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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 2 of 2 1 Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Ramakanth; Bansal, Mohit. - : Underline Science Inc., 2021 BASE Show details 2 Continual Few-Shot Learning for Text Classification ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; ., Ramakanth; Bansal, Mohit; Stoyanov, Veselin. - : Underline Science Inc., 2021 Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.460/ Abstract: 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 ... Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Inference; Natural Language Processing URL: https://dx.doi.org/10.48448/bn6k-b147 https://underline.io/lecture/37753-continual-few-shot-learning-for-text-classification BASE Hide details

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