TacoPrompt: A Collaborative Multi-Task Prompt Learning Method for Self-Supervised Taxonomy Completion

Challenge:	Existing methods for automating taxonomy completion use subtasks to learn subtask results, ignoring the effects of subtask on the final prediction.
Approach:	They propose a multi-task automatic taxonomy completion method that attaches emerging concepts to an appropriate pair of hypernym and hyponym in existing taxonomies.
Outcome:	The proposed method improves on three datasets and improves inference efficiency.

Similar Papers

Overview

Challenge

Approach

Outcome

TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification (2021.emnlp-main)

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Chengyu Wang, Jianing Wang, Minghui Qiu, Jun Huang, Ming Gao

Language Models

Challenge:	Recent studies show that prompts improve performance of large pre-trained language models for few-shot text classification.
Approach:	They propose a prompt-based framework for few-shot learning that captures cross-task transferable knowledge and uses two de-biasing techniques to make it more task-agnostic and unbiased .
Outcome:	The proposed framework outperforms strong baselines over multiple NLP tasks and datasets.

Prompt-Tuned Muti-Task Taxonomic Transformer (PTMTTaxoFormer) (2024.emnlp-industry)

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Rajashekar Vasantha, Nhan Nguyen, Yue Zhang

Semantic Text Processing

Challenge:	Existing methods for Hierarchical Text Classification (HTC) are expensive and require explicit injection of the hierarchy, verbalizers, and/or prompt engineering.
Approach:	They propose a hierarchical text classification system that uses a single classifier to predict one or more topics using differentiable prompts and labels that are learnt through backpropagation.
Outcome:	The proposed model outperforms existing models on several benchmarks that span a range of topics consistently.

TEPrompt: Task Enlightenment Prompt Learning for Implicit Discourse Relation Recognition (2023.findings-acl)

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Wei Xiang, Chao Liang, Bang Wang

Semantic Text Processing

Challenge:	Existing prompt learning models for IDRR use multiple-prompt decisions from three different yet much similar connective prediction templates.
Approach:	They propose to fuse three related tasks to fuse the learned features of auxiliary tasks to create a prompt learning model that can be used to boost the main task.
Outcome:	The proposed model outperforms the ConnPrompt in the training phase and in the testing phase.

OpenPrompt: An Open-source Framework for Prompt-learning (2022.acl-demo)

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Ning Ding, Shengding Hu, Weilin Zhao, Yulin Chen, Zhiyuan Liu, Haitao Zheng, Maosong Sun

Language Models

Challenge:	Prompt-learning is a new paradigm in natural language processing, adapting pre-trained language models to cloze-style prediction, autoregressive modeling, or sequence to sequence generation.
Approach:	They propose a framework for prompt-learning that integrates pre-trained language models with a unified framework.
Outcome:	The proposed framework is easy to use and flexible enough to integrate with other frameworks.

NCPrompt: NSP-Based Prompt Learning and Contrastive Learning for Implicit Discourse Relation Recognition (2024.findings-emnlp)

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Yuetong Rong, Yijun Mo

Semantic Text Processing

Challenge:	Recent prompt learning methods have demonstrated success in IDRR, but they fail to fully exploit critical semantic features shared among various forms of templates.
Approach:	They propose an NSP-based prompt learning and contrastive learning method for IDRR that transforms the IDRR task into a next sentence prediction task.
Outcome:	The proposed model can be used to classify the discourse relation sense between argument pairs without an explicit connective.

Bridging the Sensory Gap: Visual Injection for Taxonomy Completion (2026.acl-long)

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Yuhang Niu, Hongyuan Xu, Ciyi Liu, Bofan Wei, Jiaqi Ye, Yanlong Wen, Xiaojie Yuan

Multimodality

Challenge:	Existing text-only methods suffer from a "Sensory Gap" in integrating new concepts into existing hierarchies.
Approach:	They propose a framework leveraging Visual Injection for Taxonomy Completion that maps synthesized images into intrinsic pseudo-tokens and decouples magnitude from selection to prevent visual signals from being drowned out.
Outcome:	Experiments on three datasets show that VITC achieves state-of-the-art performance . it delivers an average absolute gain of over 19% in Hit@1.

PromptPrism: A Linguistically-Inspired Taxonomy for Prompts (2026.findings-eacl)

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Sullam Jeoung, Yueyan Chen, Yi Zhang, Shuai Wang, Haibo Ding, Lin Lee Cheong

Responsible & Trustworthy NLP

Challenge:	PromptPrism is a linguistically-inspired taxonomy that enables prompt analysis across three hierarchical levels.
Approach:	They propose a linguistically-inspired taxonomy that enables prompt analysis across three hierarchical levels: functional structure, semantic component, and syntactic pattern.
Outcome:	The proposed taxonomy bridges traditional language understanding with modern LLM research . it improves prompt quality and improves model performance across tasks .

AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts (2020.emnlp-main)

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Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, Sameer Singh

Language Models

Challenge:	Pretrained language models have been successful when finetuned to downstream tasks . however, it is difficult to determine whether the knowledge that finetuning LMs contain is learned during the pretraining or the finetailing process.
Approach:	They propose a method to create prompts for a diverse set of tasks using a gradient-guided search.
Outcome:	The proposed method performs sentiment analysis and natural language inference without additional parameters and finetuning.

ClusterPrompt: Cluster Semantic Enhanced Prompt Learning for New Intent Discovery (2023.findings-emnlp)

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Jinggui Liang, Lizi Liao

Low-Resource NLP

Challenge:	Existing methods for identifying new intent categories focus on relations between utterances and clusters, while neglecting the usage of semantics.
Approach:	They propose a method that leverages contrastive learning and label semantic alignment to learn meaningful representations of intent clusters.
Outcome:	The proposed method outperforms existing methods and suggests meaningful intent labels.

NER-guided Comprehensive Hierarchy-aware Prompt Tuning for Hierarchical Text Classification (2024.lrec-main)

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Fuhan Cai, Duo Liu, Zhongqiang Zhang, Ge Liu, Xiaozhe Yang, Xiangzhong Fang

Information Extraction & Text Mining

Challenge:	Hierarchical text classification (HTC) is a challenging task in natural language processing due to its complex taxonomic label hierarchy.
Approach:	They propose to use prompts to model hierarchical text classification (HTC) they propose to introduce conditional random fields and Global Pointer to establish hierarchic dependencies .
Outcome:	The proposed approach achieves state-of-the-art (SoTA) performance on three public datasets.