Unified Feature and Instance Based Domain Adaptation for Aspect-Based Sentiment Analysis (2020.emnlp-main)
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| Challenge: | Existing approaches to aspect-based sentiment analysis rely on labeled data, but they lack the fine-grained labeles needed for the ABSA task. |
| Approach: | They propose a framework to perform feature adaptation and instance adaptation for the ABSA task . they learn domain-invariant feature representations by using part-of-speech features . |
| Outcome: | The proposed method improves on the state-of-the-art in two aspects of the ABSA task. |
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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. |
Source-free Domain Adaptation for Aspect-based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Unsupervised Domain Adaptation (UDA) of the Aspect-based Sentiment Analysis task is a data mining technique that involves aspect extraction and aspect sentiment classification subtasks. |
| Approach: | They propose a framework that allows model parameter transfer, not data transfer, between different domains. |
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Utilizing BERT for Aspect-Based Sentiment Analysis via Constructing Auxiliary Sentence (N19-1)
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| Challenge: | Sentiment analysis (SA) is a computational task that aims to identify opinion polarity towards a specific aspect. |
| Approach: | They propose to convert ABSA into a sentence-pair classification task such as question answering and natural language inference. |
| Outcome: | The proposed model is fine-tuned and achieves state-of-the-art on SentiHood and SemEval-2014 datasets. |
Bidirectional Generative Framework for Cross-domain Aspect-based Sentiment Analysis (2023.acl-long)
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| Challenge: | Aspect-based sentiment analysis (ABSA) is a task of analyzing people's sentiments at the aspect level. |
| Approach: | They propose a unified bidirectional generative framework to tackle cross-domain ABSA tasks . the framework trains a model in both text-to-label and label-totext directions . |
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Unsupervised Data Augmentation for Aspect Based Sentiment Analysis (2022.coling-1)
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| Challenge: | Recent approaches to Aspect-based Sentiment Analysis (ABSA) perform the subtasks of aspect term extraction (ATE) and aspect sentiment classification (ASC) simultaneously. |
| Approach: | They introduce an adaptation of Unsupervised Data Augmentation in semi-supervised learning that performs both aspects of Aspect-based Sentiment Analysis (ABSA) and aspect sentiment classification (ASC) they show that simple augmentations applied to modest-sized datasets along with consistency training lead to competitive performance with current ABSA state-of-the-art in restaurant and laptop domains . |
| Outcome: | The proposed approach performs well on a span-level classification task with minimal training data. |
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. |
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 . |
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Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model (2021.emnlp-main)
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| Challenge: | Existing models focus on aspect term extraction, opinion term extraction and sentiment polarity classification but ignore the difference. |
| Approach: | They propose a joint aspect-based sentiment analysis task that focuses on the difference between the two tasks to improve the model's robustness. |
| Outcome: | Empirical results show that the proposed model outperforms the previous state-of-the-art on four benchmark datasets. |
Cross-Domain Data Augmentation with Domain-Adaptive Language Modeling for Aspect-Based Sentiment Analysis (2023.acl-long)
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| Challenge: | Cross-domain Aspect-Based Sentiment Analysis (ABSA) aims to identify aspect-sentiment pairs in sentences from a target domain. |
| Approach: | They propose a domain-adaptive language model to generate labeled data from a source domain. |
| Outcome: | The proposed approach outperforms existing methods on ABSA and Aspect Extraction tasks. |
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. |