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  1. proslijedio/la je Tweet
    3. velj

    Break it down! Introducing the "Break" benchmark for testing the ability of models to break down a question into required steps for computing the answer. Accepted for . Learn more about Break in this post by Tomer Wolfson on the AI2 Blog:

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  2. 3. velj

    We define a mean. rep. (QDMR) that decomposes questions to a sequence of steps that can be executed against any context (image, text, DB), crowdsource >80K question-QDMR pairs using questions from 10 existing datasets, show usefulness for RC and release a QDMR parser. Enjoy! 2/2

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  3. 3. velj

    New TACL paper involving a lot of hard work from my twitter-less student Tomer, along with great collab. at AI2 and TAU. Paper/website at 1/2

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  4. proslijedio/la je Tweet
    1. sij

    We present our new year special: “oLMpics - On what Language Model pre-training captures״, , Exploring what symbolic reasoning skills are learned from an LM objective. We introduce 8 oLMpic games and controls for disentangling pre-training from fine-tuning.

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  5. proslijedio/la je Tweet
    18. pro 2019.

    רשמו בהיומנים - אדבר על פרישוּת (interpretability) רשתות נוירונים בסמינר לימוד המכונה של אוניברסיטת ת"א שבועיים מהיום, 1/1 למניינם, ב-13:00 ותודה ל- על ההזמנה!

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  6. proslijedio/la je Tweet
    22. stu 2019.

    תודה ❤ לעשרות התומכות והתומכים שבחרו להגדיל את התמיכה החודשית שלהם ב"שקוף" בחודש האחרון! קצת נתונים, נכון להיום: + תומכות ותומכים קבועים: 2,811 + סכום תמיכה חודשי מצטבר: 92,905 ש"ח + השקיפות הגדולה בישראל: ✅ הצטרפו עכשיו לגוף התקשורת שעובד רק בשבילכם:

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  7. 5. stu 2019.

    of course

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  8. 5. stu 2019.

    "Don't paraphrase, detect"! presenting an analysis of dist. shift issues in collecting sem. parsing data with 'overnight' and propose a new method that iteratively trains a paraphrase model. Session 8B: Sentence-level Semantics II, 17:24,

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  9. 5. stu 2019.
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  10. proslijedio/la je Tweet
    4. stu 2019.

    , , . Are We Modeling the Task or the Annotator? An Investigation of Annotator Bias in Natural Language Understanding Datasets. (Video presentation)

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  11. 4. stu 2019.

    "Are we modeling the task or the annotator? " today (Tue) at 1530 will present our work on annotator bias in Session3A machine learning ii. Come check it out!

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  12. proslijedio/la je Tweet
    3. stu 2019.

    congrats also to the AI2/TAU/Utah team for the honorable mention of their work on challenges in active learning.

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  13. proslijedio/la je Tweet
    3. stu 2019.

    yay! and 's work just won best paper! we look at what happens to word embeddings in languages in which inanimate nouns have gender (spoiler: they cluster into gender groups) and attempt to control for that, improving the embeddings quality.

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  14. 3. stu 2019.

    Congrats to Omri and all our other great collaborators!

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  15. proslijedio/la je Tweet
    3. stu 2019.

    Big congrats to the winners of the paper awards: 1) Best Paper: by Gonen, Kementchedjhieva, Goldberg; 2) Best Paper for Research Inspired by Human Language Learning: by Rabinovich, Watson, Beekhuizen, Stevenson.

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  16. proslijedio/la je Tweet
    3. stu 2019.

    We also had 2 honorable mentions for each best paper category: Honorable mentions for best paper: 1) by Koshorek, Stanovsky, Zhou, Srikumar, Berant 2) by Jumelet, Zuidema, Hupkes

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  17. proslijedio/la je Tweet
    3. stu 2019.

    Best paper at : Gonen et al. on debiasing word embeddings. Congrats! @emnlp2019

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  18. proslijedio/la je Tweet
    3. stu 2019.
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  19. proslijedio/la je Tweet
    3. stu 2019.

    talking about the MRQA 2019 shared task baseline model!

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  20. proslijedio/la je Tweet
    3. stu 2019.

    kicking off MRQA 2019! Please come by to room 201-BC at EMNLP for some discussion on machine reading!

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