| Challenge: | Existing approaches to ABSA use text encoders to locate important context features or remove them from input. |
| Approach: | They propose to improve ABSA with context denoising to remove noise from text . they use diffusion networks to perform denoizing process to gradually eliminate noise . paper shows that aspect-based sentiment analysis is effective for fine-grained analysis . |
| Outcome: | The proposed approach improves ABSA on five widely used ABSA datasets. |
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Let’s Rectify Step by Step: Improving Aspect-based Sentiment Analysis with Diffusion Models (2024.lrec-main)
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| Challenge: | Empirical evaluations conducted on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
| Approach: | They propose a diffusion model which extracts aspects step by step and learns a denoising process that progressively restores them in a reverse manner. |
| Outcome: | Empirical evaluations on eight benchmark datasets underscore the compelling advantages offered by DiffusionABSA when compared against robust baseline models. |
GCNet: Global-and-Context Collaborative Learning for Aspect-Based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing methods for analyzing aspect terms are focused on extracting semantic information inherent within the sentence. |
| Approach: | They propose a GCNet that explicitly leverages global semantic information to guide context encoding. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three public datasets. |
Complementary Learning of Aspect Terms for Aspect-based Sentiment Analysis (2022.lrec-1)
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| Challenge: | Existing ABSA models do not pay attention to aspect terms and their contexts . a discriminator is introduced to improve ABSA, allowing for better understanding of aspect terms . |
| Approach: | They propose to improve ABSA by complementary learning of aspect terms . they explicitly recover aspect terms from each input sentence to better understand aspects . |
| Outcome: | The proposed approach improves ABSA on five widely used English benchmark datasets. |
Modelling Context and Syntactical Features for Aspect-based Sentiment Analysis (2020.acl-main)
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| Challenge: | Existing approaches to aspect-based sentiment analysis do not fully leverage syntactical information. |
| Approach: | They propose an end-to-end aspect-based sentiment analysis solution that integrates syntactical information with part-of-speech embeddings and dependency-based embeddables to enhance the performance of the aspect extractor. |
| Outcome: | The proposed solution outperforms the state-of-the-art models on SemEval-2014 dataset in both subtasks. |
Aspect Is Not You Need: No-aspect Differential Sentiment Framework for Aspect-based Sentiment Analysis (2022.naacl-main)
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| Challenge: | Existing approaches to classify aspects with aspect sentiment bias are hard to find . |
| Approach: | They propose a no-aspect differential sentiment framework for the ABSA task that eliminates aspect sentiment bias and uses differential sentiment loss instead of cross-entropy loss to better classify the sentiments. |
| Outcome: | The proposed framework can be combined with almost all traditional ABSA methods. |
Deep Context- and Relation-Aware Learning for Aspect-based Sentiment Analysis (2021.acl-short)
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| Challenge: | Existing methods for aspect-based sentiment analysis (ABSA) consider relationships implicitly among subtasks at the word level. |
| Approach: | They propose a deep contextualized relation-aware network that allows interactive relations among subtasks . they propose self-supervised strategies that deal with multiple aspects . |
| Outcome: | The proposed method outperforms state-of-the-art methods on three widely used benchmarks. |
METNet: A Mutual Enhanced Transformation Network for Aspect-based Sentiment Analysis (2020.coling-main)
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| Challenge: | Existing methods for learning complex sentences with multiple aspects are ill-equipped to learn complex sentences . |
| Approach: | They propose a mutual enhanced transformation network for the ABSA task . it improves representation learning of the aspect with contextual semantic features . |
| Outcome: | The proposed model improves representation learning of the aspect with contextual semantic features, giving the aspect more abundant information. |
Improving Federated Learning for Aspect-based Sentiment Analysis via Topic Memories (2021.emnlp-main)
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| Challenge: | Aspect-based sentiment analysis (ABSA) predicts sentiment polarity for aspect term in sentences . labeled data stored at different locations and inaccessible due to privacy or legal concerns . |
| Approach: | They propose a model with federated learning to combine labeled data across different domains . they incorporate topic memory to take data from diverse domains into consideration . |
| Outcome: | The proposed model outperforms baselines on a simulated environment with three nodes. |
Towards Generative Aspect-Based Sentiment Analysis (2021.acl-short)
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| Challenge: | Existing work on Aspect-based sentiment analysis ignores the rich label semantics of ABSA. |
| Approach: | They propose to tackle various ABSA tasks in a unified generative framework . they propose to use annotation-style and extraction-style modeling to enable training . |
| Outcome: | The proposed framework achieves state-of-the-art on four ABSA tasks across multiple benchmark datasets. |
Aspect Based Sentiment Analysis with Gated Convolutional Networks (P18-1)
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| Challenge: | Aspect-based sentiment analysis can provide more detailed information than general sentiment analysis. |
| Approach: | They propose a model based on convolutional neural networks and gating mechanisms which can selectively output the sentiment features according to the given aspect or entity. |
| Outcome: | The proposed model can selectively output sentiment features according to the given aspect or entity. |