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Language is one the greatest gifts our world has ever received. Through language we’re able to communicate with each other and also express our uniqueness.
Languages are a way of seeing things. The way we explain different concepts – or whether the concepts exist in our language at all – provides a vital window to our culture. As Microsoft President Brad Smith put it: “When a community loses a language, it loses its connection to the past – and part of its present. It loses a piece of its identity.”
Te reo Māori is part of Aotearoa’s identity. When Kiwis travel, we communicate with others in English, the international language of business. But when we share what makes us unique, what do we talk about? Thanks to the success of the All Blacks, people know us for the haka. We’re also known for being “Kiwi”, for our celebrities such as Taika Waititi and places like Rotorua, Kaikoura and Waiheke. What all of these people, places and things have in common is a language – te reo Māori.
Growing up, I wasn’t able to express myself in the language of my people, something that’s helped shape who I am and what I do as the founder of a translation technology company today. Not only do we have to be open to using te reo Māori, we have to get smarter about harnessing technology to ensure more New Zealanders have an opportunity to engage with it as they go about their day.
The World Economic Forum is predicting that within the next 100 years, around 1,500 languages could disappear forever. The research found that regionally dominant languages taught in class often overshadow indigenous tongues. Unless they’re used regularly and encouraged to become a part of people’s daily lives, these foundations of our culture get lost. Without them, we lose so much more than just words – we lose what makes us uniquely us.
Are language service providers (LSPs) seizing the benefits offered by generative AI and large language models? An April 2024 Slator study has found that the answer is a resounding “yes”.
The survey of AI adoption across 223 companies found that one in three LSPs have implemented large language models into their workflows. Furthermore, close to 20% of companies have now tested use cases or beta versions.
Just 3% of LSPs told Slator that they have no plans to implement LLMs.
Beyond performing the core machine translation task, LLMs — such as Meta’s Llama 3 and OpenAI’s GPT 4 and 4o — have significant potential to augment and enable language AI services. Key applications include quality estimation, dynamic translation adaptation, and multilingual text generation, among others.
Gabriel Fairman, CEO of Bureau Works, speaking at the opening of SlatorCon London on Wednesday 21 May, emphasized that LLMs represent nothing less than “a new way for humans to relate to machines”.
In a May 16, 2024 paper, Jared Coleman, Bhaskar Krishnamachari, and Khalil Iskarous from the University of Southern California, along with Ruben Rosales from California State University introduced a new approach for machine translation (MT) that is “particularly useful” for no-resource languages, which lack publicly available bilingual or monolingual corpora.
This approach, named LLM-RBMT (LLM-Assisted Rule-Based Machine Translation), combines the strengths of large language models (LLMs) and rule-based machine translation (RBMT) techniques.
The researchers highlighted the exceptional capabilities of LLMs in MT but noted their limitations in low-resource or no-resource language scenarios. “There have been many efforts in improving MT for low-resource languages, but no-resource languages have received much less attention,” they said.
Despite the perception of RBMT as a “relic of the past”, the researchers emphasized ongoing research and development in RBMT systems tailored for under-resourced languages.
Japanese manga localization tech company Orange raised JPY 2.2bn (USD 19.5m) in what they called a “pre-Series A” financing, the company announced in a May 6, 2024 press release.
Manga publisher Shogakukan led the financing round. Other participants included the Japanese government’s Japan Industrial Innovation Investment Corporation, Globis Capital Partners, ANRI, SBI Investment, JIC Venture Growth Investments, Miyako Capital, Chiba Dojo Fund, Mizuho Capital, Mitsubishi UFJ Capital, and GFR Fund.
Orange, founded in 2021, uses computer vision and NLP, including AI translation, to translate manga titles in a fraction of the time required by traditional methods.
The company claims that it can translate, from Japanese to English, up to 500 manga titles per month — a figure Orange pegs at about five times the current industry rate.
The company’s ultimate goal: translate 50,000 manga titles in the next five years. Orange will use some of the funds raised in this round to launch a digital manga store called emaqi in summer 2024. The store will reportedly feature recommendations from manga influencers — as well as AI-generated suggestions.
It will also support AI dubbing and subtitles on LinkedIn, Coursera, Bloomberg, and more.
Microsoft Edge will soon offer real-time video translation on sites like YouTube, LinkedIn, Coursera, and more. As part of this year’s Build event, Microsoft announced that the new AI-powered feature will be able to translate spoken content through both dubbing and subtitles live as you’re watching it.
So far, the feature supports the translation of Spanish into English as well as the translation of English to German, Hindi, Italian, Russian, and Spanish. In addition to offering a neat way to translate videos into a user’s native tongue, Edge’s new AI feature should also make videos more accessible to those who are deaf or hard of hearing.
Edge will also support real-time translation for videos on news sites such as Reuters, CNBC, and Bloomberg. Microsoft plans on adding more languages and supported websites in the future.
Post-editing of machine translations is the second-most sought-after skill among language service providers
UOC researchers propose a new method for assessing AI tools to improve the work of translators
Machine translation systems are widely used in the translation industry, although the end results are always reviewed by people (foto: Adobe)
Juan F. Samaniego
ChatGPT and its ability to hold conversations and produce written content have been the focus of a lot of attention in the last year in the field of technology and artificial intelligence. However, AI has been around for some time, helping us in all sorts of everyday tasks, from navigation systems to social network algorithms, not to mention machine translation. Ever since neural machine translation (NMT) systems began to be used on a widespread basis a few years ago, AI has seen exponential growth in its uptake in the translation industry. This has led to new challenges in the relationship between human and machine translators.
Today, the post-editing of machine translations is the second-most sought-after skill among language service providers and is the task with the greatest growth potential, according to the European Language Industry Survey. Translators edit unprocessed machine translations, correcting texts produced by artificial intelligence. This brings with it many advantages for human translators, but also significant problems if the quality of the machine translation is poor. This is why the ability to objectively assess the quality of machine translation tools is essential for the sector.
COLOGNE: Online translation platform DeepL says it launching an AI assistant that, unlike ChatGPT and other rival AI chatbots, is designed to support the writing process with real-time suggestions on word choice, phrasing, style and tone.
The German software company says its new assistant allows users to optimise their texts regardless of their language skills and find the right words for any situation and any reader.
DeepL Write Pro is the company’s first product to be based on its own large AI language model (LLM). LLMs are machine learning models that are trained to understand and generate human language. Well-known LLMs include GPT from OpenAI, Gemini from Google and Llama from the Facebook group Meta.
The new AI writing assistant is primarily aimed at business users and is designed to support teams in companies when writing business content.
DeepL says the assistant, which currently works in English and German, can help companies ensure precise communication from internal content to external customer communications and contracts.
German startup DeepL, known for its AI-powered machine translation, is making a foray into the realm of writing where it adjusts the style of the sentences according to user preference with an emphasis on business application.
The startup launched the service, powered by its own large language model (LLM), in Korea on Friday. It is available in English and German with a plan to expand the number of languages supported.
The style of the sentence can be selected from four style options — business, academic, simple and casual — and four tone options — friendly, diplomatic, confident and enthusiastic.
“Words matter, and language can be the competitive edge that moves the needle for global businesses,” said Jarek Kutylowski, the company’s founder and CEO.
“DeepL Write Pro is our first product powered by our own LLMs, and is the culmination of years of research and innovation that has set us apart from other tech giants.”
In early January 2024, when many in the language industry were likely pondering how to eventually incorporate AI into their offerings or processes, OpenAI announced its GPT Store. Back then, a few translation GPTs could be found, including one built by Phrase called “Phrase Expert.”
By the time OpenAI launched the store to the general public, there were already more than three million GPTs done by pre-release testers. After the kind of hype seen in late 2022 with ChatGPT and all the drama surrounding the company’s CEO and Board in 2023, the store launch was also a popular subject in the news and on social media.
Then, the announcement of the company’s text-to-video generator, Sora, arrived in February 2024, lest people get OpenAI out of their minds for too long.
In March 2024, we asked readers if they had ever used Custom GPTs since the store was launched, and over two-thirds of respondents (64.6%) said No. Over a combined quarter of readers said they tested GPTs a bit after launch (14.6%) or from time to time (12.5%), and a very small percentage said they have been using them daily (8.3%).
Anuvadini, the AI translation tool that is breaking boundaries by translating movie dialogues from Vietnamese to diverse Indian languages will also translate ABK Media’s curated, “The Northeast Connect”, digital content into various Indian languages.
ABK Media, under the mentorship of Dr. Triveni Goswami Mathur, an Educator and Media & Communication Expert, has structured content that delves deep into the heart of Northeast India’s richness, a press release said.
The North East Connect digital content aims to promote cultural understanding and inclusivity. By unravelling the region’s socio-cultural complexities and economic potential, this content will serve as a beacon for scholars, entrepreneurs, and policymakers alike.
Overall, it will equip learners with knowledge to contribute positively to Northeast India’s development.
Anuvadini, will also become the first tool to translate the dialogue of a movie into various Indian languages.
The forthcoming language translation component won’t even require a drawn circle. Google says people will just have to long press the home button or the navigation bar and look for the translate icon. It’ll do the rest. The company showed the tech quickly translating an entire menu with one long press. Google Translate can already do this, though in a slightly different way, but this update means users won’t have to pop out of one app and into another just to check on something.
Google Meet has upgraded its service with AI note-taking and translation capabilities — although it requires an extra subscription cost.
Unveiled at this week’s Google Cloud Next, “Take notes for me” is now in public preview and allows users to engage with the conversation rather than having to manual take down notes or minutes.
Meanwhile, “Translate For Me” is coming in June and will automatically detect and translate captions in Meet. This includes support for 69 languages (equal to 4,600 language pairs), assisting users to feel more confident and connected to their colleagues, regardless of language.
“Workspace customers can already turn on translated captions during video calls and select their preferred language, helping Meet users around the world easily communicate,” said Aparna Pappu, Vice President and General Manager at Google Workspace.
“Statistics and data are what nourishes Machine Translation. But how do you take care of a high-powered machine that, with incorrect information, could cause problems in translation?”
“We have pocket translators and online translation tools in real life, any translation agency would know that they are prone to mistranslation even for simple phrases. But that hasn’t stopped Mark Zuckerberg, the owner of Meta, which manages social media giants like Facebook, and Instagram, to start developing an AI-powered technology that can help us communicate with anyone regardless of their language.”
With many organizations expanding their reach and going global, the role of translation is becoming increasingly important. Businesses need effective multilingual communication with their partners, employees and customers across cultural borders if they are to succeed in international markets.
(…) Some people think that the translating profession’s days are numbered as well. I hear it all the time from non-translators. “Is there even a need for translators when machine translation is easily available?” people sometime ask me when they find out what it is that I do for a living.
Most people have already discovered that machine translation often makes no sense, but they still don’t understand that it will never really make sense because the reason why machine translation often makes no sense is that it is not translation. It is basically just a bunch of words generated by a machine based on an algorithm and it is up to the reader to make sense of these words. More.
Twitter has quietly stopped offering users the ability to instantly translate tweets using Bing’s machine translation feature, slightly more than a year after the company started using Microsoft’s technology.
Users began noticing the feature’s absence earlier this week, though Twitter hasn’t said exactly when it stopped offering the service, or why. But one thing is clear: people who want to get tweets translated from a foreign language will have to copy and paste them into a translation service of choice, rather than clicking a button on Twitter’s website. More.
Up to this point, machine translation (MT) has been improving constantly. As a result of the work, the MT quality is getting better all the time. Ten years ago almost everyone could have said whether the translation was made by a machine or not. Today, between certain languages, simple texts translate so well automatically that it can be nearly impossible to say who made the translation.
To some extent, MT has reached or even exceeded the quality level that can be produced by someone who has studied the language as a hobby. (MT won’t ever be as good as fluent language speakers or translation professionals.) Because the quality is not people have started to use MT more actively. The usage is already so huge that it affects the quality. Whether the effect will be good or bad in the long run is unclear though. Let’s examine the options a little bit closer. More.
Google Translate has hit the 80 languages milestone, as Google announced the addition of support for nine more languages.
“Whether you’re trekking to a new place or simply trying to communicate with someone who doesn’t share a language with you, Google Translate can help you connect to new information and people,” says software engineer Arne Mauser. “Today, we’re launching 9 new languages that span Africa, Asia, and Oceania and have over 200 million native speakers, collectively.”
These languages would be: Hausa (Harshen Hausa), Igbo (Asụsụ Igbo), Yoruba (èdè Yorùbá), Somali (Af-Soomaali), Zulu (isiZulu), Mongolian (Монгол хэл), Nepali (नेपाली), Punjabi language (ਪੰਜਾਬੀ) (Gurmukhi script) and Maori (Te Reo Māori). More.
Contrary to what one might think, Google translate does not work by somehow parsing a text in the source language into its abstract meaning and then restituting that meaning in the target language. Google did not ask language experts to write grammars for every language that would parse them into semantic trees.
Instead, Google uses Statistical Machine Translation (SMT). SMT is based on using large amounts of human translations from language A to language B and uses statistical method to find the sentence it believes is the most likely language B translation of a sentence in language A based on the probabilities derived from that corpus. More.
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susan rose (X)
Egyesült Államok
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