Word2vec | EPFL Graph Search

En intelligence artificielle et en apprentissage machine, Word2vec est un groupe de modèles utilisé pour le plongement lexical (word embedding). Ces modèles ont été développés par une équipe de recherche chez Google sous la direction de . Ce sont des réseaux de neurones artificiels à deux couches entraînés pour reconstruire le contexte linguistique des mots. La méthode est implémentée dans la bibliothèque Python Gensim. Architectures Deux architectures ont été initialement proposées pour apprendre les Word2vec, le modèle de sacs de mots continus (CBOW: continuous bag of words) et le modèle skip-gram. Le CBOW vise à prédire un mot étant donné son contexte, c'est-à-dire étant donné les mots qui en sont proches dans le texte. Un tel contexte est par exemple les 5 mots à droite et les 5 mots à gauche du mot à prédire. Le skip-gram a une architecture symétrique visant à prédire les mots du contexte étant donné un mot en entrée. vignette|alt=les modèles CBOW et Skip-gram de

Further results on latent discourse models and word embeddings

Youssef Allouah

We discuss some properties of generative models for word embeddings. Namely, (Arora et al., 2016) proposed a latent discourse model implying the concentration of the partition function of the word vectors. This concentration phenomenon led to an asymptotic linear relation between the pointwise mutual information (PMI) of pairs of words and the scalar product of their vectors. Here, we first revisit this concentration phenomenon and prove it under slightly weaker assumptions, for a set of random vectors symmetrically distributed around the origin. Second, we empirically evaluate the relation between PMI and scalar products of word vectors satisfying the concentration property. Our empirical results indicate that, in practice, this relation does not hold with arbitrarily small error. This observation is further supported by two theoretical results: (i) the error cannot be exactly zero because the corresponding shifted PMI matrix cannot be positive semidefinite; (ii) under mild assumptions, there exist pairs of words for which the error cannot be close to zero. We deduce that either natural language does not follow the assumptions of the considered generative model, or the current word vector generation methods do not allow the construction of the hypothesized word embeddings.

MICROTOME PUBL2021

Learning computationally efficient static word and sentence representations

Prakhar Gupta

Most of the Natural Language Processing (NLP) algorithms involve use of distributed vector representations of linguistic units (primarily words and sentences) also known as embeddings in one way or another. These embeddings come in two flavours namely, static/non-contextual and contextual. In a static embedding, the vector representation of a word is independent of its context as opposed to a contextual embedding where the word representation incorporates additional information from its surrounding context.Recently, advancements in deep learning when applied to contextual embeddings have seen them outperforming their static counterparts. However, this improvement in performance with respect to that of the static embeddings has come at the cost of lesser computational efficiency in terms of both computational resources as well as training and inference times, relative lack of interpretability, and higher costs to the environment. Consequently, static embedding models despite not being as expressive and powerful as contextual embedding models continue to be of relevance in Natural Language Processing Research.In this thesis, we propose improvements to the current state-of-the-art static word embedding and sentence embedding models in three different settings. Firstly, we propose an improved algorithm to learn word and sentence embedding from raw text by proposing changes to the Word2Vec training objective formulation and adding n-grams to the training to incorporate local contextual information. Consequently, we end up obtaining improved unsupervised static word and sentence embeddings. Our second major contribution is learning cross-lingual static word and sentence representations from parallel bilingual data where two corpora are aligned sentence-wise. Our word and sentence embeddings thus obtained outperform other bag-of-words bilingual embeddings on cross-lingual sentence retrieval and monolingual word similarity tasks while staying competitive with them on cross-lingual word translation tasks. In our last major contribution, we aim towards harnessing the expressive power of the contextual embedding models by distilling static word embeddings from contextual embedding models to use improved word representations for computationally light tasks. This allows us to utilize the semantic information possessed by the contextual embedding models while maintaining computational efficiency for inference tasks at the same time.

EPFL2021

Learning computationally efficient static word and sentence representations

Prakhar Gupta

EPFL2021