Обучение на основе подсказок

method ID: promptbasedlearning

Related Papers

Automatic Multi-Label Prompting: Simple and Interpretable Few-Shot Classification

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractPrompt-based learning (i.e., prompting) is an emerging paradigm for exploiting knowledge learned by a pretrained language model. In this paper, we propose Automatic Multi-Label Prompting (AMuLaP), a simple yet effective …...

21011

Decorate the Examples: A Simple Method of Prompt Design for Biomedical Relation Extraction

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractRelation extraction is a core problem for natural language processing in the biomedical domain. Recent research on relation extraction showed that prompt-based learning improves the performance on both fine-tuning on …...

15127

DESED: Dialogue-based Explanation for Sentence-level Event Detection

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractMany recent sentence-level event detection efforts focus on enriching sentence semantics, e.g., via multi-task or prompt-based learning. Despite the promising performance, these methods commonly depend on label-extensive manual annotations or …...

4998

Good Examples Make A Faster Learner: Simple Demonstration-based Learning for Low-resource NER

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractRecent advances in prompt-based learning have shown strong results on few-shot text classification by using cloze-style templates. Similar attempts have been made on named entity recognition (NER) which manually design …...

39185

Learning to Transfer Prompts for Text Generation

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractPretrained language models (PLMs) have made remarkable progress in text generation tasks via fine-tuning. While, it is challenging to fine-tune PLMs in a data-scarce situation. Therefore, it is non-trivial to …...

21151

Prompt-based Distribution Alignment for Domain Generalization in Text Classification

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractPrompt-based learning (a.k.a. prompting) achieves high performance by bridging the gap between the objectives of language modeling and downstream tasks. Domain generalization ability can be improved by prompting since classification …...

30079

Prompting ELECTRA: Few-Shot Learning with Discriminative Pre-Trained Models

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractPre-trained masked language models successfully perform few-shot learning by formulating downstream tasks as text infilling. How- ever, as a strong alternative in full-shot settings, discriminative pre-trained models like ELECTRA do …...

29913

Cold-Start Data Selection for Better Few-shot Language Model Fine-tuning: A Prompt-based Uncertainty Propagation Approach

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractWe present PATRON, a prompt-based data selection method for pre-trained language model fine-tuning under cold-start scenarios, i.e., no initial labeled data are available. In PATRON, we design (1) a prompt-based …...

38712

Hierarchical Verbalizer for Few-Shot Hierarchical Text Classification

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractDue to the complex label hierarchy and intensive labeling cost in practice, the hierarchical text classification (HTC) suffers a poor performance especially when low-resource or few-shot settings are considered. Recently, …...

38693

L3I++ at SemEval-2023 Task 2: Prompting for Multilingual Complex Named Entity Recognition

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractThis paper summarizes the participation of the L3i laboratory of the University of La Rochelle in the SemEval-2023 Task 2, Multilingual Complex Named Entity Recognition (MultiCoNER II). Similar to MultiCoNER …...

24856

Scalable Prompt Generation for Semi-supervised Learning with Language Models

Authors: nan

Publication: TIPSTER (2023)

DOI: nan

AbstractPrompt-based learning methods in semi-supervised learning (SSL) settings have been shown to be effective on multiple natural language understanding (NLU) datasets and tasks in the literature. However, manually designing multiple …...

33392