Large Language Models (LLMs) have stunningly advanced the field of machine translation, though their effectiveness within the financial domain remains largely underexplored. To probe this issue, we constructed a fine-grained Chinese-English parallel corpus of financial news called FFN. We acquired financial news articles spanning between January 1st, 2014, to December 31, 2023, from mainstream media websites such as CNN, FOX, and China Daily. The dataset consists of 1,013 main text and 809 titles, all of which have been manually corrected. We measured the translation quality of two LLMs -- ChatGPT and ERNIE-bot, utilizing BLEU, TER and chrF scores as the evaluation metrics. For comparison, we also trained an OpenNMT model based on our dataset. We detail problems of LLMs and provide in-depth analysis, intending to stimulate further research and solutions in this largely uncharted territory. Our research underlines the need to optimize LLMs within the specific field of financial translation to ensure accuracy and quality.

Before AI came into use, translation was a job that was time-consuming, well-paid, and required a high level of education. Thanks to AI, translation software makes translating a common service that is instant, free, and convenient. In this article, we will explore what machine translation is, how AI improves the industry, and why AI-powered software cannot replace human translators. Machine Translation uses AI-powered software to automatically translate the language in the source material to another language, without any interventions from human agents. In 1970, the first machine translation software was developed.

Translation tools from Google and other companies could be contributing to significant misunderstanding of legal terms with conflicting meanings such as "enjoin," according to research due to be presented at an academic workshop. Google's translation software turns an English sentence about a court enjoining violence, or banning it, into one in the Indian language of Kannada that implies the court ordered violence, according to the new study. "Enjoin" can refer to either promoting or restraining an action. Mistranslations also arise with other contronyms, or words with contradictory meanings depending on context, including "all over," "eventual" and "garnish," the paper said. Google said machine translation is "is still just a complement to specialized professional translation" and that it is "continually researching improvements, from better handling ambiguous language, to mitigating bias, to making large quality gains for under-resourced languages."

It is not out of reason to boldly say that translation is of great importance to man. The diversity of languages and cultures in the world makes translation essential to humanity. The benefits of translation to humankind spread across businesses, politics, international relations, tourism, and education. Any company can go global. Moreover, the secret of a successful international business lies in quality translation services.

Google has released an improved version of its speech software for its cloud customers, and is allowing them to use the software more widely. The software is used for tasks such as transcription and voice commands. Google, which makes most of its money from digital advertising and search, sees enterprise offerings like cloud services as a key driver of future revenue growth, but it lags behind competitors that have been in the cloud space longer, like Amazon and Microsoft. The new version of Google's speech software is another example of how Google is trying to be a more competitive cloud provider. It's also another way to show off Google's AI abilities in what is quickly becoming a technology arms race with Amazon, Facebook and Apple. Google had released a beta version of the software before, but with certain restrictions.

Katrina Rippel is a careful speaker who follows all the rules. Hao Chen is a more freewheeling conversationalist. And I'm a nonstop troublemaker, constantly blurting out whatever notions pass through my head. On a recent morning, the three of us met in cyberspace to find out how well (or poorly) we could communicate in a mixture of German, Mandarin, and English. Each of us spoke only our native language.

In an ideal world, everyone would speak the same language or at least be able to understand other languages fluently. But we don't live in that ideal world, yet. We do, however, live, work, and interact in a global society, where effective communication with co-workers is vital, and machine translation software has become a must for any company that works on internationally. There are many types of machine-based translation software. The two types most talked about assist translators and those who can do the translation themselves.

The potential usefulness of automatic computerized translation was recognized by the very first AI researchers in the 1950s. But it wasn't until new algorithms emerged in the 1980s and 1990s that the field made significant progress. Now, translation tools of great sophistication are playing a growing role in both everyday office use and for specialized fields, as the economy becomes increasingly globalized and companies sell products and services in multiple markets. The poster child for computer translation is Google Translate, the easy-to-use, general purpose Web-based translation engine that can handle nearly 60 languages. Google's Translate has the same 800-pound gorilla status in its world that the company's namesake product does in search.

Compiled by Carl Zimmer (zimmer@panix.com) Natural Language Laboratory, School of Computing Science, Simon Fraser University Burnaby, British Columbia Researchers are devising "relaxed grammars" to extract the sense of transcribed speech through a method known as "partial parsing," which deciphers chunks of language rather than breaking down the structure of entire sentences. Their work is being integrated into closed-captioning technology for real-time TV translations. This article has been reproduced in a new format and may be missing content or contain faulty links. Contact wiredlabs@wired.com to report an issue.

Sometimes you may feel like there's nothing worth reading on the Web, but at least there's plenty of material you can read and understand. Millions of people around the world, in contrast, speak languages that are still barely represented online, despite widespread Internet access and improving translation technology. Web giants Microsoft and Google are trying to change that with new translation technology aimed at languages that are being left behind--or perhaps even being actively killed off--by the Web. Although both companies have worked on translation technology for years, they have, until now, focused on such major languages of international trade as English, Spanish, and Chinese. Microsoft and Google's existing translation tools, which are free, are a triumph of big data.