We study the problem of few-shot Fine-grained Entity Typing (FET),
where only a few annotated entity mentions with contexts are given
for each entity type. Recently, prompt-based tuning has demonstrated
superior performance to standard fine-tuning in few-shot
scenarios by formulating the entity type classification task as a
“fill-in-the-blank” problem. This allows effective utilization of the
strong language modeling capability of Pre-trained Language Models
(PLMs). Despite the success of current prompt-based tuning
approaches, two major challenges remain: (1) the verbalizer in
prompts is either manually designed or constructed from external
knowledge bases, without considering the target corpus and
label hierarchy information, and (2) current approaches mainly
utilize the representation power of PLMs, but have not explored
their generation power acquired through extensive general-domain
pre-training. In this work, we propose a novel framework for fewshot
FET consisting of two modules: (1) an entity type label interpretation
module automatically learns to relate type labels to the
vocabulary by jointly leveraging few-shot instances and the label
hierarchy, and (2) a type-based contextualized instance generator
produces new instances based on given instances to enlarge the
training set for better generalization. On three benchmark datasets,
our model outperforms existing methods by significant margins.
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Formulating Few-shot Fine-tuning Towards Language Model Pre-training: A Pilot Study on Named Entity Recognition
Fine-tuning pre-trained language models is a common practice in building NLP models for various tasks, including the case with less supervision. We argue that under the few-shot setting, formulating fine-tuning closer to the pre-training objective shall be able to unleash more benefits from the pre-trained language models. In this work, we take few-shot named entity recognition (NER) for a pilot study, where existing fine-tuning strategies are much different from pre-training. We propose a novel few-shot fine-tuning framework for NER, FFF-NER. Specifically, we introduce three new types of tokens, “is-entity”, “which-type” and “bracket”, so we can formulate the NER fine-tuning as (masked) token prediction or generation, depending on the choice of the pre-training objective. In our experiments, we apply to fine-tune both BERT and BART for few-shot NER on several benchmark datasets and observe significant improvements over existing fine-tuning strategies, including sequence labeling, prototype meta-learning, and prompt-based approaches. We further perform a series of ablation studies, showing few-shot NER performance is strongly correlated with the similarity between fine-tuning and pre-training.
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- Award ID(s):
- 2040727
- NSF-PAR ID:
- 10403517
- Date Published:
- Journal Name:
- Findings of the Association for Computational Linguistics: EMNLP 2022
- Page Range / eLocation ID:
- 3186–3199
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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- The DOI is not currently available.
