Papers with ASTE
Dual-Channel Span for Aspect Sentiment Triplet Extraction (2023.emnlp-main)
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| Challenge: | Existing approaches to extract sentiment triplets are too noisy and enumerate all possible spans. |
| Approach: | They propose a dual-channel span generation method to constrain the search space of span candidates. |
| Outcome: | The proposed method reduces span enumeration by nearly half on two versions of public datasets. |
A Robustly Optimized BMRC for Aspect Sentiment Triplet Extraction (2022.naacl-main)
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| Challenge: | Aspect sentiment triplet extraction (ASTE) is a challenging subtask in aspect-based sentiment analysis. |
| Approach: | They propose a bidirectional machine reading comprehension method to extract triplets of aspects, opinions and sentiments with complex correspondence from the context. |
| Outcome: | The proposed method achieves state-of-the-art on multiple benchmark datasets. |
MATO: A Model-Agnostic Training Optimization for Aspect Sentiment Triplet Extraction (2025.naacl-long)
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| Challenge: | Existing models with strong in-house performance may struggle to generalize to diverse expressions. |
| Approach: | They propose a model-agnostic t**raining method to improve ASTE model inference . they propose to compute the violation rate (VR) on each element of one triplet . |
| Outcome: | The proposed method can improve aspect sentiment triplet extraction models consistent with expected results facing triplet element diversity. |
Semantic and Syntactic Enhanced Aspect Sentiment Triplet Extraction (2021.findings-acl)
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| Challenge: | Existing approaches to extract triplets from sentences neglect the mutual information between aspects and have the problem of error propagation. |
| Approach: | They propose a Semantic and Syntactic Enhanced aspect Sentiment triplet Extraction model to exploit the syntactical and semantic relationships between the triplet elements and jointly extract them. |
| Outcome: | The proposed model outperforms existing methods on four benchmark datasets and significantly outperformed existing approaches. |
Tagging-Assisted Generation Model with Encoder and Decoder Supervision for Aspect Sentiment Triplet Extraction (2023.emnlp-main)
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| Challenge: | Recent advances in the ASTE task have been driven by Natural Language Generation-based approaches, but most NLG methods overlook the supervision of the encoder-decoder hidden representations and fail to fully utilize the semantic information provided by the labels. |
| Approach: | They propose a tagging-assisted generation model with encoder and decoder supervision that enhances the supervision of the encoder-decoder through multiple-perspective tabbing assistance and label semantic representations. |
| Outcome: | The proposed model enhances the supervision of the encoder and decoder through multiple-perspective tagging assistance and label semantic representations. |
ASTE-Transformer: Modelling Dependencies in Aspect-Sentiment Triplet Extraction (2024.findings-emnlp)
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| Challenge: | Aspect-Sentiment Triplet Extraction (ASTE) is a recent task in aspect-based sentiment analysis. |
| Approach: | They propose a task of aspect-based sentiment analysis that extracts triples from sentences . they propose three transformer-inspired layers to enable modelling of dependencies . |
| Outcome: | The proposed method achieves higher performance in terms of F1 measure than other methods studied on popular benchmarks. |
MiniConGTS: A Near Ultimate Minimalist Contrastive Grid Tagging Scheme for Aspect Sentiment Triplet Extraction (2024.emnlp-main)
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| Challenge: | Existing approaches within the pretraining-finetuning paradigm tend to meticulously craft complex tagging schemes and classification heads, or incorporate external semantic enhancements to enhance performance. |
| Approach: | They propose to integrate a minimalist tagging scheme and a novel token-level contrastive learning strategy to improve pretrained representations. |
| Outcome: | The proposed framework achieves comparable or superior performance compared to state-of-the-art techniques while featuring a more compact design and reduced computational overhead. |
Automatic Construction of a Chinese Review Dataset for Aspect Sentiment Triplet Extraction via Iterative Weak Supervision (2024.lrec-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is a task that involves the extraction of three key elements: target aspects, descriptive opinion spans, and their corresponding sentiment polarity. |
| Approach: | They propose a framework that facilitates automatic construction of Aspect Sentiment Triplet Extraction (ASTE) by iterative weak supervision and a discriminator to weed out subpar samples. |
| Outcome: | The proposed framework automates the construction of Aspect Sentiment Triplet Extraction tasks in Chinese by using iterative weak supervision. |
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment Analysis (2024.eacl-long)
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Robert Vacareanu, Siddharth Varia, Kishaloy Halder, Shuai Wang, Giovanni Paolini, Neha Anna John, Miguel Ballesteros, Smaranda Muresan
| Challenge: | Existing methods to improve few-shot performance in aspect-based sentiment analysis (ABSA) require complex interactions between the target and the polarity of the sentiment. |
| Approach: | They propose a pipeline approach to construct a noisy ABSA dataset and adapt it to the ABSA tasks. |
| Outcome: | The proposed model outperforms the state-of-the-art on the aspect extraction sentiment classification task and is capable of performing the harder aspect sentiment triplet extraction task. |
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. |
Measuring Your ASTE Models in The Wild: A Diversified Multi-domain Dataset For Aspect Sentiment Triplet Extraction (2023.findings-acl)
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| Challenge: | Existing ASTE datasets are limited in their ability to represent real-world scenarios, hindering progress in this area. |
| Approach: | They propose a new ASTE dataset that is manually annotated to better fit real-world scenarios by providing more diverse and realistic reviews. |
| Outcome: | The proposed dataset is manually annotated to better fit real-world scenarios. |
Position-Aware Tagging for Aspect Sentiment Triplet Extraction (2020.emnlp-main)
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| Challenge: | Existing research efforts focus on extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment. |
| Approach: | They propose a position-aware tagging scheme that can extract triplets using a sequence tapping approach. |
| Outcome: | The proposed model improves performance on multiple datasets and compares with existing models. |
Refine, Align, and Aggregate: Multi-view Linguistic Features Enhancement for Aspect Sentiment Triplet Extraction (2024.findings-acl)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) aims to extract the triplets of aspect terms, their associated sentiment and opinion terms. |
| Approach: | They propose to use multi-view linguistic features enhancement to explore the prior indication effect in the “Refine, Align, and Aggregate” learning process to enhance aspect-opinion relations. |
| Outcome: | The proposed model achieves state-of-the-art on several benchmark datasets and is robust to state- of-the art constraints. |
COM-MRC: A COntext-Masked Machine Reading Comprehension Framework for Aspect Sentiment Triplet Extraction (2022.emnlp-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) aims to extract sentiment triplets from sentences, but when faced with multiple aspect terms, the MRC-based methods could fail due to the interference from other aspect terms. |
| Approach: | They propose a COntext-Masked MRC framework for Aspect Sentiment Triplet Extraction (ASTE) which aims to extract sentiment triplets from sentences . |
| Outcome: | The proposed framework outperforms state-of-the-art methods on benchmark datasets and shows that it can extract sentiment triplets from multiple aspect terms. |
Enhanced Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction (2022.acl-long)
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| Challenge: | Existing methods to extract aspect triplets ignore the relationships between words . Enhanced Multi-Channel Graph Convolutional Network model can be used to learn relation-aware node representations. |
| Approach: | They propose an Enhanced Multi-Channel Graph Convolutional Network model to fully utilize the relations between words for ASTE task. |
| Outcome: | The proposed model outperforms state-of-the-art methods significantly on a benchmark dataset. |
Learning Cooperative Interactions for Multi-Overlap Aspect Sentiment Triplet Extraction (2022.findings-emnlp)
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| Challenge: | Existing methods for aspect sentiment triplet extraction focus on the single interactions between an aspect and an opinion. |
| Approach: | They propose a multi-overlap triplet extraction method which decodes the complex relations between multiple aspects and opinions by learning their cooperative interactions. |
| Outcome: | The proposed method outperforms baselines, especially multi-overlap triplets. |
Test-Time Code-Switching for Cross-lingual Aspect Sentiment Triplet Extraction (2025.naacl-long)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is a thriving research area . current code-switching methods suffer from term boundary detection issues and out-of-dictionary problems. |
| Approach: | They propose a test-time code-switching framework which bridges the gap between bilingual training and monolingual test- time prediction. |
| Outcome: | The proposed framework achieves an average improvement of 3.7% on four cross-lingual datasets. |
A Span-level Bidirectional Network for Aspect Sentiment Triplet Extraction (2022.emnlp-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is a new fine-grained sentiment analysis task . recent studies have focused on solving aspects term extraction, opinion term extraction and aspect-level sentiment classification tasks individually or in combination of two subtasks. |
| Approach: | They propose a span-level bidirectional network which utilizes all possible spans as input and extracts triplets from spans bidirectionally. |
| Outcome: | The proposed framework outperforms state-of-the-art methods and improves performance . it can extract triplets of aspect terms, sentiments, and opinion terms from review sentences . |
Making Better Use of Training Corpus: Retrieval-based Aspect Sentiment Triplet Extraction via Label Interpolation (2023.findings-acl)
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| Challenge: | Existing methods to extract sentimental triplets are infeasible and counterproductive . aspect Sentiment Triplets Extraction (ASTE) task is an emerging sub-task of Aspect-based Sentimence Analysis . |
| Approach: | They propose a retrieval-based approach to the Aspect Sentiment Triplet Extraction task . they retrieve semantic similar triplets from the training corpus and interpolate their label information . |
| Outcome: | The proposed approach establishes a new state-of-the-art on the Aspect Sentiment Triplet Extraction task. |
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. |
Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction (2021.acl-long)
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| Challenge: | Recent models perform the triplet extraction in an end-to-end manner but heavily rely on the interactions between each word and opinion word. |
| Approach: | They propose a span-level approach which explicitly considers the interaction between whole spans of targets and opinions when predicting their sentiment relation. |
| Outcome: | The proposed approach improves on triplets with multi-word targets and opinions . it explicitly considers the interaction between whole spans of targets and opinion words . |
Boundary-Driven Table-Filling for Aspect Sentiment Triplet Extraction (2022.emnlp-main)
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| Challenge: | Existing work focuses on extracting aspect terms and opinion terms without considering the relations between aspect terms . |
| Approach: | They propose a task to extract aspect terms, opinion terms, and expressed sentiments from a two-dimensional (2D) table. |
| Outcome: | The proposed method achieves state-of-the-art on several public benchmarks and is well-suited to the ASTE task. |
Dual Encoder: Exploiting the Potential of Syntactic and Semantic for Aspect Sentiment Triplet Extraction (2024.lrec-main)
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| Challenge: | Aspect Sentiment Triple Extraction (ASTE) is an advanced natural language processing task. |
| Approach: | They propose a Dual Encoder: Exploiting the potential of Syntactic and Semantic model which maximizes syntactical and semantic relationships among words. |
| Outcome: | The proposed model surpasses the current state-of-the-art on public benchmarks and shows that it is highly efficient. |
Structural Bias for Aspect Sentiment Triplet Extraction (2022.coling-1)
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| Challenge: | Existing structural bias adapters for aspect sentiment triplet extraction are under-confident . a large-scale dataset for ASTE shows the adapter is effective and efficient to a larger scale. |
| Approach: | They propose to use a structural adapter to integrate structural bias into pretrained language models . they propose to add a relative position structure in place of the syntactic dependency structure . |
| Outcome: | The proposed adapter achieves state-of-the-art performance over strong baselines, but with a light parameter demand and low latency. |
A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction (2022.coling-1)
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| Challenge: | Existing methods are poor at detecting complicated relations between aspects and opinions . detecting unclear boundaries of multi-word aspects and opinion is also a challenge . |
| Approach: | They propose a multi-task dual-tree network to extract triplets from a given sentence . they employ a constituency tree and a modified dependency tree to enhance the interaction . |
| Outcome: | The proposed model extracts triplets from a given sentence, and it is effective on four datasets. |
PASTE: A Tagging-Free Decoding Framework Using Pointer Networks for Aspect Sentiment Triplet Extraction (2021.emnlp-main)
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| Challenge: | Existing methods for tagging opinion triplets fail to capture the strong interdependence between the three opinion factors, whereas grid tabbing fails to capture span-level semantics while predicting sentiment between an aspect-opinion pair. |
| Approach: | They propose a tagging-free approach to extracting opinion triplets using a pointer network decoding framework that captures the interdependence between the three elements of an opinion triple. |
| Outcome: | The proposed architecture captures the interdependence between the aspect and opinion triplets while predicting their connecting sentiment. |
Hybrid of Spans and Table-Filling for Aspect-Level Sentiment Triplet Extraction (2024.lrec-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is an emerging task in sentiment analysis research. |
| Approach: | They propose a model which combines span with table-filling to extract triplets from words . they use syntactic and contextual features to generate word-pair tables and convert them to span tables . |
| Outcome: | The proposed model achieves competitive results on a dataset with a large dataset. |
Target-to-Source Augmentation for Aspect Sentiment Triplet Extraction (2023.emnlp-main)
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| Challenge: | Aspect Sentiment Triplet Extraction (ASTE) is an important task in sentiment analysis, but data scarcity limits performance of existing methods. |
| Approach: | They propose a target-to-source augmentation approach to alleviate the issue of data scarcity in Aspect Sentiment Triplet Extraction (ASTE) they use fluency and alignment discriminators to provide feedback and use this feedback to optimize the generator. |
| Outcome: | The proposed approach significantly improves the performance of existing methods. |
CONTRASTE: Supervised Contrastive Pre-training With Aspect-based Prompts For Aspect Sentiment Triplet Extraction (2023.findings-emnlp)
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| Challenge: | Existing studies on Aspect Sentiment Triplet Extraction focus on developing more efficient techniques for the task, but our proposed approach can improve the downstream performance of multiple ABSA tasks simultaneously. |
| Approach: | They propose a novel approach that uses contrastive learning to enhance the ASTE performance by masked sentiments. |
| Outcome: | The proposed approach improves the performance of multiple ABSA tasks simultaneously. |
Polish-ASTE: Aspect-Sentiment Triplet Extraction Datasets for Polish (2024.lrec-main)
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| Challenge: | Aspect-Sentiment Triplet Extraction (ASTE) is one of the most challenging and complex tasks in sentiment analysis. |
| Approach: | They propose to use customer opinions of hotels and purchased products in Polish to extract ASTE triplets that contain an aspect, its associated sentiment polarity, and an opinion phrase that serves as a rationale for the assigned polarities. |
| Outcome: | The proposed datasets contain customer opinions about hotels and purchased products expressed in Polish and are available under a permissive licence and have the same file format as the English datasets. |
PASTEL : Polarity-Aware Sentiment Triplet Extraction with LLM-as-a-Judge (2025.findings-acl)
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| Challenge: | Existing methods for extracting triplets of aspect terms and opinions are inadequate due to complexity of aspect-opinion interactions and implicit nature of sentiment dependencies in natural language. |
| Approach: | They propose a pipeline that decomposes the ASTE task into structured subtasks . they employ fine-tuned LLMs to separately extract the aspect and opinion terms . |
| Outcome: | The proposed pipeline outperforms existing baselines in the ASTE subtask. |
CLAOCS-TX: Cross-Lingual Triplet Extraction with Aspect-Opinion-Aware Code-Switched Prompting and LLM-Guided Contrastive Distillation (2026.acl-long)
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| Challenge: | Existing work on cross-lingual aspect–opinion–sentiment triplet extraction has focused on coarse-grained sentiment classification or aspect extraction. |
| Approach: | They propose a framework that leverages large language models as pseudo-label generators and semantic teachers for ASTE. |
| Outcome: | The proposed framework generates reliable pseudo triplets for unlabeled languages, while maintaining high-confidence supervision. |