Visual Elements Mining as Prompts for Instruction Learning for Target-Oriented Multimodal Sentiment Classification (2023.findings-emnlp)
Copied to clipboard
| Challenge: | VEMP uses visual elements with text symbols embedded in the image to classify sentiment polarity towards a given opinion target. |
| Approach: | They propose a visual element mining as prompts method to fuse visual and text semantic information into instruction prompts for TMSC. |
| Outcome: | The proposed method achieves state-of-the-art performance on two benchmark datasets. |
Similar Papers
Descriptive Prompt Paraphrasing for Target-Oriented Multimodal Sentiment Classification (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Current researches mainly work on either of two types of targets in a decentralized manner. |
| Approach: | They propose a model to perform sentiment polarity on a target jointly considering its corresponding multiple modalities including text, image, and others. |
| Outcome: | The proposed model performs well on four datasets spanning the above two target types and is prompt-based language modelling. |
Learning from Adjective-Noun Pairs: A Knowledge-enhanced Framework for Target-Oriented Multimodal Sentiment Classification (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods to determine sentiment polarity of opinion target are inconsistent and lack visual attention. |
| Approach: | They propose a framework which can exploit adjective-noun pairs extracted from images to improve visual attention and sentiment prediction capability of the TMSC task. |
| Outcome: | The proposed framework outperforms state-of-the-art on two public datasets. |
Mixture-of-Prompt-Experts for Multi-modal Semantic Understanding (2024.lrec-main)
Copied to clipboard
| Challenge: | Multimodal semantic understanding is crucial for developing machines capable of interpreting complex interplay of text and visual information. |
| Approach: | They propose a multi-modal soft prompt framework that integrates three experts of soft prompts . they propose sarcasm detection and sentiment analysis tasks that are critical for few-shot learning . |
| Outcome: | The proposed model outperforms the 8.2B model InstructBLIP with 2% parameters . it significantly outperformed other prompt methods on VLMs or task-specific methods . |
RethinkingTMSC: An Empirical Study for Target-Oriented Multimodal Sentiment Classification (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Recent studies have shown that current TMSC systems rely on textual information, and the progress in tackling this task has slowed down. |
| Approach: | They propose to integrate both visual and textual information to improve the performance of TMSC by considering multimodal information. |
| Outcome: | The proposed model integrates both visual and textual information to improve performance. |
Autonomous Aspect-Image Instruction a2II: Q-Former Guided Multimodal Sentiment Classification (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods to identify sentiment polarities of aspects are limited by the limited multimodal data available. |
| Approach: | They propose to use instruction tuning paradigm to combine language and vision data to combine text and image modalities. |
| Outcome: | The proposed model achieves state-of-the-art on benchmark datasets and in few-shot settings. |
SynPrompt: Syntax-aware Enhanced Prompt Engineering for Aspect-based Sentiment Analysis (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods of prompt-tuning for Aspect-based Sentiment Analysis (ABSA) are crude and simple. |
| Approach: | They propose a Syntax-aware Enhanced Prompt method which mines syntactic information related to aspect words from the syntaktic dependency tree. |
| Outcome: | The proposed method exploits the syntactic knowledge embedded in PLMs and achieves favorable results on three benchmark datasets. |
Prompt-learning for Fine-grained Entity Typing (2022.findings-emnlp)
Copied to clipboard
Ning Ding, Yulin Chen, Xu Han, Guangwei Xu, Xiaobin Wang, Pengjun Xie, Haitao Zheng, Zhiyuan Liu, Juanzi Li, Hong-Gee Kim
| Challenge: | Extensive experiments on fine-grained entity typing under fully supervised, few-shot, and zero-shot settings show the effectiveness of prompt-learning. |
| Approach: | They propose a prompt-learning pipeline that stimulates versatile knowledge of pre-trained language models (PLMs) by constructing entity-oriented verbalizers and templates and conducting masked language modeling. |
| Outcome: | The proposed approach can be applied to fine-grained entity typing in fully supervised, few-shot, and zero-shot scenarios. |
A Parameter-Efficient and Fine-Grained Prompt Learning for Vision-Language Models (2025.acl-long)
Copied to clipboard
| Challenge: | Current vision-language models extract semantic information from large-scale cross-modal associations, limiting performance and efficiency. |
| Approach: | They propose a detail-oriented prompt learning method to implement fine-grained multi-modal semantic alignment with merely 0.25M trainable parameters. |
| Outcome: | The proposed method implements fine-grained multi-modal semantic alignment with merely 0.25M trainable parameters. |
Open Aspect Target Sentiment Classification with Natural Language Prompts (2021.emnlp-main)
Copied to clipboard
| Challenge: | Existing aspects target sentiment classification models are not trainable if annotated data are not available. |
| Approach: | They propose an approach that solves ATSC with natural language prompts by 24.13 accuracy points and 33.14 macro F1 points. |
| Outcome: | The proposed model outperforms supervised SOTA approaches under few-shot scenarios and under supervised settings, especially for few-shot cases. |
Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts (2023.acl-long)
Copied to clipboard
| Challenge: | Recent studies have demonstrated that natural-language prompts can help to leverage the knowledge learned by pre-trained language models for the binary sentence-level sentiment classification task. |
| Approach: | They propose to use few-shot learning settings to fine-tune the sentiment classification model using manual or automatically generated prompts. |
| Outcome: | The proposed method outperforms the base prompt and the prompts generated using few-shot learning for the binary sentence-level sentiment classification task. |