Massively Multilingual NLU 2022: Call for papers and shared-task entries

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{"value":"Let’s scale natural-language-understanding technology to every language on Earth!\n\n![下载 4.jpg](https://dev-media.amazoncloud.cn/40eab197044942c8accc4da5167351b3_%E4%B8%8B%E8%BD%BD%20%284%29.jpg)\n\nThe MASSIVE dataset, which is the basis for the MMNLU-22 shared task, is a parallel dataset spanning 51 languages.\n\n++[Massively Multilingual NLU 2022 (MMNLU-22)](https://mmnlu-22.github.io/)++ is an upcoming workshop collocated with the Empirical Methods in Natural Language Processing (++[EMNLP](https://www.amazon.science/conferences-and-events/emnlp-2022)++) conference. The workshop, which centers on natural-language-processing (NLP) technologies in the regime of 50-plus languages, will feature talks by prominent NLP researchers, paper presentations, and a poster session. The workshop will take place on December 7, 2022, both in Abu Dhabi and online. Amazon is cohosting the workshop with partners in academia.\n\nThe ++[call for papers](https://mmnlu-22.github.io/Calls/)++ for MMNLU 2022 has been issued. The deadline for OpenReview direct submissions is September 7, and the deadline for ACL Rolling Review (ARR) commitments is October 2. Any work related to the advancement of multilingual natural-language-understaning (NLU) models and systems will be considered, including modeling results on multilingual benchmarks, exploration and visualization of multilingual representations, data reduction and augmentation techniques (including those that use machine translation), tokenization analyses, distillation and quantization work, and more. We also welcome opinion papers, reports on negative results, and extended abstracts.\n\nBesides paper submissions, there is a shared-task competition using the ++[MASSIVE](https://www.amazon.science/blog/amazon-releases-51-language-dataset-for-language-understanding)++ dataset. MASSIVE is a parallel NLU dataset composed of one million labeled utterances spanning 51 languages. More details can be found in the ++[paper preprint](https://arxiv.org/abs/2204.08582)++ and in the ++[Github repository.](https://github.com/alexa/massive)++ Training and validation sets were released on April 20, and the unlabeled inputs for the Massively Multilingual NLU 2022 shared task will be released on July 25. Participants will have until Aug 8 to submit their models’ predictions to ++[eval.ai](https://eval.ai/web/challenges/challenge-page/1697/overview)++. Winners will be given an opportunity to present their models and results at the workshop.\n\nWe look forward to seeing your papers and shared-task submissions.\n\n#### **Important dates:**\n- **July 25**: Shared-task test set released\n- **Aug. 8**: Deadline for shared-task submissions\n- **Sep. 7**: Deadline for direct submissions of workshop papers\n- **Oct. 2**: Deadline for ACL Rolling Review (ARR) commitments\n- **Dec. 7**: MMNLU Workshop\n\nABOUT THE AUTHOR\n#### **[Jack FitzGerald](https://www.amazon.science/author/jack-g-m-fitzgerald)**\nJack G. M. FitzGerald is a senior applied scientist in Alexa AI's Natural Understanding group.\n#### **[Kay Rottmann](https://www.amazon.science/author/kay-rottmann)**\nKay Rottmann is a senior applied scientist with the Alexa AI organization.\n","render":"<p>Let’s scale natural-language-understanding technology to every language on Earth!</p>\n<p><img src=\"https://dev-media.amazoncloud.cn/40eab197044942c8accc4da5167351b3_%E4%B8%8B%E8%BD%BD%20%284%29.jpg\" alt=\"下载 4.jpg\" /></p>\n<p>The MASSIVE dataset, which is the basis for the MMNLU-22 shared task, is a parallel dataset spanning 51 languages.</p>\n<p><ins><a href=\"https://mmnlu-22.github.io/\" target=\"_blank\">Massively Multilingual NLU 2022 (MMNLU-22)</a></ins> is an upcoming workshop collocated with the Empirical Methods in Natural Language Processing (<ins><a href=\"https://www.amazon.science/conferences-and-events/emnlp-2022\" target=\"_blank\">EMNLP</a></ins>) conference. The workshop, which centers on natural-language-processing (NLP) technologies in the regime of 50-plus languages, will feature talks by prominent NLP researchers, paper presentations, and a poster session. The workshop will take place on December 7, 2022, both in Abu Dhabi and online. Amazon is cohosting the workshop with partners in academia.</p>\n<p>The <ins><a href=\"https://mmnlu-22.github.io/Calls/\" target=\"_blank\">call for papers</a></ins> for MMNLU 2022 has been issued. The deadline for OpenReview direct submissions is September 7, and the deadline for ACL Rolling Review (ARR) commitments is October 2. Any work related to the advancement of multilingual natural-language-understaning (NLU) models and systems will be considered, including modeling results on multilingual benchmarks, exploration and visualization of multilingual representations, data reduction and augmentation techniques (including those that use machine translation), tokenization analyses, distillation and quantization work, and more. We also welcome opinion papers, reports on negative results, and extended abstracts.</p>\n<p>Besides paper submissions, there is a shared-task competition using the <ins><a href=\"https://www.amazon.science/blog/amazon-releases-51-language-dataset-for-language-understanding\" target=\"_blank\">MASSIVE</a></ins> dataset. MASSIVE is a parallel NLU dataset composed of one million labeled utterances spanning 51 languages. More details can be found in the <ins><a href=\"https://arxiv.org/abs/2204.08582\" target=\"_blank\">paper preprint</a></ins> and in the <ins><a href=\"https://github.com/alexa/massive\" target=\"_blank\">Github repository.</a></ins> Training and validation sets were released on April 20, and the unlabeled inputs for the Massively Multilingual NLU 2022 shared task will be released on July 25. Participants will have until Aug 8 to submit their models’ predictions to <ins><a href=\"https://eval.ai/web/challenges/challenge-page/1697/overview\" target=\"_blank\">eval.ai</a></ins>. Winners will be given an opportunity to present their models and results at the workshop.</p>\n<p>We look forward to seeing your papers and shared-task submissions.</p>\n<h4><a id=\"Important_dates_14\"></a><strong>Important dates:</strong></h4>\n<ul>\n<li><strong>July 25</strong>: Shared-task test set released</li>\n<li><strong>Aug. 8</strong>: Deadline for shared-task submissions</li>\n<li><strong>Sep. 7</strong>: Deadline for direct submissions of workshop papers</li>\n<li><strong>Oct. 2</strong>: Deadline for ACL Rolling Review (ARR) commitments</li>\n<li><strong>Dec. 7</strong>: MMNLU Workshop</li>\n</ul>\n<p>ABOUT THE AUTHOR</p>\n<h4><a id=\"Jack_FitzGeraldhttpswwwamazonscienceauthorjackgmfitzgerald_22\"></a><strong><a href=\"https://www.amazon.science/author/jack-g-m-fitzgerald\" target=\"_blank\">Jack FitzGerald</a></strong></h4>\n<p>Jack G. M. FitzGerald is a senior applied scientist in Alexa AI’s Natural Understanding group.</p>\n<h4><a id=\"Kay_Rottmannhttpswwwamazonscienceauthorkayrottmann_24\"></a><strong><a href=\"https://www.amazon.science/author/kay-rottmann\" target=\"_blank\">Kay Rottmann</a></strong></h4>\n<p>Kay Rottmann is a senior applied scientist with the Alexa AI organization.</p>\n"}
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