A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis (2022.findings-naacl)
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| Challenge: | Pre-trained language models are often used to achieve state-of-the-art results . eval paper shows that generative language model can handle joint and multi-task settings . |
| Approach: | They propose to reformulate extraction and prediction tasks into a sequence generation task . they propose a generative language model with unidirectional attention that learns to accomplish the tasks via language generation . |
| Outcome: | The proposed model outperforms the state-of-the-art in few-shot and full-shot settings. |
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| Challenge: | Existing work on Aspect-based sentiment analysis ignores the rich label semantics of ABSA. |
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| Challenge: | Existing methods for Aspect category sentiment analysis use pre-trained language models to learn aspect category-specific representations. |
| Approach: | They propose to make use of pre-trained language models by casting the ACSA tasks into natural language generation tasks, using natural language sentences to represent the output. |
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Few-shot Joint Multimodal Aspect-Sentiment Analysis Based on Generative Multimodal Prompt (2023.findings-acl)
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| Challenge: | Existing studies require massive labeled data to train models for multimodal data analysis. |
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Exploring Graph Pre-training for Aspect-based Sentiment Analysis (2023.findings-emnlp)
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| Challenge: | Existing studies tend to extract the sentiment elements in a generative manner to avoid complex modeling of sentiment elements. |
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| Challenge: | Recent advances in Aspect-Based Sentiment Analysis (ABSA) have shown promising results, yet the semantics derived solely from textual data remain limited. |
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Joint Aspect and Polarity Classification for Aspect-based Sentiment Analysis with End-to-End Neural Networks (D18-1)
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| Challenge: | a new approach for aspect-based sentiment analysis is proposed . we compare the performance of the proposed approach with pipeline approaches . |
| Approach: | They propose a model for aspect-based sentiment analysis that uses a convolutional neural network and fasttext embeddings to combine the two approaches. |
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Open Aspect Target Sentiment Classification with Natural Language Prompts (2021.emnlp-main)
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| 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. |
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A Unified Generative Framework for Aspect-based Sentiment Analysis (2021.acl-long)
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| Challenge: | Existing complicated ABSA models focus on subtasks, which leads to complicated solutions . et al., j. c. d. r., and j dr. s. v. present a unified approach to solve seven subtask tasks in one framework. |
| Approach: | They redefine every subtask target as a sequence mixed by pointer indexes and sentiment class indexe . they exploit the pre-training sequence-to-sequence model BART to solve all ABSA subtasks in an end-to end framework. |
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Exploring Multilingual Pre-trained Language Model for Aspect-based Sentiment Analysis (2026.findings-acl)
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| Challenge: | Aspect-based sentiment analysis studies have focused on English datasets, but labeled data is scarce. |
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A Weak Supervision Approach for Few-Shot Aspect Based Sentiment Analysis (2024.eacl-long)
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Robert Vacareanu, Siddharth Varia, Kishaloy Halder, Shuai Wang, Giovanni Paolini, Neha Anna John, Miguel Ballesteros, Smaranda Muresan
| Challenge: | Existing methods to improve few-shot performance in aspect-based sentiment analysis (ABSA) require complex interactions between the target and the polarity of the sentiment. |
| Approach: | They propose a pipeline approach to construct a noisy ABSA dataset and adapt it to the ABSA tasks. |
| Outcome: | The proposed model outperforms the state-of-the-art on the aspect extraction sentiment classification task and is capable of performing the harder aspect sentiment triplet extraction task. |