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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This content will become publicly available on July 1, 2024
Tuning Language Models as Training Data Generators for Augmentation-Enhanced Few-Shot Learning
Recent studies have revealed the intriguing fewshot learning ability of pretrained language models (PLMs): They can quickly adapt to a new task when fine-tuned on a small amount of labeled data formulated as prompts, without requiring abundant task-specific annotations. Despite their promising performance, most existing few-shot approaches that only learn from the small training set still underperform fully supervised training by nontrivial margins. In this work, we study few-shot learning with PLMs from a different perspective: We first tune an autoregressive PLM on the few-shot samples and then use it as a generator to synthesize a large amount of novel training samples which augment the original training set. To encourage the generator to produce label discriminative samples, we train it via weighted maximum likelihood where the weight of each token is automatically adjusted based on a discriminative meta-learning objective. A classification PLM can then be fine-tuned on both the few-shot and the synthetic samples with regularization for better generalization and stability. Our approach FewGen achieves an overall better result across seven classification tasks of the GLUE benchmark than existing few-shot learning methods, improving no-augmentation methods by 5+ average points, and outperforming augmentation methods by 3+ average points.
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- NSF-PAR ID:
- 10467077
- Editor(s):
- Proc. 2023 Int. Conf. on Machine Learning
- Publisher / Repository:
- PMLR: Proceedings of Machine Learning Research
- Date Published:
- Edition / Version:
- 1
- Subject(s) / Keyword(s):
- ["Tuning Language Models, Training Data Generators, Augmentation-Enhanced Few-Shot Learning"]
- Format(s):
- Medium: X
- Location:
- Honolulu, Hawaii
- Sponsoring Org:
- National Science Foundation
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