F act M ix: Using a Few Labeled In-domain Examples to Generalize to Cross-domain Named Entity Recognition (2022)

Abstract: Few-shot Named Entity Recognition (NER) is imperative for entity tagging in limited resource domains and thus received proper attention in recent years. Existing approaches for few-shot NER are evaluated mainly under in-domain settings. In contrast, little is known about how these inherently faithful models perform in cross-domain NER using a few labeled in-domain examples. This paper proposes a two-step rationale-centric data augmentation method to improve the model’s generalization ability. Results on several datasets show that our model-agnostic method significantly improves the performance of cross-domain NER tasks compared to previous state-of-the-art methods compared to the counterfactual data augmentation and prompt-tuning methods.

## introduction named entity recognition (ner) is a subtask of natural language processing, which detects the mentions of named entities in input text, such as location, organization, and person @xcite @xcite . it has attracted research from academia and industry due to its broadened usage in customer services and document parsing as a core task in natural language understanding @xcite @xcite . however, training data for ner is available only for limited domains. it has been shown that such labeled data introduces challenges for a model to generalize to new domains @xcite @xcite . to address this problem, a line of research considers how to allow a model to effectively learn from a few labeled examples in a new target domain @xcite @xcite input: germany imported 47,600 sheep from britain last year nearly half of total imports. ## settings we investigate the effectiveness of factmix using different methods under several settings. we first introduce task settings in section 3.1, then show the standard fine-tuning method and prompt-based method in section 3.2 and section 3.3, respectively. ## method factmix automatically generates semi-fact examples for both standard fine-tuning and prompttuning. the pipeline of our approach is shown in figure 3 and is made up of three components: (1) entity-level semi-fact generator; ## experiments as shown in