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Register a new userActive Learning by Acquiring Contrastive Examples
التعلم النشط عن طريق الحصول على أمثلة مضادفة
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تستخدم وظائف الاستحواذ المشتركة للتعلم النشط إما أخذ عينات من عدم اليقين أو التنوع، تهدف إلى تحديد نقاط بيانات صعبة ومتنوعة من مجموعة البيانات غير المسبقة، على التوالي. في هذا العمل، استمتع بأفضل ما في العالمين، نقترح وظيفة الاستحواذ المفتوحة لاختار الأمثلة المتعاقبة تماما، أي نقاط بيانات مشابهة في مساحة ميزة النموذج وحتى الآن مخرجات النموذج احتمالية تنبؤية مختلفة. قارنا نهجنا، CAL (التعلم النشط الصنع)، مع مجموعة متنوعة من وظائف الاستحواذ في أربعة مهام فهم اللغة الطبيعية وسبع مجموعات البيانات. تظهر تجاربنا أن CUR يؤدي Cal بشكل أفضل أو متساو من أفضل خط الأساس الأدائي عبر جميع المهام، على كل من البيانات داخل المجال والخروج. نقوم أيضا بإجراء دراسة واسعة النمذجة لطرأتنا، ونحن نتحلل جميع مجموعات البيانات المكتسبة بنشاط والتي توضح أن كال يحصل على مفاضلة أفضل بين عدم اليقين والتنوع مقارنة باستراتيجيات أخرى.
Common acquisition functions for active learning use either uncertainty or diversity sampling, aiming to select difficult and diverse data points from the pool of unlabeled data, respectively. In this work, leveraging the best of both worlds, we propose an acquisition function that opts for selecting contrastive examples, i.e. data points that are similar in the model feature space and yet the model outputs maximally different predictive likelihoods. We compare our approach, CAL (Contrastive Active Learning), with a diverse set of acquisition functions in four natural language understanding tasks and seven datasets. Our experiments show that CAL performs consistently better or equal than the best performing baseline across all tasks, on both in-domain and out-of-domain data. We also conduct an extensive ablation study of our method and we further analyze all actively acquired datasets showing that CAL achieves a better trade-off between uncertainty and diversity compared to other strategies.
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High-quality arguments are an essential part of decision-making. Automatically predicting the quality of an argument is a complex task that recently got much attention in argument mining. However, the annotation effort for this task is exceptionally
high. Therefore, we test uncertainty-based active learning (AL) methods on two popular argument-strength data sets to estimate whether sample-efficient learning can be enabled. Our extensive empirical evaluation shows that uncertainty-based acquisition functions can not surpass the accuracy reached with the random acquisition on these data sets.
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Neural machine translation (NMT) is sensitive to domain shift. In this paper, we address this problem in an active learning setting where we can spend a given budget on translating in-domain data, and gradually fine-tune a pre-trained out-of-domain N
MT model on the newly translated data. Existing active learning methods for NMT usually select sentences based on uncertainty scores, but these methods require costly translation of full sentences even when only one or two key phrases within the sentence are informative. To address this limitation, we re-examine previous work from the phrase-based machine translation (PBMT) era that selected not full sentences, but rather individual phrases. However, while incorporating these phrases into PBMT systems was relatively simple, it is less trivial for NMT systems, which need to be trained on full sequences to capture larger structural properties of sentences unique to the new domain. To overcome these hurdles, we propose to select both full sentences and individual phrases from unlabelled data in the new domain for routing to human translators. In a German-English translation task, our active learning approach achieves consistent improvements over uncertainty-based sentence selection methods, improving up to 1.2 BLEU score over strong active learning baselines.
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