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1
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging ...
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2
ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoning ...
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3
EmailSum: Abstractive Email Thread Summarization ...
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4
Inducing Transformer’s Compositional Generalization Ability via Auxiliary Sequence Prediction Tasks ...
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5
I like fish, especially dolphins: Addressing Contradictions in Dialogue Modeling ...
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6
InfoSurgeon: Cross-Media Fine-grained Information Consistency Checking for Fake News Detection ...
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7
Continuous Language Generative Flow ...
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8
ChrEnTranslate: Cherokee-English Machine Translation Demo with Quality Estimation and Corrective Feedback ...
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9
Integrating Visuospatial, Linguistic, and Commonsense Structure into Story Visualization ...
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10
Summary-Source Proposition-level Alignment: Task, Datasets and Supervised Baseline ...
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11
Continual Few-Shot Learning for Text Classification ...
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
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12
Finding a Balanced Degree of Automation for Summary Evaluation ...
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13
Analysis of Tree-Structured Architectures for Code Generation ...
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