Arab News boosts female staff in drive to become first Saudi ‘gender-balanced’ newspaper

Arab News' Jeddah bureau. The newspaper aims to have a 50:50 gender-balanced newsroom by 2020. (AN Photo by Huda Bashatah)
Updated 08 March 2019

Arab News boosts female staff in drive to become first Saudi ‘gender-balanced’ newspaper

RIYADH: The proportion of women working for Arab News rose to more than a third in 2018, moving closer toward the Riyadh-based newspaper’s target to have a 50:50 gender-balanced newsroom by 2020.
The ratio of women working across the global operations — including editorial staff in the Saudi, London and Dubai bureaus, regular Opinion writers and foreign correspondents — stood at 35 percent at the end of 2018.
That compares to 31 percent in 2017 and just 13 percent the previous year, according to Arab News’ “Gender equality meter,” published today.
Arab News last year outlined its aim to become the first newspaper in Saudi Arabia to have a gender-balanced newsroom. The drive — referred to internally as the “50:50 by 2020” initiative — covers all the newspaper’s bureaus and areas of operation.

The increase in the proportion of female staff last year was the result of active recruitment, increased training initiatives, and steps to provide career guidance with the help of the newspaper’s publisher, the Saudi Research and Marketing Group.
Faisal J. Abbas, editor in chief of Arab News, said the newspaper’s “50:50 by 2020” initiative reflected the wider reform drive in Saudi Arabia, part of which is to encourage more women into work.
“Having a diverse newsroom is not about ticking boxes — it is about giving equal opportunities to skilled journalists based in Saudi Arabia and beyond, while also providing training and nurturing young talent within the Kingdom,” said Abbas.
“It is also about serving our community better by doing what we do best — quality, insightful and inclusive journalism.”
“Our ‘50:50 by 2020’ initiative is in line with the positive steps in Saudi Arabia toward giving opportunities to everyone in society, especially the burgeoning youth population.”
Further announcements regarding the progress of the “50:50 by 2020” initiative will be made in the future, along with updates to the Arab News “Gender equality meter.”


Facebook researchers use maths for better translations

Updated 13 October 2019

Facebook researchers use maths for better translations

  • Facebook researchers say rendering words into figures and exploiting mathematical similarities between languages is a promising avenue
  • Allowing as many people as possible worldwide to communicate is not just an altruistic goal, but also good business

PARIS: Designers of machine translation tools still mostly rely on dictionaries to make a foreign language understandable. But now there is a new way: numbers.

Facebook researchers say rendering words into figures and exploiting mathematical similarities between languages is a promising avenue — even if a universal communicator a la Star Trek remains a distant dream.

Powerful automatic translation is a big priority for Internet giants. Allowing as many people as possible worldwide to communicate is not just an altruistic goal, but also good business.

Facebook, Google and Microsoft as well as Russia’s Yandex, China’s Baidu and others are constantly seeking to improve their translation tools.

Facebook has artificial intelligence experts on the job at one of its research labs in Paris. Up to 200 languages are currently used on Facebook, said Antoine Bordes, European co-director of fundamental AI research for the social network.

Automatic translation is currently based on having large databases of identical texts in both languages to work from. But for many language pairs there just aren’t enough such parallel texts.

That’s why researchers have been looking for another method, like the system developed by Facebook which creates a mathematical representation for words.

Each word becomes a “vector” in a space of several hundred dimensions. Words that have close associations in the spoken language also find themselves close to each other in this vector space.

“For example, if you take the words ‘cat’ and ‘dog’, semantically, they are words that describe a similar thing, so they will be extremely close together physically” in the vector space, said Guillaume Lample, one of the system’s designers.

“If you take words like Madrid, London, Paris, which are European capital cities, it’s the same idea.”

These language maps can then be linked to one another using algorithms — at first roughly, but eventually becoming more refined, until entire phrases can be matched without too many errors.

Lample said results are already promising. For the language pair of English-Romanian, Facebook’s current machine translation system is “equal or maybe a bit worse” than the word vector system, said Lample.

But for the rarer language pair of English-Urdu, where Facebook’s traditional system doesn’t have many bilingual texts to reference, the word vector system is already superior, he said.

But could the method allow translation from, say, Basque into the language of an Amazonian tribe? In theory, yes, said Lample, but in practice a large body of written texts are needed to map the language, something lacking in Amazonian tribal languages.

“If you have just tens of thousands of phrases, it won’t work. You need several hundreds of thousands,” he said.

Experts at France’s CNRS national scientific center said the approach Lample has taken for Facebook could produce useful results, even if it doesn’t result in perfect translations.

Thierry Poibeau of CNRS’s Lattice laboratory, which also does research into machine translation, called the word vector approach “a conceptual revolution.”

He said “translating without parallel data” — dictionaries or versions of the same documents in both languages — “is something of the Holy Grail” of machine translation.

“But the question is what level of performance can be expected” from the word vector method, said Poibeau. The method “can give an idea of the original text” but the capability for a good translation every time remains unproven.

Francois Yvon, a researcher at CNRS’s Computer Science Laboratory for Mechanics and Engineering Sciences, said “the linking of languages is much more difficult” when they are far removed from one another.

“The manner of denoting concepts in Chinese is completely different from French,” he added.
However even imperfect translations can be useful, said Yvon, and could prove sufficient to track hate speech, a major priority for Facebook.