MvP: Multi-view Prompting Improves Aspect Sentiment Tuple Prediction (2023.acl-long)
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| Challenge: | Existing studies predict sentiment elements in a fixed order, which ignores the interdependence of the elements and the diversity of language expression. |
| Approach: | They propose a multi-view process that aggregates sentiment elements generated in different order . they use element order prompts to guide the language model to generate multiple tuples with different element order based on a given text . |
| Outcome: | The proposed method outperforms existing methods on 10 datasets of 4 benchmark tasks and is highly flexible and transferable across tasks. |
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| Challenge: | Aspect-based sentiment analysis (ABSA) assesses sentiments towards aspects within texts, resulting in detailed sentiment tuples. |
| Approach: | They propose a dynamic order template method that dynamically creates an order template that contains only the necessary views for each instance. |
| Outcome: | The proposed method improves F1 scores on ASQP and ACOS datasets while significantly reducing inference time. |
LEGO-ABSA: A Prompt-based Task Assemblable Unified Generative Framework for Multi-task Aspect-based Sentiment Analysis (2022.coling-1)
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| Challenge: | Existing generative methods focus on a single task at a time. |
| Approach: | They propose a unified generative multi-task framework that can solve multiple ABSA tasks . they propose to control the type of task prompts consisting of multiple element prompts . |
| Outcome: | The proposed framework achieves state-of-the-art results in almost all ABSA tasks and competitive results in task transfer scenarios. |
BvSP: Broad-view Soft Prompting for Few-Shot Aspect Sentiment Quad Prediction (2024.acl-long)
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| Challenge: | Aspect sentiment quad prediction aims to predict aspects due to distinct data distribution. |
| Approach: | They propose a method that aggregates multiple templates with a broader view . they first construct a few-shot ASQP dataset that contains richer categories . |
| Outcome: | The proposed method outperforms the state-of-the-art methods under four few-shot settings and other public datasets. |
Aspect Sentiment Quad Prediction as Paraphrase Generation (2021.emnlp-main)
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| Challenge: | Existing studies focus on predicting the four elements in one shot, instead of predicting them all. |
| Approach: | They propose a task to jointly detect all sentiment elements in quads for a given opinionated sentence. |
| Outcome: | The proposed method can generate the semantics of the sentiment elements in the natural language form. |
It is Simple Sometimes: A Study On Improving Aspect-Based Sentiment Analysis Performance (2024.findings-acl)
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| Challenge: | Existing studies have focused on developing ad hoc models of varying complexity for ABSA subtasks. |
| Approach: | They propose an instruction-based model with task descriptions followed by in-context examples on ABSA subtasks. |
| Outcome: | The proposed method outperforms state-of-the-art methods on most domains and achieves competitive results on biomedical domain datasets. |
On the Strength of Sequence Labeling and Generative Models for Aspect Sentiment Triplet Extraction (2023.findings-acl)
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| Challenge: | Existing generative models ignore the mutual informative clues between aspect and opinion terms and may generate false paired triplets. |
| Approach: | They propose a generative model that encodes aspect and opinion into two bidirectional templates and introduces a marker-oriented sequence labeling module to improve models’ ability of tackling complex structures. |
| Outcome: | The proposed model captures the boundary information of aspect/opinion spans and provides hints to decode multiple triplets with the shared marker. |
E-ABSA20K: A Dataset and Propose-and-Verify for Aspect-Based Sentiment Analysis in Long E-commerce Reviews (2026.findings-acl)
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| Challenge: | Aspect-based Sentiment Analysis (ABSA) is critical for extracting actionable product insights from e-commerce reviews. |
| Approach: | They propose a framework that decomposes ABSA into two stages to extract review-level quadruple reviews from 20K reviews from four product categories. |
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Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation (2022.emnlp-main)
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| Challenge: | Recent work on aspect sentiment quad prediction (ASQP) uses a template to extract aspect quadruplets from review sentences. |
| Approach: | They propose to use a pre-trained language model to select proper orders from a template order perspective to improve aspect sentiment quad prediction. |
| Outcome: | The proposed method outperforms state-of-the-art methods significantly in low-resource settings. |
SynPrompt: Syntax-aware Enhanced Prompt Engineering for Aspect-based Sentiment Analysis (2024.lrec-main)
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| Challenge: | Existing methods of prompt-tuning for Aspect-based Sentiment Analysis (ABSA) are crude and simple. |
| Approach: | They propose a Syntax-aware Enhanced Prompt method which mines syntactic information related to aspect words from the syntaktic dependency tree. |
| Outcome: | The proposed method exploits the syntactic knowledge embedded in PLMs and achieves favorable results on three benchmark datasets. |
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. |
| Outcome: | The proposed framework achieves substantial performance gain and provides a real unified solution for the whole ABSA subtasks. |