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    Machine Translation Not Solved, Researcher States

    Academic Mohit Iyyer retweets claim that machine translation remains unsolved.

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    5 Sources, 26d ago, first seen 26d ago

    TLDR

    Associate professor Mohit Iyyer of the University of Maryland retweeted a post by @zouharvi. The message asserts that machine translation is not solved and will take a while to finish. It includes a link to an arXiv paper. Iyyer focuses on NLP and LLMs in his academic work. The packet shows only this retweet and the quoted statement in an AI topic discussion on social media. No paper details or further corroboration appear in the supplied lines.

    Combined views

    6.9K

    5 Sources, first seen 26d ago

    Combined views

    6.9K

    5 Sources, first seen 26d ago

    86 likes
    86 likes
    3 comments
    25 saves
    104 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

    3 comments
    25 saves
    104 reposts

    Sentiment

    Positive——Negative

    Summary

    Not enough discussion yet.

    No sentiment analysis available yet.

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    5 Sources

    @MohitIyyerRT @zouharvi: Machine translation is not solved and it will take a while for it to be done https://arxiv.org/abs/2609.04173
    @jessyjliRT @zouharvi: Machine translation is not solved and it will take a while for it to be done https://arxiv.org/abs/2609.04173
    @fredahshiRT @zouharvi: Machine translation is not solved and it will take a while for it to be done https://arxiv.org/abs/2609.04173
    @askalphaxiv“Last Translation Benchmark” This paper builds 3,456 hard translation examples across 109 languages, each with explicit verification rules describing the specific error to avoid, such as wrong word sense, lost wordplay, gender resolution, cultural meaning, tone, or multimodal context. So instead of vague quality scores, it checks whether translations avoid concrete failures. When LLMs are given the human-written rules, verifier pass rate jumps from 7.2% to 89.8%, suggesting the bottleneck is often identifying the right translation constraint rather than satisfying it. https://www.alphaxiv.org/abs/2609.04173
    @LChoshenRT @askalphaxiv: “Last Translation Benchmark” This paper builds 3,456 hard translation examples across 109 languages, each with explicit v…

    5 Sources

    @MohitIyyerRT @zouharvi: Machine translation is not solved and it will take a while for it to be done https://arxiv.org/abs/2609.04173
    @jessyjliRT @zouharvi: Machine translation is not solved and it will take a while for it to be done https://arxiv.org/abs/2609.04173
    @fredahshiRT @zouharvi: Machine translation is not solved and it will take a while for it to be done https://arxiv.org/abs/2609.04173
    @askalphaxiv“Last Translation Benchmark” This paper builds 3,456 hard translation examples across 109 languages, each with explicit verification rules describing the specific error to avoid, such as wrong word sense, lost wordplay, gender resolution, cultural meaning, tone, or multimodal context. So instead of vague quality scores, it checks whether translations avoid concrete failures. When LLMs are given the human-written rules, verifier pass rate jumps from 7.2% to 89.8%, suggesting the bottleneck is often identifying the right translation constraint rather than satisfying it. https://www.alphaxiv.org/abs/2609.04173
    @LChoshenRT @askalphaxiv: “Last Translation Benchmark” This paper builds 3,456 hard translation examples across 109 languages, each with explicit v…