Challenge: Existing studies on aspect-level sentiment analysis focus on extracting aspect terms and sentiment polarities separately.
Approach: They propose a multi-modal joint learning approach with auxiliary cross-modal relation detection for multi-dimensional aspect-level sentiment analysis.
Outcome: The proposed approach can obtain all aspect-level sentiment polarities dependent on the jointly extracted specific aspects.

Similar Papers

Joint Aspect Extraction and Sentiment Analysis with Directional Graph Convolutional Networks (2020.coling-main)

Copied to clipboard

Challenge: End-to-end aspect-based sentiment analysis uses two sub-tasks to extract aspect terms . experimental results demonstrate the effectiveness of our approach on all datasets .
Approach: They propose to combine aspect extraction and sentiment analysis with encoding syntactic information to improve model's representation of input sentences.
Outcome: The proposed approach achieves state-of-the-art on three benchmark datasets.
Aspect-to-Scope Oriented Multi-view Contrastive Learning for Aspect-based Sentiment Analysis (2023.findings-emnlp)

Copied to clipboard

Challenge: Existing methods for Aspect-based sentiment analysis (ABSA) focus on mining syntactic or semantic information, which suffers from noisy interference when multiple aspects exist in a sentence.
Approach: They propose a scope-assisted multi-view graph contrastive learning framework that captures correlation and difference between aspect and syntactic/semantic information.
Outcome: The proposed framework outperforms state-of-the-art methods on five benchmark datasets and verifies its effectiveness and robustness.
Complementary Learning of Aspect Terms for Aspect-based Sentiment Analysis (2022.lrec-1)

Copied to clipboard

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.
A Multi-task Learning Framework for Opinion Triplet Extraction (2020.findings-emnlp)

Copied to clipboard

Challenge: Existing approaches to Aspect-based sentiment analysis (ABSA) use aspect terms and their corresponding sentiment polarities as a reference, but they lack opinion terms as .
Approach: They propose a multi-task learning framework to extract aspect terms and opinion terms and parse their sentiment dependencies with a biaffine scorer.
Outcome: The proposed framework outperforms baseline and state-of-the-art approaches on four SemEval benchmarks.
A Hierarchical Interactive Network for Joint Span-based Aspect-Sentiment Analysis (2022.coling-1)

Copied to clipboard

Challenge: Existing methods for aspect-sentiment analysis ignore internal correlations between aspect extraction and sentiment classification.
Approach: They propose a hierarchical interactive network to model two-way interactions between two tasks appropriately using shallow-level and deep-level inputs.
Outcome: Extensive experiments on three real-world datasets demonstrate that the proposed model outperforms existing methods.
Making Flexible Use of Subtasks: A Multiplex Interaction Network for Unified Aspect-based Sentiment Analysis (2021.findings-acl)

Copied to clipboard

Challenge: Existing studies aim to integrate multiple sub-tasks into a unified ABSA model but suffer from major disadvantages .
Approach: They propose a multi-task learning approach to make use of sub-tasks for a unified ABSA.
Outcome: The proposed model can work well when some sub-tasks are absent, and the interactive relations among subtasks not adequate.
Seeking Common but Distinguishing Difference, A Joint Aspect-based Sentiment Analysis Model (2021.emnlp-main)

Copied to clipboard

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.
AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis (2023.findings-acl)

Copied to clipboard

Challenge: Existing methods to extract aspects from text-image pairs and recognize their sentiments are noisy and coarsely establishing image-aspect alignment will interfere with aspect-relevant semantic and sentiment information.
Approach: They propose an Aspect-oriented method to detect aspect-relevant semantic and sentiment information by selecting textual tokens and image blocks that are semantically related to the aspects.
Outcome: The proposed method is superior to existing methods in the field of sentiment analysis.
Relation-Aware Collaborative Learning for Unified Aspect-Based Sentiment Analysis (2020.acl-main)

Copied to clipboard

Challenge: Existing studies focus on one of three subtasks for aspect-based sentiment analysis (ABSA) Existing work develops separate methods for each subtask, or takes OE as an auxiliary task of AE.
Approach: They propose a relation-aware collaborative learning framework which allows subtasks to work coordinately via multi-task learning and relation propagation mechanisms.
Outcome: Extensive experiments on three real-world datasets show that RACL outperforms state-of-the-art methods for ABSA.
Self Question-answering: Aspect-based Sentiment Analysis by Role Flipped Machine Reading Comprehension (2021.findings-emnlp)

Copied to clipboard

Challenge: unified Aspect-based Sentiment Analysis (ABSA) aims to couple aspect terms with their corresponding opinion terms, which might make it easier to predict sentiment polarities.
Approach: They propose a new paradigm to pair aspect terms with their corresponding opinion terms . they propose to use a machine learning paradigm to solve the unified ABSA task .
Outcome: The proposed framework can solve the ABSA task without any additional data annotation or transformation.

What is GenGO?

GenGO is an NLP powered publication search system. It currenctly indexes 30k+ papers from ACL Anthology, and implements multi-aspect summarization, semantic search, and more!

Information

About
Limitations