MultiVec: a Multilingual and Multilevel Representation Learning Toolkit for NLP

By Alexandre Berard, Christophe Servan, Olivier Pietquin, Laurent Besacier. 2016

56 citations recorded by the source corpus.

We present MultiVec, a new toolkit for computing continuous representations for text at different granularity levels (word-level or sequences of words). MultiVec includes word2vec’s features, paragraph vector (batch and online) and bivec for bilingual distributed representations. MultiVec also includes different distance measures between words and sequences of words. The toolkit is written in C++ and is aimed at being fast (in the same order of magnitude as word2vec), easy to use, and easy to extend. It has been evaluated on several NLP tasks: the analogical reasoning task, sentiment analysis, and crosslingual document classification.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Findings of the 2016 Conference on Machine Translation (2016, methodology): “To produce these features, GIZA++ (Och and Ney, 2003) was used for word alignment and Multivec (Berard et al., 2016) was used for the bilingual model, which jointly learns distributed representations for source and target languages using a parallel corpus.”
Bilingual Embeddings and Word Alignments for Translation Quality Estimation (2016, methodology): “For this purpose, we use the implementation provided by the Multivec tool (Bérard et al., 2016) for the bilingual distributed representation model, described by Luong et al. (2015) and the GIZA++ word alignment model (Och and Ney, 2003).”
CompiLIG at SemEval-2017 Task 1: Cross-Language Plagiarism Detection Methods for Semantic Textual Similarity (2017, methodology): “• Bag-of-words of a word = all its possible translations or nyms, jointly given by ontology DBNary [1] and by word embeddings with the MultiVec toolkit [2] ; • Bag-of-words of a sentence = merge of the bag-of-words of its words ; • Syntactically [3] and frequentially (idf ) weighted augmentation of ”
H2@BUCC18: Parallel Sentence Extraction from Comparable Corpora Using Multilingual Sentence Embeddings (2018, methodology): “For this, we use multivec (Bérard et al., 2016), an implementation of (Luong et al., 2015)’s bivec model for bilingual distributed representations.”