{"id":179338,"date":"2024-05-03T18:17:08","date_gmt":"2024-05-03T17:17:08","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=179338"},"modified":"2024-02-22T10:34:29","modified_gmt":"2024-02-22T09:34:29","slug":"nlp-word-translation","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/nlp-word-translation","title":{"rendered":"NLP- Word translation"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"179338\" class=\"elementor elementor-179338\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-9b5b610 e-flex e-con-boxed e-con\" data-id=\"9b5b610\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-d6e3fda elementor-widget elementor-widget-heading\" data-id=\"d6e3fda\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\"><p>Today, we present the 4th installment of our exciting NLP dossier.<\/p>\n<p>In this article, we'll look at how to build a translation algorithm on Python -machine translation in English- that translates any word from a source language into a target language from words embeddings.<\/p><\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e79456c e-flex e-con-boxed e-con\" data-id=\"e79456c\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a991492 elementor-widget elementor-widget-text-editor\" data-id=\"a991492\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<strong>Missed the first episodes? Don&#8217;t worry, here they are:<\/strong>\n<ol>\n    <li><a href=\"https:\/\/datascientest.com\/introduction-au-nlp-natural-language-processing\">Introduction to NLP<\/a><\/li>\n    <li><a href=\"https:\/\/datascientest.com\/nlp-word-embedding-word2vec\">Word embedding &#8211; Word2vec<\/a><\/li>\n    <li>\n        <a href=\"https:\/\/datascientest.com\/nlp-word-translation\">NLP &#8211; Word translation<\/a>\n        <ol>\n            <li><a href=\"#matriceembedding\">What is an embedding matrix?<\/a><\/li>\n            <li><a href=\"#algorithme\">Implementation of a translation algorithm and its limits<\/a><\/li>\n        <\/ol>\n    <\/li>\n    <li><a href=\"https:\/\/datascientest.com\/nlp-twitter-analyse-de-sentiment\">NLP Twitter &#8211; Sentiment analysis<\/a><\/li>\n<\/ol>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0a35fe2 e-flex e-con-boxed e-con\" data-id=\"0a35fe2\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-612dd67 elementor-widget elementor-widget-text-editor\" data-id=\"612dd67\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Impl\u00e9menter ce genre d\u2019algorithme peut para\u00eetre, au premier abord, un peu flou. Math\u00e9matiquement, cela revient \u00e0<\/span> <b>trouver la matrice de correspondance (ou matrice d\u2019alignement) W<\/b><span style=\"font-weight: 400;\"> r\u00e9solvant le probl\u00e8me d&#8217;optimisation suivant :<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e346bc3 elementor-widget elementor-widget-image\" data-id=\"e346bc3\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"226\" height=\"68\" src=\"https:\/\/datascientest.com\/en\/files\/2020\/10\/Word-translation_8-.png\" class=\"attachment-full size-full wp-image-11356\" alt=\"Word translation\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-066d670 elementor-widget elementor-widget-text-editor\" data-id=\"066d670\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><strong>Where :<\/strong><\/p><ul><li>Od(R) is the set of orthogonal matrices (i.e. the set of M matrices with real coefficients, invertible and such that \ud835\udc40-1=\ud835\udc40T ).<\/li><li>X and Y are respectively the matrices containing the embeddings of transparent words (e.g. table, television, radio, piano, telephone, etc.) present in the source language (French) and in the target language (English).<\/li><li>X and Y are thus called the French and English word embedding matrices respectively.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1d352df elementor-widget elementor-widget-text-editor\" data-id=\"1d352df\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Transparent words will be essential to the construction of the translation algorithm, in that they correspond to reference points for aligning the two embedding vector spaces after application of the alignment matrix W. In other words, transparent words will make it possible to determine the existing geometric transformation between the two subspaces. In other words, the transparent words determine the geometric transformation between the two subspaces.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-76974fb e-flex e-con-boxed e-con\" data-id=\"76974fb\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-066387e elementor-widget elementor-widget-menu-anchor\" data-id=\"066387e\" data-element_type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\nbody.elementor-page .elementor-widget-menu-anchor{margin-bottom:0}<\/style>\t\t<div id=\"matriceembedding\" class=\"elementor-menu-anchor\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1046236 elementor-widget elementor-widget-heading\" data-id=\"1046236\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">By the way, what is an embedding matrix?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3ddecf4 elementor-widget elementor-widget-text-editor\" data-id=\"3ddecf4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>To explain what an embedding <a href=\"https:\/\/datascientest.com\/en\/confusion-matrix-what-is-it-and-how-do-i-use-it\">matrix<\/a> is, let&#8217;s take the following three sentences:<\/p><ul><li>When the worker left.<\/li><li>When the fisherman left.<\/li><li>When the dog left.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0d5f0e4 elementor-widget elementor-widget-text-editor\" data-id=\"0d5f0e4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Let&#8217;s imagine that we don&#8217;t know the meaning of the words &#8216;worker&#8217;, &#8216;fisherman&#8217; and &#8216;dog&#8217;, but we&#8217;re able to say that these three sentences are identical except for one word. As the contexts are similar, we could deduce that there is a certain similarity between &#8216;worker&#8217;, &#8216;fisherman&#8217; and &#8216;dog&#8217;.<\/p><p>Applying this idea to an entire corpus, we could define relations between several words in this way.<\/p><p><em>However, the question remains:<\/em> how best to represent these relationships? This is where the concept of the embedding matrix comes in.<\/p><p>For the sake of simplicity, let&#8217;s imagine that each word and its meaning can be represented in an abstract three-dimensional space.<\/p><p>Conceptually, this means that all words exist as singular points in a 3D space, and any word can be uniquely defined by its position in this space described by three numbers (x, y, z).<\/p><p>In reality, however, the meaning of a word is too complicated to be simply described in a three-dimensional space, and as a general rule it takes almost 300 dimensions to fully define a word and its meaning.<\/p><p>The vector of 300 numbers that identifies a given word is called the embedding of that word.<\/p><p>Ultimately, an <strong>embedding matrix<\/strong> is nothing more than a matrix containing a wide variety of words and their corresponding embeddings. Below is an example of an English embedding matrix:<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a2aab12 elementor-widget elementor-widget-image\" data-id=\"a2aab12\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"512\" height=\"176\" src=\"https:\/\/datascientest.com\/en\/files\/2020\/10\/Word-translation_3-.png\" class=\"attachment-full size-full wp-image-11361\" alt=\"Word translation\" srcset=\"https:\/\/datascientest.com\/en\/files\/2020\/10\/Word-translation_3-.png 512w, https:\/\/datascientest.com\/en\/files\/2020\/10\/Word-translation_3--300x103.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-b84e4bd e-flex e-con-boxed e-con\" data-id=\"b84e4bd\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-880669f elementor-widget elementor-widget-menu-anchor\" data-id=\"880669f\" data-element_type=\"widget\" data-widget_type=\"menu-anchor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div id=\"algorithme\" class=\"elementor-menu-anchor\"><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b742158 elementor-widget elementor-widget-heading\" data-id=\"b742158\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Impl\u00e9mentation d\u2019un algorithme de traduction et ses limites<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-82d2f57 elementor-widget elementor-widget-text-editor\" data-id=\"82d2f57\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">In this section, we will implement a translation <a href=\"https:\/\/datascientest.com\/en\/boosting-business-with-3-essential-machine-learning-algorithms\">algorithm<\/a> and try to determine both its strengths and weaknesses.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-83dd516 elementor-widget elementor-widget-heading\" data-id=\"83dd516\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\"><u>Step 0:<\/u><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a1da9f4 elementor-widget elementor-widget-text-editor\" data-id=\"a1da9f4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Recovery of two embedding matrices U1 and U2 respectively French and English pre-trained on Common Crawl and Wikipedia using fastText.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c2989dc elementor-widget elementor-widget-image\" data-id=\"c2989dc\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"2560\" height=\"2438\" src=\"https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-02.png\" class=\"attachment-full size-full wp-image-11990\" alt=\"embedding\" srcset=\"https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-02.png 2560w, https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-02-300x286.png 300w, https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-02-1024x975.png 1024w, https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-02-768x731.png 768w\" sizes=\"(max-width: 2560px) 100vw, 2560px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Matrice d\u2019embeddings U1 et U2<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-89f81dd elementor-widget elementor-widget-shortcode\" data-id=\"89f81dd\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/garybedoucha\/4ea1873b00971dbda24e58a20773f89d.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9823aba elementor-widget elementor-widget-heading\" data-id=\"9823aba\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\"><u>Step 1:<\/u><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8140452 elementor-widget elementor-widget-text-editor\" data-id=\"8140452\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>First, we need to define a bilingual dictionary (e.g. French-English) containing all the transparent words contained in both vocabularies. Once this has been obtained, we need to construct the embedding matrices X and Y corresponding respectively to the French and English matrices containing the embeddings associated with the words in the previously defined bilingual dictionary.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5e9fda8 elementor-widget elementor-widget-shortcode\" data-id=\"5e9fda8\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/garybedoucha\/e945b8353e682615808ea7d6a4da1a40.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0e1e5da elementor-widget elementor-widget-heading\" data-id=\"0e1e5da\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\"><u>Step 2:<\/u><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6e30aa7 elementor-widget elementor-widget-image\" data-id=\"6e30aa7\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"4961\" height=\"4726\" src=\"https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-01.png\" class=\"attachment-full size-full wp-image-11991\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-01.png 4961w, https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-01-300x286.png 300w, https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-01-1024x975.png 1024w, https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-01-768x732.png 768w, https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-01-1536x1463.png 1536w, https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-01-2048x1951.png 2048w\" sizes=\"(max-width: 4961px) 100vw, 4961px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Explanatory diagram of step 2<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1c80cef elementor-widget elementor-widget-text-editor\" data-id=\"1c80cef\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The aim of this second step is to<strong> find the W application (correspondence matrix)<\/strong> that will project the source word vector space (e.g. French) onto the target word vector space (e.g. English).<\/p><p>Obviously, the more similar the structure of the embedding spaces, the more efficient this method will be (which is the case here with French and English).<\/p><p>As we saw in the preamble, W* = argmin || WX &#8211; Y || under the constraint that W is an orthogonal matrix.<\/p><p>Using orthogonality and the properties of the trace of a matrix, we can prove that<\/p><p><strong>W*=UVT with U\u03a3VT = SVD (YXT)<\/strong><\/p><p>Where SVD is the singular value decomposition of a matrix (a generalization of the spectral theorem which states that all orthogonal matrices can be diagonalized by an orthonormal basis of eigenvectors).<\/p><p>Given the previous result, finding W is simply a matter of singular-value decomposition of the matrix \ud835\udc4c\ud835\udc4bT.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0a28fef elementor-widget elementor-widget-shortcode\" data-id=\"0a28fef\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/garybedoucha\/49cc5f5c1d4c7f6f756d3394dff8ba95.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8937593 elementor-widget elementor-widget-text-editor\" data-id=\"8937593\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Here is the W matrix obtained as code output:<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f2fad4e elementor-widget elementor-widget-image\" data-id=\"f2fad4e\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"504\" height=\"193\" src=\"https:\/\/datascientest.com\/en\/files\/2020\/10\/Word-translation_7-.png\" class=\"attachment-full size-full wp-image-11357\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2020\/10\/Word-translation_7-.png 504w, https:\/\/datascientest.com\/en\/files\/2020\/10\/Word-translation_7--300x115.png 300w\" sizes=\"(max-width: 504px) 100vw, 504px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Resulting W matrix<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-94257bd elementor-widget elementor-widget-heading\" data-id=\"94257bd\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-medium\"><u>Step 3:<\/u><\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-974257e elementor-widget elementor-widget-image\" data-id=\"974257e\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"2560\" height=\"2438\" src=\"https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-03.png\" class=\"attachment-full size-full wp-image-11989\" alt=\"embedding\" srcset=\"https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-03.png 2560w, https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-03-300x286.png 300w, https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-03-1024x975.png 1024w, https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-03-768x731.png 768w, https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-03-1536x1463.png 1536w, https:\/\/datascientest.com\/en\/files\/2020\/10\/illustration_vector-03-2048x1951.png 2048w\" sizes=\"(max-width: 2560px) 100vw, 2560px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Applying the W matrix to the vector space of French word embeddings<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dbd8cdb elementor-widget elementor-widget-text-editor\" data-id=\"dbd8cdb\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Once the W alignment matrix has been obtained, we can now find the English translation of any word in the French vocabulary by identifying the <a href=\"https:\/\/datascientest.com\/en\/knn-what-is-the-knn-algorithm\">source words&#8217; nearest neighbors<\/a> in the target word vector space.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-79ef046 elementor-widget elementor-widget-shortcode\" data-id=\"79ef046\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/garybedoucha\/cb81402a8eebebe155a44b2d9f03a389.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6381178 elementor-widget elementor-widget-text-editor\" data-id=\"6381178\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Here are the results (each time, 3 English terms closest to the translation and their probability of correspondence with the word to be translated):<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4361fab elementor-widget elementor-widget-image\" data-id=\"4361fab\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"274\" height=\"298\" src=\"https:\/\/datascientest.com\/en\/files\/2020\/10\/Word-translation_5-.png\" class=\"attachment-full size-full wp-image-11359\" alt=\"Word translation\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\">Result of the translation algorithm<\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8d377d5 elementor-widget elementor-widget-text-editor\" data-id=\"8d377d5\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>As we can see, this algorithm is quite powerful and can be used as a French-English word translator.<\/p><p>We could try using this kind of algorithm to translate an entire text:<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6580935 elementor-widget elementor-widget-shortcode\" data-id=\"6580935\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/garybedoucha\/1c48d2a19c9023d2f4a6d9bc2b3aaa6c.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f16be83 elementor-widget elementor-widget-text-editor\" data-id=\"f16be83\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p><span style=\"font-weight: 400;\">Here is the result<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-de6d869 elementor-widget elementor-widget-image\" data-id=\"de6d869\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"824\" height=\"21\" src=\"https:\/\/datascientest.com\/en\/files\/2020\/10\/Word-translation_9.png\" class=\"attachment-full size-full wp-image-11364\" alt=\"Word translation\" srcset=\"https:\/\/datascientest.com\/en\/files\/2020\/10\/Word-translation_9.png 824w, https:\/\/datascientest.com\/en\/files\/2020\/10\/Word-translation_9-300x8.png 300w, https:\/\/datascientest.com\/en\/files\/2020\/10\/Word-translation_9-768x20.png 768w\" sizes=\"(max-width: 824px) 100vw, 824px\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6ff5488 elementor-widget elementor-widget-text-editor\" data-id=\"6ff5488\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Given the <strong>difference in linguistic structure between French and English<\/strong> (e.g. noun-adjective order in French, adjective-noun order in English), word-to-word translation is not optimal. What&#8217;s more, we can see that our translation algorithm doesn&#8217;t work for words such as: de, en, un, une, etc.<\/p><p>As we suspected,<strong> word-for-word translation<\/strong> is not the most efficient way to translate a text. One possible solution is to use a Seq2seq (or BERT) approach, which will be the subject of a forthcoming article.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2618092 elementor-widget elementor-widget-text-editor\" data-id=\"2618092\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Did you like this article?<\/p><p>Would you like to learn more about Deep Learning?<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-765e5e7 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"765e5e7\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-courses\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover our training courses<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Today, we present the 4th installment of our exciting NLP dossier. In this article, we&#8217;ll look at how to build a translation algorithm on Python -machine translation in English- that translates any word from a source language into a target language from words embeddings. Missed the first episodes? Don&#8217;t worry, here they are: Introduction to [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":179341,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>NLP- Word translation<\/title>\n<meta name=\"description\" content=\"Delve into NLP word translation, a key aspect of natural language processing (NLP) that involves translating\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/nlp-word-translation\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"NLP- Word translation\" \/>\n<meta property=\"og:description\" content=\"Today, we present the 4th installment of our exciting NLP dossier. 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