Tech giant MetaAhas created a single artificial intelligence (AI)-based model capable of translating across 200 different languages, including many not supported by current commercial tools. According to The Verge, the company is open-sourcing the project in the hopes that others will build on its work. The AI model is part of an ambitious R&D project by Meta to create a so-called "universal speech translator," which the company sees as important for growth across its many platforms -- from Facebook and Instagram to developing domains like VR and AR. Machine translation not only allows Meta to better understand its users (and so improve the advertising systems that generate 97 per cent of its revenue) but could also be the foundation of a killer app for future projects like its augmented reality glasses. Experts in machine translation told the website that Meta's latest research was ambitious and thorough, but noted that the quality of some of the model's translations would likely be well below that of better-supported languages like Italian or German.

Language is our lifeline to the world. But because high-quality translation tools don't exist for hundreds of languages, billions of people today can't access digital content or participate fully in conversations and communities online in their preferred or native languages. This is particularly an issue for hundreds of millions of people who speak the many languages of Africa and Asia. To help people connect better today and be part of the metaverse of tomorrow, our AI researchers created No Language Left Behind (NLLB), an effort to develop high-quality machine translation capabilities for most of the world's languages. Today, we're announcing an important breakthrough in NLLB: We've built a single AI model called NLLB-200, which translates 200 different languages with results far more accurate than what previous technology could accomplish.

Imagine a surgeon taking video calls with patients across the globe without the need of a human translator. What if a fledgling startup could easily expand their product across borders and into new geographical markets by offering fluid, accurate, multilingual customer support and sales, all without the need of a live human translator? What happens to your business when you're no longer bound by language? It's common today to have virtual meetings with international teams and customers that speak many different languages. Whether they're internal or external meetings, meaning often gets lost in complex discussions and you may encounter language barriers that prevent you from being as effective as you could be.

Facebook's owner Meta has created an artificial intelligence model that can translate 204 written languages and has released it under an open source licence so that anyone can use or improve the software. The company claims that the AI supports more languages and provides higher-quality translations than world-leading software. The model, called No Language Left Behind, supports dozens more text-based languages than Google Translate, which currently works for 133, and Microsoft Translator, which caters for 110.

Abstract: Numerical simulations can help solve complex problems. Most of these algorithms are massively parallel and thus good candidates for FPGA acceleration thanks to spatial parallelism. Modern FPGA devices can leverage high-bandwidth memory technologies, but when applications are memory-bound designers must craft advanced communication and memory architectures for efficient data movement and on-chip storage. This development process requires hardware design skills that are uncommon in domain-specific experts. In this paper, we propose an automated tool flow from a domain-specific language (DSL) for tensor expressions to generate massively-parallel accelerators on HBM-equipped FPGAs.

Have you ever wondered how to make your videos reachable to a wider audience spanning across multiple languages? Adding subtitles in regional languages is one way. But subtitles reduce focus on the actual content of the video. Definitely adding vocal narration improves comprehensibility of a video. But isn't it too much work to create vocal narration separately in individual languages?

In this paper we share findings from our effort to build practical machine translation (MT) systems capable of translating across over one thousand languages. We describe results in three research domains: (i) Building clean, web-mined datasets for 1500+ languages by leveraging semi-supervised pre-training for language identification and developing data-driven filtering techniques; (ii) Developing practical MT models for under-served languages by leveraging massively multilingual models trained with supervised parallel data for over 100 high-resource languages and monolingual datasets for an additional 1000+ languages; and (iii) Studying the limitations of evaluation metrics for these languages and conducting qualitative analysis of the outputs from our MT models, highlighting several frequent error modes of these types of models. We hope that our work provides useful insights to practitioners working towards building MT systems for currently understudied languages, and highlights research directions that can complement the weaknesses of massively multilingual models in data-sparse settings.

There has been a surge in research in multimodal machine translation (MMT), where additional modalities such as images are used to improve translation quality of textual systems. A particular use for such multimodal systems is the task of simultaneous machine translation, where visual context has been shown to complement the partial information provided by the source sentence, especially in the early phases of translation. In this paper, we propose the first Transformer-based simultaneous MMT architecture, which has not been previously explored in simultaneous translation. Additionally, we extend this model with an auxiliary supervision signal that guides the visual attention mechanism using labelled phrase-region alignments. We perform comprehensive experiments on three language directions and conduct thorough quantitative and qualitative analyses using both automatic metrics and manual inspection. Our results show that (i) supervised visual attention consistently improves the translation quality of the simultaneous MMT models, and (ii) fine-tuning the MMT with supervision loss enabled leads to better performance than training the MMT from scratch. Compared to the state-of-the-art, our proposed model achieves improvements of up to 2.3 BLEU and 3.5 METEOR points.

The automatic translation of text from one natural language (the source) to another is known as machine translation (the target). It was one of the first applications for computers that were imagined (Weaver, 1949). The translation is tough since it necessitates a thorough understanding of the text in the most general scenario. This is true even for very basic messages, such as one-word "texts." Consider the word "Open" on a store's front door.

The European Association for Machine Translation (EAMT) conference is a venue where MT researchers, users and translators gather to discuss the latest advances in the industry. It is really interesting to go there and see what is going on in the European continent in terms of MT development and adoption. In this article, I want to share some ideas from the Best Paper Award of this year. Its title is "Searching for COMETINHO: The Little Metric That Could", from the research lab of Unbabel, a company based in Lisbon, Portugal that offers translation services using MT and human translators. You can find the online version of the paper in the ACL Anthology.