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Register a new userContinual Few-Shot Learning for Text Classification
عدد قليل من التعلم قليلا لتصنيف النص
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تعتمد معالجة اللغة الطبيعية (NLP) بشكل متزايد على الأنظمة العامة المناسبة التي تحتاج إلى التعامل مع العديد من الظواهر اللغوية المختلفة والفروق الدقيقة. على سبيل المثال، يتعين على نظام الاستدلال باللغة الطبيعية (NLI) أن يتعرف على المعنويات، والتعامل مع الأرقام، وإجراء حلول، وما إلى ذلك. لا تزال حلولنا للمشاكل المعقدة بعيدة عن الكمال، لذلك من المهم إنشاء أنظمة يمكن أن تتعلم تصحيح الأخطاء بسرعة، تدريجيا، ومع القليل من البيانات التدريبية. في هذا العمل، نقترح مهمة التعلم القليلة المستمرة (CFL)، حيث يتم الطعن للنظام بظاهرة صعبة وطلب منهم أن يتعلموا تصحيح الأخطاء مع أمثلة تدريبية فقط (10 إلى 15). تحقيقا لهذه الغاية، نقوم أولا بإنشاء معايير بناء على البيانات المشروحة مسبقا: DetaSets NLI (Anli and Snli) ومجموعات بيانات تحليل المشاعر (IMDB). بعد ذلك، نقدم خطوط أساس مختلفة من النماذج المتنوعة (على سبيل المثال، أخطاقات علم الذاكرة والشبكات النموذجية) ومقارنتها في التعلم القليل من الطلقات والكم من إعدادات التعلم القليلة المستمرة. إن مساهماتنا هي في إنشاء بروتوكول جناح وتقييم معيار لاستمرار التعلم القليل من الرصاص حول مهام تصنيف النص، وعمل العديد من الملاحظات المثيرة للاهتمام حول سلوك الأساليب القائمة على التشابه. نأمل أن يعمل عملنا كنقطة انطلاق مفيدة للعمل في المستقبل على هذا الموضوع الهام.
Natural Language Processing (NLP) is increasingly relying on general end-to-end systems that need to handle many different linguistic phenomena and nuances. For example, a Natural Language Inference (NLI) system has to recognize sentiment, handle numbers, perform coreference, etc. Our solutions to complex problems are still far from perfect, so it is important to create systems that can learn to correct mistakes quickly, incrementally, and with little training data. In this work, we propose a continual few-shot learning (CFL) task, in which a system is challenged with a difficult phenomenon and asked to learn to correct mistakes with only a few (10 to 15) training examples. To this end, we first create benchmarks based on previously annotated data: two NLI (ANLI and SNLI) and one sentiment analysis (IMDB) datasets. Next, we present various baselines from diverse paradigms (e.g., memory-aware synapses and Prototypical networks) and compare them on few-shot learning and continual few-shot learning setups. Our contributions are in creating a benchmark suite and evaluation protocol for continual few-shot learning on the text classification tasks, and making several interesting observations on the behavior of similarity-based methods. We hope that our work serves as a useful starting point for future work on this important topic.
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This paper investigates the effectiveness of pre-training for few-shot intent classification. While existing paradigms commonly further pre-train language models such as BERT on a vast amount of unlabeled corpus, we find it highly effective and effic
ient to simply fine-tune BERT with a small set of labeled utterances from public datasets. Specifically, fine-tuning BERT with roughly 1,000 labeled data yields a pre-trained model -- IntentBERT, which can easily surpass the performance of existing pre-trained models for few-shot intent classification on novel domains with very different semantics. The high effectiveness of IntentBERT confirms the feasibility and practicality of few-shot intent detection, and its high generalization ability across different domains suggests that intent classification tasks may share a similar underlying structure, which can be efficiently learned from a small set of labeled data. The source code can be found at https://github.com/hdzhang-code/IntentBERT.
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g new intents and slots, data scarcity issue often occurs when implementing IC and SF. However, few IC/SF models perform well when the number of training samples per class is quite small. In this paper, we propose a novel explicit-joint and supervised-contrastive learning framework for few-shot intent classification and slot filling. Its highlights are as follows. (i) The model extracts intent and slot representations via bidirectional interactions, and extends prototypical network to achieve explicit-joint learning, which guarantees that IC and SF tasks can mutually reinforce each other. (ii) The model integrates with supervised contrastive learning, which ensures that samples from same class are pulled together and samples from different classes are pushed apart. In addition, the model follows a not common but practical way to construct the episode, which gets rid of the traditional setting with fixed way and shot, and allows for unbalanced datasets. Extensive experiments on three public datasets show that our model can achieve promising performance.
Meta learning aims to optimize the model's capability to generalize to new tasks and domains. Lacking a data-efficient way to create meta training tasks has prevented the application of meta-learning to the real-world few shot learning scenarios. Rec
ent studies have proposed unsupervised approaches to create meta-training tasks from unlabeled data for free, e.g., the SMLMT method (Bansal et al., 2020a) constructs unsupervised multi-class classification tasks from the unlabeled text by randomly masking words in the sentence and let the meta learner choose which word to fill in the blank. This study proposes a semi-supervised meta-learning approach that incorporates both the representation power of large pre-trained language models and the generalization capability of prototypical networks enhanced by SMLMT. The semi-supervised meta training approach avoids overfitting prototypical networks on a small number of labeled training examples and quickly learns cross-domain task-specific representation only from a few supporting examples. By incorporating SMLMT with prototypical networks, the meta learner generalizes better to unseen domains and gains higher accuracy on out-of-scope examples without the heavy lifting of pre-training. We observe significant improvement in few-shot generalization after training only a few epochs on the intent classification tasks evaluated in a multi-domain setting.
The ability to continuously expand knowledge over time and utilize it to rapidly generalize to new tasks is a key feature of human linguistic intelligence. Existing models that pursue rapid generalization to new tasks (e.g., few-shot learning methods
), however, are mostly trained in a single shot on fixed datasets, unable to dynamically expand their knowledge; while continual learning algorithms are not specifically designed for rapid generalization. We present a new learning setup, Continual Learning of Few-Shot Learners (CLIF), to address challenges of both learning settings in a unified setup. CLIF assumes a model learns from a sequence of diverse NLP tasks arriving sequentially, accumulating knowledge for improved generalization to new tasks, while also retaining performance on the tasks learned earlier. We examine how the generalization ability is affected in the continual learning setup, evaluate a number of continual learning algorithms, and propose a novel regularized adapter generation approach. We find that catastrophic forgetting affects generalization ability to a lesser degree than performance on seen tasks; while continual learning algorithms can still bring considerable benefit to the generalization ability.
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