Few-shot Slot Tagging with Collapsed Dependency Transfer and Label-enhanced Task-adaptive Projection Network

Abstract

In this paper, we explore the slot tagging with only a few labeled support sentences (a.k.a. few-shot). Few-shot slot tagging faces a unique challenge compared to the other fewshot classification problems as it calls for modeling the dependencies between labels. But it is hard to apply previously learned label dependencies to an unseen domain, due to the discrepancy of label sets. To tackle this, we introduce a collapsed dependency transfer mechanism into the conditional random field (CRF) to transfer abstract label dependency patterns as transition scores. In the few-shot setting, the emission score of CRF can be calculated as a word's similarity to the representation of each label. To calculate such similarity, we propose a Label-enhanced Task-Adaptive Projection Network (L-TapNet) based on the stateof-the-art few-shot classification model -Tap-Net, by leveraging label name semantics in representing labels. Experimental results show that our model significantly outperforms the strongest few-shot learning baseline by 14.64 F1 scores in the one-shot setting. 1

Simple and Effective Few-Shot Named Entity Recognition with Structured Nearest Neighbor Learning

“…Prior work (Fritzler et al, 2019;Hou et al, 2020) on few-shot NER followed few-shot classification literature and adopted the episode evaluation methodology. Specifically, a NER system is evaluated with respect to multiple evaluation episodes.…”

“…Most meta-learning approaches (Snell et al, 2017;Hou et al, 2020) simulate the test time setup during training. Hence, these approaches sample multiple support sets and test sets from the training data and learn representations to minimize their corresponding few-shot loss on the source domain.…”

“…Additionally, STRUCT-SHOT discards training phase in CRF and only makes use of its Viterbi decoder during inference. In particular, similar to Hou et al (2020), we utilize a transition matrix that captures transition probabilities between three abstract NER tags: O, I, I-Other 4 . For instance, p(O|I) and p(I|O) correspond to the transition probabilities between an entity tag and O, whereas p(I|I) and p(I-Other|I) correspond to the probabilities of transitioning from an entity tag to itself and to a different entity tag respectively.…”

“…In the context of NER, these fewshot classification methods can enable rapid building of NER systems for a new domain by labeling only a few examples per entity class. Several previous studies (Fritzler et al, 2019;Hou et al, 2020) propose using prototypical networks (Snell et al, 2017), a popular few-shot classification algorithm, to address the few-shot NER problem. However, these approaches only achieve 10 ∼ 30% F1 scores on average, when transferring knowledge between different NER datasets with one or five shot examples, warranting more effective methods for the problem.…”

“…We test our systems on both identifying new types of entities in the source domain as well as identifying new types of entities in various target domains in one-shot and five-shot settings. In addition to the previous evaluation setup followed by Hou et al (2020), we propose a more standard and reproducible evaluation setup for few-shot NER by using standard test sets and development sets from benchmark datasets of several domains. In particular, we sample support sets from the standard development set and evaluate our models on the standard test set.…”