focus from words to documents and introduce a method for embedding doc ...

2019

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

Robust and Discriminative Speaker Embedding via Intra-Class Distance Variance Regularization

Jean-Marc Odobez

Learning a good speaker embedding is critical for many speech processing tasks, including recognition, verification, and diarization. To this end, we propose a complementary optimizing goal called intra-class loss to improve deep speaker embed dings learne ...

ISCA-INT SPEECH COMMUNICATION ASSOC2018

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 ...

2017

Building Word Embeddings for Solving Natural Language Processing

Rémi Philippe Lebret

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 through Hellinger PCA

Rémi Philippe Lebret, Ronan Collobert

Word embeddings resulting from neural language models have been shown to be a great asset for a large variety of NLP tasks. However, such architecture might be difficult and time-consuming to train. Instead, we propose to drastically simplify the word embe ...

2014

Word Embeddings through Hellinger PCA

Rémi Philippe Lebret, Ronan Collobert

Word embeddings resulting from neural lan- guage models have been shown to be successful for a large variety of NLP tasks. However, such architecture might be difficult to train and time-consuming. Instead, we propose to drastically simplify the word embed ...

Idiap2013

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Sentence embedding

Better Word Embeddings by Disentangling Contextual n-Gram Information

Crosslingual Document Embedding as Reduced-Rank Ridge Regression

Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

Robust and Discriminative Speaker Embedding via Intra-Class Distance Variance Regularization

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

Word Embeddings for Natural Language Processing

Building Word Embeddings for Solving Natural Language Processing

Word Sequence Modeling using Deep Learning

Word Embeddings through Hellinger PCA

Word Embeddings through Hellinger PCA

Graph Chatbot

Chat with Graph Search

Word Sequence Modeling using Deep Learning

Word Embeddings through Hellinger PCA

Word Embeddings through Hellinger PCA

Robust and Discriminative Speaker Embedding via Intra-Class Distance Variance Regularization

Crosslingual Document Embedding as Reduced-Rank Ridge Regression

Simple Unsupervised Keyphrase Extraction using Sentence Embeddings

Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

Better Word Embeddings by Disentangling Contextual n-Gram Information

Word Embeddings for Natural Language Processing

Building Word Embeddings for Solving Natural Language Processing