Papers with AOPE
InstructABSA: Instruction Learning for Aspect Based Sentiment Analysis (2024.naacl-short)
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| Challenge: | Experimental results on the Sem Eval 2014, 15, and 16 datasets demonstrate that InstructABSA outperforms the previous state-of-the-art (SOTA) approaches on Term Extraction (ATE), Sentiment Classification(ATSC) and Sentimence Pair Extraction(ASPE) subtasks. |
| Approach: | They introduce positive, negative, and neutral examples to each training sample, and instruction tune the model (Tk-Instruct) for ABSA subtasks. |
| Outcome: | The proposed model outperforms the state-of-the-art (SOTA) on Term Extraction (ATE), Sentiment Classification (ATSC) and Sentimence Pair Extractions (ASPE) subtasks. |
Target-specified Sequence Labeling with Multi-head Self-attention for Target-oriented Opinion Words Extraction (2021.naacl-main)
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| Challenge: | Recent studies on ABSA focus on Target-oriented Opinion Words (or Terms) Extraction . Experimental results indicate that TSMSA outperforms the benchmark methods on TOWE significantly . |
| Approach: | They propose to use a pre-trained language model with multi-head self-attention to integrate TOWE with AOPE to extract aspects and opinion terms in pairs. |
| Outcome: | The proposed structure outperforms the benchmark methods on TOWE significantly . the proposed structure is similar or even better than state-of-the-art AOPE models . |
Seq2Path: Generating Sentiment Tuples as Paths of a Tree (2022.findings-acl)
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| Challenge: | Existing generative methods for extracting sentiment tuples do not have orders between the t-uples . a novel parallel generative framework for ABSA is proposed . |
| Approach: | They propose a parallel generative framework to generate sentiment tuples as paths of a tree . they train the model with an independent target and introduce a discriminative token . |
| Outcome: | The proposed method achieves state-of-the-art on AOPE, ASTE, TASD, UABSA, ACOS . it trains with the loss of ordinary Seq2Seq averaged over paths, and inferences automatically select valid paths. |
Train Once for All: A Transitional Approach for Efficient Aspect Sentiment Triplet Extraction (2025.findings-emnlp)
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| Challenge: | Existing approaches to extract aspects and opinions independently, optionally adding pairwise relations, often lead to error propagation and high time complexity. |
| Approach: | They propose a transition-based model that performs aspect and opinion extraction jointly and integrates contrastive-augmented optimization. |
| Outcome: | The proposed model outperforms previous models on two out of four datasets when trained on a single dataset. |
Synchronous Double-channel Recurrent Network for Aspect-Opinion Pair Extraction (2020.acl-main)
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| Challenge: | Existing studies focus on aspect-opinion relation detection, but neglect to recognize the relations between aspects and opinion expressions. |
| Approach: | They propose a Synchronous Double-channel Recurrent Network to deal with AOPE task . they propose an opinion entity extraction unit, a relation detection unit, and a synchronization unit . |
| Outcome: | The proposed system achieves state-of-the-art in opinion entity extraction . it is based on three datasets based upon SemEval 2014 and 2015 benchmarks . |