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A native-speaker-curated dataset called MIDI (Multilingual Idiom Dataset) is tracking how large language models interpret idiomatic expressions across 18 high-, medium-, and low-resource languages. The dataset tests AI by placing these idioms into both isolated sentences and multi-turn conversations to see if models can successfully distinguish between a phrase's literal meaning and its culturally grounded, figurative translation. This research exposes a major flaw in how we currently evaluate machine intelligence: our models are largely memorizing linguistic patterns rather than achieving true cultural reasoning. While LLMs are highly proficient at mapping figurative translations in high-resource environments, they struggle significantly with low-resource languages and, ironically, have a much harder time recognizing when an idiom is meant entirely literally within a conversation. For AI to become a truly global, empathetic tool, engineers must move past raw data scaling. We need architectures capable of dynamic, context-aware reasoning that can grasp the nuanced, localized intent behind human speech rather than just regurgitating statistical probabilities. Read full paper >> arxiv.org/abs/2606.02147 @bilalelbouardi @salsabilzahirah @Irina_Akishina @Ashwathrao @Paramkrishnaa @tebe5496 @bao_nguyen38224 @AmirHossein1040 @mukhitson @MennaAttia3 @besherhassan @ahmadfauzanhid4 @ttk_kuribayashi @haonanlp #EqualyzAI #ArtificialIntelligence #LLMs #NLP #MachineTranslation #EthicalAI #TechInnovation #GenerativeAI
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Why does it matter if an AI Translation tool stores your text to train its model? If an AI model trains on your confidential data, that information can inadvertently resurface. If a user elsewhere in the world prompts the same AI engine with a highly specific query related to your industry or company, elements of your leaked data could theoretically be generated in their results. This isn’t science fiction, major global tech firms have already banned internal use of public AI tools precisely because proprietary source code was leaked this way. Learn More: bolingoconsult.com/translati… #DataPrivacy #MachineTranslation #InformationSecurity #Compliance #AITranslation #KeepTranslate
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Late update, but we had two great talks last month! #MachineTranslation #FBK #NLProc #GenderBias #SpeechSynthesis
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In a retrospective article, Jaap van der Meer and Anne-Maj van der Meer detail how @TAUS_Data developed the EPIC Partner Program to train specialized models for distinct domains like legal, clinical trials, and gaming. Read the full story here: buff.ly/uaIBZkc #MultiLingualMedia #LanguageIndustry #TranslationTech #AI #MachineTranslation #TAUS #MultiLingualMedia
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šŸŒ šš¢ š’šœš”šØšØš„ š©š«šžš¬šžš§š­š¬ šŒšžšžš­š°šžšžš§ ššš­ š“š€š”š’ šŸšŸŽšŸšŸ” š–š”ššš­ š¢šŸ š²šØš® šœšØš®š„š š¬šžš§š šš šš¢š š¢š­ššš„ šššÆššš­ššš« š­šØ š²šØš®š« š§šžš±š­ šœššš„š„? That's not a hypothetical. It's what the Meetween project is building. On 5 June, Pi School's Managing Director SĆ©bastien BratiĆØres will take the stage at the TAUS Massively Multilingual AI Conference in Rome to present Meetween, the EU Horizon Europe project developing technology for multilingual, multimodal AI-powered meetings, where language and culture no longer become barriers. The consortium behind Meetween includes Academic Computer Centre CYFRONET AGH, Fondazione Bruno Kessler - FBK, İstanbul Teknik Üniversitesi, Karlsruher Institut für Technologie (KIT), Zoom, Translated, and TAUS, the organiser of the event. šŸ“ Rome | 5 June, 11:15 | Day 2 š…šØš„š„šØš° @MeetweenEU on LinkedIn and X and visit šŸ‘‰Ā  pischool.link/Meetween #Meetween #TAUS2026 #MultilingualAI #MachineTranslation #EUHorizon #ArtificialIntelligence #NLP #HorizonEurope
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Replying to @TencentAI_News
One More Thing. We are thrilled to announce that Tencent Hy is officially collaborating with the WMT26 organizers on the Preliminary Video Subtitle Translation Competition! šŸ‘‰Join here: statmt.org/wmt26/video-subti… Powered by our advanced Hy-MT series models, you can also take part in the General Machine Translation Competition. šŸ“· Join here: statmt.org/wmt26/translation… Top participants have the chance to win special grand prizes! We warmly invite researchers, developers, AI enthusiasts, and everyone passionate about machine translation to join us. Together, let’s push the boundaries of AI and drive the future of machine translation technology forward! Don’t miss this incredible opportunity — register and participate today! #WMT26 #Hy #MachineTranslation #AIInnovationWe look forward to seeing your submissions!

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One More Thing. We are thrilled to announce that Tencent Hy is officially collaborating with the WMT26 organizers on the Preliminary Video Subtitle Translation Competition! šŸ‘‰Join here: statmt.org/wmt26/video-subti… Powered by our advanced Hy-MT series models, you can also take part in the General Machine Translation Competition. šŸ“· Join here: statmt.org/wmt26/translation… Top participants have the chance to win special grand prizes! We warmly invite researchers, developers, AI enthusiasts, and everyone passionate about machine translation to join us. Together, let’s push the boundaries of AI and drive the future of machine translation technology forward! Don’t miss this incredible opportunity — register and participate today! #WMT26 #Hy #MachineTranslation #AIInnovationWe look forward to seeing your submissions!

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šŸļø At #LREC2026? @luisabentivogli is there too, come say hi and chat about the positions in person! #PhDPosition #MachineTranslation #NLProc #LREC2026 #FBK
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1. Was working on a task related to representation of low-resource Indic language (Punjabi) in NLP. I found two good datasets (parallel corpora) for it namely - Samanantar, BPCC - both curated by @ai4bharat (thx for cool dataset). #NLP #MachineTranslation #Punjabi #AI #CSE
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This study by Hiba El Oirghi et al., analyzes 4,335 translation pairs across Hausa, Igbo, Swahili, Yoruba, and Zulu using Wikipedia-based data, comparing raw machine translation (MT) outputs with human-edited (HE) versions. It finds a surprising pattern: in several cases, native annotators preferredĀ machine-generated translations over human-edited ones,Ā including 86% preference for MT in Zulu and 62% in Igbo. The findings point to a key tension in low-resource language workflows: human editing does not always guarantee improvement. In many cases, edits introduced issues such as missing information or unnecessary additions that reduced translation quality. This highlights the importance of designing translation systems that are sensitive to context, editorĀ proficiency, and language structure. For African language AI, theĀ real challengeĀ is not just generating translations, but ensuring that any refinement stepĀ preserves meaning, fluency, and cultural intent without distortion. Read full paper >> aclanthology.org/2026.africa… @MarineCarpuat @UMBaltimore @MasakhaneNLP @AfricaAIForum @AfricaAI_Summit @Africa_In_EN #EqualyzAI #AfricanNLP #MachineTranslation #Wikipedia #LowResourceLanguages #NLPResearch #AIforAfrica #DigitalInclusion #LanguageTechnology
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Replying to @alemgoshaar
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*Press Release* One AI model is no longer enough. MachineTranslation(dot)com is the only translation tool that runs 20 AI models and gives you the one most of them agree on. Today, MachineTranslation(dot)com expands its AI pool with two new large language models – Aya Expanse 32B by Cohere and MiniMax M2.7 - with @MTTomedes slator.ch/MTAdds2AIModels
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We just upgraded our model, and raised the bar for AI translation. Across 16 language pairs, blind testers commissioned by DeepL preferred our translations in 94% of head-to-head tests: • 100% of language pairs vs Google Translate • 100% vs OpenAI GPT 5.2 (high reasoning) • 100% vs Microsoft Translate • 88% vs Google Gemini 3.1 Pro (high thinking) • 81% vs Claude Opus 4.6 (high reasoning) Translation isn’t just about words. It’s about capturing human meaning precisely. That’s what we test for. That’s what DeepL delivers. Discover more about DeepL's translation quality: deepl.com/en/quality #BestAITranslations #DeepLQuality #MachineTranslation #LanguageAI
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Breaking new ground for African language technology, a recent paper fromĀ Findings of the Association for Computational Linguistics: EACL 2026Ā introduces a first‑of‑its‑kind translation resource for East Africa. ThisĀ study by Naome A. Etomi @netori3 . et al., presents AfriMMT‑EA, a large‑scale, multi‑domain machine translation dataset coveringĀ 53 East African languages, many with little or no prior digital presence. The authors also fine‑tune and release adapted models (ā€œSafari‑270Mā€ and ā€œSafari‑1Bā€) that outperform existing multilingual baselines across these languages, addressing historical under‑representation in NLP benchmarks and MT systems. This work reflects the urgent needĀ and real possibility, to build AI thatĀ serves all voices, not just a handful of high‑resource languages.Ā WeĀ share this mission: expanding linguistic inclusion in AI so that diverse African communities can trulyĀ benefitĀ from NLP technologies. Models likeĀ AfriMMT‑EA point toward a future where language barriers no longer limit access to information, services, or digital participation. Read the Full Paper > aclanthology.org/2026.findin… @EzemaKelechi @elishaondieki @alfredkondoro1 @nithrobinson @UMNews @UniversityofColorado @university3466 @blackswandata @TechHanyang @uonbi @sartify_co #EqualyzAI #AfricanLanguages #MachineTranslation #InclusiveAI #NLP
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A lot of people are terrified of a full machine replacement, but in this episode, Sébastien Bratières points out that viewing it as Human vs. Machine is completely absurd. Apple Podcast: buff.ly/SNPcDUb Spotify: buff.ly/ZzSwReZ #AI #LanguageTech #Localization #FutureOfWork #MachineTranslation #CareerGrowth #TechTrends #MultiLingualMedia
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They started with a language that barely exists in digital systems, built a corpus, aligned it with English and turned it into a working translation pipeline. That’s what this paper does, developing an English–Efik corpus and machine translation system, proving that even low-resource languages can power real AI applications when the right data foundation exists. The challenge was never just the model, it was always the data, and that’s the signal. For languages like Efik and many others across Africa,Ā progress doesn’t begin with scaling models. It begins with creating structured, usable datasets from scratch. This is exactly where the work is, at @equalyz_ai. From collecting voice data across local languages to enabling systems that can train them, the focus is the same: Build the data → unlock the models. Once the data exists, everything else becomes possible. Read the full paper here >> - openreview.net/attachment?id… @mb_awak @AJUniversity @Unical1975 @ml_collective @state95307 #EqualyzAI #LanguageAI #LowResourceLanguages #MachineTranslation #AfricanLanguages #DataInfrastructure
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Anyway, Google Translate has pulled quite a few stunts—it's hilariously abstract.#GoogleTranslateFails #TranslationGoneWrong #AbstractHumor #AILaughs #TechFails #MachineTranslation #FunnyMistakes #GrokVsGoogle @Google @gork
The mind of humanity
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