How AI and ML make language translation more efficient for non-English speakers
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Imagine visiting a website, clicking one link after another and getting nowhere – the links are either dead or they keep sending you back to a language you don’t read or speak. The frustration would immediately flare up.
Now consider that this is a daily experience for millions of non-English speakers in the world.
“The native market needs to provide the same great customer experience as in all other markets. And that’s just not true for most companies right now,” said Spence Green, CEO of Lilt, a translation software and services company.
While most companies pride themselves on being global and meeting customers “where they are,” their websites, customer support, and social media channels often indicate otherwise.
Of the thousands of spoken languages in the world, only about 100 are on the Internet. The largest companies support about 70 languages, but the average company can accommodate fewer than 10, according to Lilt’s research.
Add to that the fact that by 2030 there will be more than 4.7 billion middle-class and affluent consumers in non-English speaking regions, as estimated by Brookings Institute.
The power of diversity
Language is central to the customer experience and is a critical part of global commerce. By not making themselves linguistically diverse, companies lose existing and potential customers, revenue and brand loyalty, Green said.
“The question to ask is, ‘Why isn’t every company doing this now?’ and the reason is the same as for individuals: we don’t see the whole other world out there,” Green said.
But, he added, “It should be intuitive that language is part of a personalized customer experience.”
Lilt claims its $23.8 billion claim in the global language services and technology industry. The company, which today announced a $55 million Series C financing round, offers translation technology and services through a human-in-the-loop AI approach. This branch of AI creates machine learning (ML) models of both human and machine intelligence.
The company delivers scalable business translations with a system that Green described as adaptive, machine learning, interactive and predictive. This is critical, he said, as the industry moves from high labor intensity to high automation.
He pointed out that the average consumer uses 10 channels to interact with companies and that 80% of customers now value their experience with a company as much as their products – not the other way around. Still, a survey by the online payment portal Stripe found that 74% of European e-commerce websites had not translated their payment pages into local languages. So visitors couldn’t even understand the information in front of them. Such errors on the checkout page are responsible for 9 out of 10 lost sales in Europe.
Test it yourself
Just test it for yourself by going to a company website, changing the language and clicking on links. As Green pointed out, you are invariably kicked back to English.
“That’s the equivalent of broken links and broken images on your website, which no business would ever tolerate,” he said.
And there’s no reason why it should happen, he said. “It’s just fixable, it can be repaired.”
Businesses just need to make it a strategic priority. If they do, they will see measurable returns. “Incorporating this into your customer experience and your digital presence is just a completely obvious thing,” said Green. “It leads to growth. Any company can do this now.”
Green’s vision is machine translation capabilities – just the click of a button, wherever or however someone writes. That button would be integrated directly into business systems. That shouldn’t be too far off, as he underlined the fact that in five years of R&D, Lilt has accelerated the number of words the machine generates from 50% to 80%.
“It becomes measurably more efficient,” says Green.
The company has worked with Intel, ASICS, Emerson, UIPath and Canva, with local governments such as Lewiston, Maine and with the US government. In Ukraine, for example, Lilt is involved in defense and intelligence applications. As Green noted, the FBI has not invested in Slavic language capabilities since the end of the Cold War and the situation is too dire to train human talent, which could take months.
The new funding round was led by Four Rivers, joined by new investors Sorenson Capital, CLEAR Ventures and Wipro Ventures and with the participation of existing investors Sequoia Capital, Intel Capital, Redpoint Ventures and XSeed Capital. It brings the total amount raised from the company to $92.5 million.
The company will use the new funding to expand its team and international footprint, while further strengthening its R&D efforts.
“We are taking another important step toward achieving our mission of making the world’s information accessible to everyone,” Green said, “regardless of where they were born or what language they speak.”
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This post How AI and ML make language translation more efficient for non-English speakers
was original published at “https://venturebeat.com/2022/04/08/how-ai-and-ml-makes-language-translation-more-efficient-for-non-english-speakers/”
