An Enhanced Span-based Decomposition Method for Few-Shot Sequence Labeling
Few-Shot Sequence Labeling (FSSL) is a canonical paradigm for the tagging models, e.g., named entity recognition and slot filling, to generalize on an emerging, resource-scarce domain. Recently, the metric-based meta-learning framework has been recognized as a promising approach for FSSL. However, most prior works assign a label to each token based on the token-level similarities, which ignores the integrality of named entities or slots. To this end, in this paper, we propose ESD, an Enhanced Span-based Decomposition method for FSSL. ESD formulates FSSL as a span-level matching problem between test query and supporting instances. Specifically, ESD decomposes the span matching problem into a series of span-level procedures, mainly including enhanced span representation, class prototype aggregation and span conflicts resolution. Extensive experiments show that ESD achieves the new state-of-the-art results on two popular FSSL benchmarks, FewNERD and SNIPS, and is proven to be more robust in the nested and noisy tagging scenarios. Our code is available at https://github.com/Wangpeiyi9979/ESD.
Code (1)
Tasks
Few-shot NERMeta-Learningnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)slot-fillingSlot FillingMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Decomposed Meta-Learning for Few-Shot Sequence Labeling
Few-shot sequence labeling is a general problem formulation for many natural language understanding tasks in data-scarcity scenarios, which require models to generalize to new types via only a few labeled examples. Recen…
Entity TypingEvent DetectionFew-shot NERMeta-Learning+8Decomposed Meta-Learning for Few-Shot Named Entity Recognition
Few-shot named entity recognition (NER) systems aim at recognizing novel-class named entities based on only a few labeled examples. In this paper, we present a decomposed meta-learning approach which addresses the proble…
Entity TypingFew-shot NERMeta-LearningNamed Entity Recognition+1Unifying Token and Span Level Supervisions for Few-Shot Sequence Labeling
Few-shot sequence labeling aims to identify novel classes based on only a few labeled samples. Existing methods solve the data scarcity problem mainly by designing token-level or span-level labeling models based on metri…
Metric LearningToPro: Token-Level Prompt Decomposition for Cross-Lingual Sequence Labeling Tasks
Prompt-based methods have been successfully applied to multilingual pretrained language models for zero-shot cross-lingual understanding. However, most previous studies primarily focused on sentence-level classification …
BenchmarkingCross-Lingual TransferIn-Context Learningnamed-entity-recognition+8Detecting Local Insights from Global Labels: Supervised & Zero-Shot Sequence Labeling via a Convolutional Decomposition
We propose a new, more actionable view of neural network interpretability and data analysis by leveraging the remarkable matching effectiveness of representations derived from deep networks, guided by an approach for cla…
Grammatical Error DetectionText GenerationWord Embeddings