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نظرة عامة على ورشة العمل الثانية حول معالجة المستندات العلمية
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مع سرعة البحوث المتزايدة بشكل مفيد والحجم المرتفع للاتصال العلمي، يواجه العلماء مهمة شاقة. ليس فقط يجب عليهم مواكبة الأدبيات المتزايدة في مجالاتهم ذات الصلة، كما يحتاج العلماء بشكل متزايد إلى إعادة صياغة العلوم الزائفة والإضاءة. تحفز هذه الاحتياجات تركيزا متزايدا على الأساليب الحسابية لتعزيز البحث والتلخيص وتحليل الوثائق العلمية. ومع ذلك، فإن خيوط البحث المختلفة بشأن معالجة الوثائق العلمية تظل مجزأة. للوصول إلى مجتمع NLP و AI / ML الأوسع، بجميع الجهود الموزعة في هذا المجال، وتمكين الوصول المشترك إلى البحث المنشور، عقدنا ورشة العمل الثانية بشأن معالجة الوثائق العلمية (SDP) في Naacl 2021 كحدث افتراضي (HTTPS: //sdproc.org/2021/). تتألف ورشة عمل SDP من مسار أبحاث وثلاثة محادثات مدعوة وثلاث مهام مشتركة (LongsUMM 2021، SEXIVER، و 3C). تم توجيه البرنامج نحو تطبيق NLP واسترجاع المعلومات والتعدين البيانات للمستندات العلمية، مع التركيز على تحديد وحلول الحلول لفتح التحديات.
With the ever-increasing pace of research and high volume of scholarly communication, scholars face a daunting task. Not only must they keep up with the growing literature in their own and related fields, scholars increasingly also need to rebut pseudo-science and disinformation. These needs have motivated an increasing focus on computational methods for enhancing search, summarization, and analysis of scholarly documents. However, the various strands of research on scholarly document processing remain fragmented. To reach out to the broader NLP and AI/ML community, pool distributed efforts in this area, and enable shared access to published research, we held the 2nd Workshop on Scholarly Document Processing (SDP) at NAACL 2021 as a virtual event (https://sdproc.org/2021/). The SDP workshop consisted of a research track, three invited talks, and three Shared Tasks (LongSumm 2021, SCIVER, and 3C). The program was geared towards the application of NLP, information retrieval, and data mining for scholarly documents, with an emphasis on identifying and providing solutions to open challenges.
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This paper presents the results of the shared tasks from the 8th workshop on Asian translation (WAT2021). For the WAT2021, 28 teams participated in the shared tasks and 24 teams submitted their translation results for the human evaluation. We also ac
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In this paper, we introduce the Eval4NLP-2021 shared task on explainable quality estimation. Given a source-translation pair, this shared task requires not only to provide a sentence-level score indicating the overall quality of the translation, but
also to explain this score by identifying the words that negatively impact translation quality. We present the data, annotation guidelines and evaluation setup of the shared task, describe the six participating systems, and analyze the results. To the best of our knowledge, this is the first shared task on explainable NLP evaluation metrics. Datasets and results are available at https://github.com/eval4nlp/SharedTask2021.
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e been proposed in the literature for quantifying predictive uncertainty and calibrating model outputs, with varying degrees of complexity. In this work, we present a systematic study of a few of these methods. Focusing on the text classification task and finetuned large pretrained language models, we first show that many of the finetuned models are not well calibrated out-of-the-box, especially when the data come from out-of-domain settings. Next, we compare the effectiveness of a few widely-used recalibration methods (such as ensembles, temperature scaling). Then, we empirically illustrate a connection between distillation and calibration. We view distillation as a regularization term encouraging the student model to output uncertainties that match those of a teacher model. With this insight, we develop simple recalibration methods based on distillation with no additional inference-time cost. We show on the GLUE benchmark that our simple methods can achieve competitive out-of-domain (OOD) calibration performance w.r.t. more expensive approaches. Finally, we include ablations to understand the usefulness of components of our proposed method and examine the transferability of calibration via distillation.
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