Papers with COQE

4 papers
COF: Adaptive Chain of Feedback for Comparative Opinion Quintuple Extraction (2025.coling-main)

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Challenge: Comparative Opinion Quintuple Extraction (COQE) aims to extract all comparative sentiment quintuples from product review text.
Approach: They propose a model-unaware adaptive chain-of-feedback method to extract quintuples from product review text.
Outcome: The proposed method improves performance on three benchmarks.
Low-Resource Comparative Opinion Quintuple Extraction by Data Augmentation with Prompting (2023.findings-emnlp)

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Challenge: Comparative Opinion Quintuple Extraction (COQE) aims to predict comparative opinion quintuples from comparative sentences.
Approach: They propose a low-resource approach to extract comparative opinion quintuples from comparative sentences . they propose augmentation using ChatGPT and a data-centric approach .
Outcome: The proposed approach improves the existing pipeline-based method and achieves state-of-the-art results.
Comparative Opinion Quintuple Extraction from Product Reviews (2021.emnlp-main)

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Challenge: Comparative opinion mining is an important task in opinion mining.
Approach: They propose a task to extract comparative opinion quintuples from product reviews . they propose supplementary annotations and construct three datasets for the task .
Outcome: The proposed method outperforms baseline systems on three datasets and represents a strong benchmark for COQE.
UniCOQE: Unified Comparative Opinion Quintuple Extraction As A Set (2023.findings-acl)

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Challenge: Existing methods decompose the COQE task into multiple subtasks and solve them in a pipeline manner, but ignore the intrinsic connection between subtask and the error propagation among stages.
Approach: They propose a unified generative model that solves COQE in one shot by concatenating all the comparative tuples into a target output sequence.
Outcome: The proposed model significantly outperforms the SOTA method on multiple benchmarks and ablation experiments.

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