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https://github.com/marceloprates/gender-bias

Data and code used to produce the analyses in the paper "Assessing gender bias in machine translation: a case study with Google Translate" (Neural Computing and Applications 2019). The goal of this paper was to measure the extent of gender bias in Google Translate. Our results show that GT is heavily biased towards male defaults, which are more pronounced in stereotypical job positions such as STEM fields.
https://github.com/marceloprates/gender-bias
Last synced: 26 days ago
JSON representation
Host: GitHub
URL: https://github.com/marceloprates/gender-bias
Owner: marceloprates
Created: 2017-12-12T11:18:26.000Z (almost 7 years ago)
Default Branch: master
Last Pushed: 2019-07-18T14:43:12.000Z (over 5 years ago)
Last Synced: 2024-05-02T01:09:26.869Z (6 months ago)
Language: Rich Text Format
Homepage: https://arxiv.org/abs/1809.02208
Size: 10.2 MB
Stars: 7
Watchers: 5
Forks: 6
Open Issues: 0
Metadata Files:
- Readme: README.html
Awesome Lists containing this project

README

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  margin-bottom: 16px;

}

.markdown-body hr {

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.markdown-body ul,

.markdown-body ol {

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}

.markdown-body ul ul,

.markdown-body ul ol,

.markdown-body ol ol,

.markdown-body ol ul {

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  margin-bottom: 0;

}

.markdown-body li>p {

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}

.markdown-body dl {

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}

.markdown-body dl dt {

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  margin-top: 16px;

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  font-style: italic;

  font-weight: bold;

}

.markdown-body dl dd {

  padding: 0 16px;

  margin-bottom: 16px;

}

.markdown-body blockquote {

  padding: 0 15px;

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}

.markdown-body blockquote>:first-child {

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}

.markdown-body blockquote>:last-child {

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}

.markdown-body table {

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  width: 100%;

  overflow: auto;

  word-break: normal;

  word-break: keep-all;

}

.markdown-body table th {

  font-weight: bold;

}

.markdown-body table th,

.markdown-body table td {

  padding: 6px 13px;

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}

.markdown-body table tr {

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  border-top: 1px solid #ccc;

}

.markdown-body table tr:nth-child(2n) {

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}

.markdown-body img {

  max-width: 100%;

  -moz-box-sizing: border-box;

  box-sizing: border-box;

}

.markdown-body code,

.markdown-body samp {

  padding: 0;

  padding-top: 0.2em;

  padding-bottom: 0.2em;

  margin: 0;

  font-size: 85%;

  background-color: rgba(0,0,0,0.04);

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.markdown-body code:after {

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.markdown-body pre>code {

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  margin: 0;

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.markdown-body .codehilite {

  margin-bottom: 16px;

}

.markdown-body .codehilite pre,

.markdown-body pre {

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  font-size: 85%;

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.markdown-body pre {

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.markdown-body pre code:before,

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.markdown-body .admonition table {

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.markdown-body .admonition p {

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.markdown-body .admonition-title {

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.markdown-body .admonition>.admonition-title {

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.markdown-body .attention>.admonition-title {

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.markdown-body .caution>.admonition-title {

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}

.markdown-body .hint>.admonition-title {

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.markdown-body .danger>.admonition-title {

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}

.markdown-body .question>.admonition-title {

  color: #96a6d7;

}

.markdown-body .note>.admonition-title {

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}

.markdown-body .admonition:before,

.markdown-body .attention:before,

.markdown-body .caution:before,

.markdown-body .hint:before,

.markdown-body .danger:before,

.markdown-body .question:before,

.markdown-body .note:before {

  font: normal normal 16px fontawesome-mini;

  -moz-osx-font-smoothing: grayscale;

  -webkit-user-select: none;

  -moz-user-select: none;

  -ms-user-select: none;

  user-select: none;

  line-height: 1.5;

  color: #333;

  position: absolute;

  left: 0;

  top: 0;

  padding-top: 10px;

  padding-left: 10px;

}

.markdown-body .admonition:before {

  content: "\f056\00a0";

  color: 333;

}

.markdown-body .attention:before {

  content: "\f058\00a0";

  color: #a6d796;

}

.markdown-body .caution:before {

  content: "\f06a\00a0";

  color: #d7a796;

}

.markdown-body .hint:before {

  content: "\f05a\00a0";

  color: #96c6d7;

}

.markdown-body .danger:before {

  content: "\f057\00a0";

  color: #c25f77;

}

.markdown-body .question:before {

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  color: #96a6d7;

}

.markdown-body .note:before {

  content: "\f040\00a0";

  color: #d7c896;

}

.markdown-body .admonition::after {

  content: normal;

}

.markdown-body .attention {

  border-left: 6px solid #a6d796;

}

.markdown-body .caution {

  border-left: 6px solid #d7a796;

}

.markdown-body .hint {

  border-left: 6px solid #96c6d7;

}

.markdown-body .danger {

  border-left: 6px solid #c25f77;

}

.markdown-body .question {

  border-left: 6px solid #96a6d7;

}

.markdown-body .note {

  border-left: 6px solid #d7c896;

}

.markdown-body .admonition>*:first-child {

  margin-top: 0 !important;

}

.markdown-body .admonition>*:last-child {

  margin-bottom: 0 !important;

}

/* progress bar*/

.markdown-body .progress {

  display: block;

  width: 300px;

  margin: 10px 0;

  height: 24px;

  -webkit-border-radius: 3px;

  -moz-border-radius: 3px;

  border-radius: 3px;

  background-color: #ededed;

  position: relative;

  box-shadow: inset -1px 1px 3px rgba(0, 0, 0, .1);

}

.markdown-body .progress-label {

  position: absolute;

  text-align: center;

  font-weight: bold;

  width: 100%; margin: 0;

  line-height: 24px;

  color: #333;

  text-shadow: 1px 1px 0 #fefefe, -1px -1px 0 #fefefe, -1px 1px 0 #fefefe, 1px -1px 0 #fefefe, 0 1px 0 #fefefe, 0 -1px 0 #fefefe, 1px 0 0 #fefefe, -1px 0 0 #fefefe, 1px 1px 2px #000;

  -webkit-font-smoothing: antialiased !important;

  white-space: nowrap;

  overflow: hidden;

}

.markdown-body .progress-bar {

  height: 24px;

  float: left;

  -webkit-border-radius: 3px;

  -moz-border-radius: 3px;

  border-radius: 3px;

  background-color: #96c6d7;

  box-shadow: inset 0 1px 0 rgba(255, 255, 255, .5), inset 0 -1px 0 rgba(0, 0, 0, .1);

  background-size: 30px 30px;

  background-image: -webkit-linear-gradient(

    135deg, rgba(255, 255, 255, .4) 27%,

    transparent 27%,

    transparent 52%, rgba(255, 255, 255, .4) 52%,

    rgba(255, 255, 255, .4) 77%,

    transparent 77%, transparent

  );

  background-image: -moz-linear-gradient(

    135deg,

    rgba(255, 255, 255, .4) 27%, transparent 27%,

    transparent 52%, rgba(255, 255, 255, .4) 52%,

    rgba(255, 255, 255, .4) 77%, transparent 77%,

    transparent

  );

  background-image: -ms-linear-gradient(

    135deg,

    rgba(255, 255, 255, .4) 27%, transparent 27%,

    transparent 52%, rgba(255, 255, 255, .4) 52%,

    rgba(255, 255, 255, .4) 77%, transparent 77%,

    transparent

  );

  background-image: -o-linear-gradient(

    135deg,

    rgba(255, 255, 255, .4) 27%, transparent 27%,

    transparent 52%, rgba(255, 255, 255, .4) 52%,

    rgba(255, 255, 255, .4) 77%, transparent 77%,

    transparent

  );

  background-image: linear-gradient(

    135deg,

    rgba(255, 255, 255, .4) 27%, transparent 27%,

    transparent 52%, rgba(255, 255, 255, .4) 52%,

    rgba(255, 255, 255, .4) 77%, transparent 77%,

    transparent

  );

}

.markdown-body .progress-100plus .progress-bar {

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}

.markdown-body .progress-80plus .progress-bar {

  background-color: #c6d796;

}

.markdown-body .progress-60plus .progress-bar {

  background-color: #d7c896;

}

.markdown-body .progress-40plus .progress-bar {

  background-color: #d7a796;

}

.markdown-body .progress-20plus .progress-bar {

  background-color: #d796a6;

}

.markdown-body .progress-0plus .progress-bar {

  background-color: #c25f77;

}

.markdown-body .candystripe-animate .progress-bar{

  -webkit-animation: animate-stripes 3s linear infinite;

  -moz-animation: animate-stripes 3s linear infinite;

  animation: animate-stripes 3s linear infinite;

}

@-webkit-keyframes animate-stripes {

  0% {

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  100% {

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}

@-moz-keyframes animate-stripes {

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  100% {

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}

@keyframes animate-stripes {

  0% {

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  }

  100% {

    background-position: 60px 0;

  }

}

.markdown-body .gloss .progress-bar {

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    inset 0 -12px 0 rgba(0, 0, 0, .05);

}

/* Multimarkdown Critic Blocks */

.markdown-body .critic_mark {

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}

.markdown-body .critic_delete {

  color: #c82829;

  text-decoration: line-through;

}

.markdown-body .critic_insert {

  color: #718c00 ;

  text-decoration: underline;

}

.markdown-body .critic_comment {

  color: #8e908c;

  font-style: italic;

}

.markdown-body .headeranchor {

  font: normal normal 16px octicons-anchor;

  line-height: 1;

  display: inline-block;

  text-decoration: none;

  -webkit-font-smoothing: antialiased;

  -moz-osx-font-smoothing: grayscale;

  -webkit-user-select: none;

  -moz-user-select: none;

  -ms-user-select: none;

  user-select: none;

}

.headeranchor:before {

  content: '\f05c';

}

.markdown-body .task-list-item {

  list-style-type: none;

}

.markdown-body .task-list-item+.task-list-item {

  margin-top: 3px;

}

.markdown-body .task-list-item input {

  margin: 0 4px 0.25em -20px;

  vertical-align: middle;

}

/* Media */

@media only screen and (min-width: 480px) {

  .markdown-body {

    font-size:14px;

  }

}

@media only screen and (min-width: 768px) {

  .markdown-body {

    font-size:16px;

  }

}

@media print {

  .markdown-body * {

    background: transparent !important;

    color: black !important;

    filter:none !important;

    -ms-filter: none !important;

  }

  .markdown-body {

    font-size:12pt;

    max-width:100%;

    outline:none;

    border: 0;

  }

  .markdown-body a,

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    content: " (" attr(href) ")";

  }

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  }

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  .markdown-body a[href^="javascript:"]:after,

  .markdown-body a[href^="#"]:after {

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  }

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    word-wrap: break-word;

  }

  .markdown-body pre,

  .markdown-body blockquote {

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    padding-right: 1em;

    page-break-inside: avoid;

  }

  .markdown-body .progress,

  .markdown-body .progress-bar {

    -moz-box-shadow: none;

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  .markdown-body .progress {

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  .markdown-body .progress-bar {

    height: 22px;

    border-right: 1px solid #ddd;

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  .markdown-body tr,

  .markdown-body img {

    page-break-inside: avoid;

  }

  .markdown-body img {

    max-width: 100% !important;

  }

  .markdown-body p,

  .markdown-body h2,

  .markdown-body h3 {

    orphans: 3;

    widows: 3;

  }

  .markdown-body h2,

  .markdown-body h3 {

    page-break-after: avoid;

  }

}

README


Gender Bias



In an attempt to shed further light on the phenomenon of Machine Bias, this repository provides a self-contained set of data and code resources to analyze and visualize gender assymetries in the output of statistical machine translation.



A Python script (script.py) translates a list of occupations collected from the U.S. Bureau of Labor Statistics into a set of selected gender-neutral languages, which is made available on TSV format in /Results/job-translations.tsv. Each of these occupations is then inputed into a template to build a simple sentence (“He/She is a \<Occupation Name>“) in each of the selected languages, and this sentences are afterwards translated into English. This processes potentially translates the gender neutral pronoun of the source language into a gender specific English pronoun (He or She). Collecting statistics about the gender ratio among different occupations grouped according to different categories helps uncover the extent of implicit gender stereotypes in statistical translation tools trained with real-world data.