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Register a new userAn Uncertainty-Aware Encoder for Aspect Detection
تشفير عدم اليقين على الكشف عن الجانب
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كشف الجانب هو مهمة أساسية في التعدين في الرأي.تستخدم الأشغال السابقة كلمات البذور إما كعظمون من نماذج الموضوع، كمراسين لتوجيه تعلم الجوانب، أو كميزات من صفوف الأنفاق.تقدم هذه الورقة طريقة رواية متشرفة ضعيفة لاستغلال كلمات البذور للكشف عن الجانب بناء على بنية تشفير.شرائح خرائط التشفير والجوانب في مساحة تضمين منخفضة الأبعاد.الهدف هو تقريب التشابه بين القطاعات والجوانب في مساحة التضمين وإشطاه الحقيقة الأرضية الناتجة عن كلمات البذور.ويقترح وظيفة موضوعية للقبض على عدم اليقين في التشابه الأساسي للحقيقة.الطريقة التي تتفوقها على العمل السابق على العديد من المعايير في المجالات المختلفة.
Aspect detection is a fundamental task in opinion mining. Previous works use seed words either as priors of topic models, as anchors to guide the learning of aspects, or as features of aspect classifiers. This paper presents a novel weakly-supervised method to exploit seed words for aspect detection based on an encoder architecture. The encoder maps segments and aspects into a low-dimensional embedding space. The goal is approximating similarity between segments and aspects in the embedding space and their ground-truth similarity generated from seed words. An objective function is proposed to capture the uncertainty of ground-truth similarity. Our method outperforms previous works on several benchmarks in various domains.
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Recent advances in NLP systems, notably the pretraining-and-finetuning paradigm, have achieved great success in predictive accuracy. However, these systems are usually not well calibrated for uncertainty out-of-the-box. Many recalibration methods hav
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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rk presents a rigorous study of this problem, focusing on an approach that incorporates model uncertainty into the collaborative process. First, we introduce principled metrics to describe the performance of the collaborative system under capacity constraints on the human moderator, quantifying how efficiently the combined system utilizes human decisions. Using these metrics, we conduct a large benchmark study evaluating the performance of state-of-the-art uncertainty models under different collaborative review strategies. We find that an uncertainty-based strategy consistently outperforms the widely used strategy based on toxicity scores, and moreover that the choice of review strategy drastically changes the overall system performance. Our results demonstrate the importance of rigorous metrics for understanding and developing effective moderator-model systems for content moderation, as well as the utility of uncertainty estimation in this domain.
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