Generating Disentangled Arguments with Prompts: A Simple Event Extraction Framework that Works

Si, Jinghui; Peng, Xutan; Li, Chen; Xu, Haotian; Li, Jianxin

doi:10.48550/arxiv.2110.04525

Cited by 1 publication

(1 citation statement)

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“…It reformulates various NLP tasks as clozestyle questions, and by doing so, the knowledge stored in PLMs can be fully exploited, making PLMs achieve impressive performance in few-shot and zero-shot settings. Along this research line, various types of prompts are explored including discrete and continuous prompts (Gao et al, 2021a;Shin et al, 2020;Cui et al, 2021;Si et al, 2021;Li and Liang, 2021;Schick and Schütze, 2021b). In this work, we exploit prompts of different downstream tasks to assign various virtual semantic prototypes to each instance.…”

Section: Prompt-based Learningmentioning

confidence: 99%

Contrastive Learning with Prompt-derived Virtual Semantic Prototypes for Unsupervised Sentence Embedding

Zeng¹,

Yin²,

Jiang³

et al. 2022

Preprint

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Contrastive learning has become a new paradigm for unsupervised sentence embeddings. Previous studies focus on instance-wise contrastive learning, attempting to construct positive pairs with textual data augmentation. In this paper, we propose a novel Contrastive learning method with Prompt-derived Virtual semantic Prototypes (ConPVP). Specifically, with the help of prompts, we construct virtual semantic prototypes to each instance, and derive negative prototypes by using the negative form of the prompts. Using a prototypical contrastive loss, we enforce the anchor sentence embedding to be close to its corresponding semantic prototypes, and far apart from the negative prototypes as well as the prototypes of other sentences. Extensive experimental results on semantic textual similarity, transfer, and clustering tasks demonstrate the effectiveness of our proposed model compared to strong baselines. Code is available at https://github.com/lemon0830/promptCSE.

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