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E2TP: Element to Tuple Prompting Improves Aspect Sentiment Tuple Prediction

Mohammad Ghiasvand Mohammadkhani, Niloofar Ranjbar, Saeedeh Momtazi

TL;DR

This paper introduces E2TP, a two-step prompting framework for ABSA that first predicts single elements and then maps them to full sentiment tuples. It defines three paradigms—$diet$, $f_1$, and $f_2$—with distinct template styles and data-augmentation strategies, including cross-domain BGCA integration. Across TASD, ASTE, ASQP, and ACOS tasks, E2TP achieves new state-of-the-art results in most settings, notably improving over strong baselines and outperforming cross-domain methods. The approach offers data-efficient and highly augmentable configurations, highlighting the practical benefits of decomposing complex tuple predictions into manageable subproblems and demonstrating resilience to cross-domain challenges. The work suggests broad applicability of element-to-tuple prompting beyond ABSA and provides detailed analyses of first- and second-step behaviors and case studies to illustrate robustness and limitations.

Abstract

Generative approaches have significantly influenced Aspect-Based Sentiment Analysis (ABSA), garnering considerable attention. However, existing studies often predict target text components monolithically, neglecting the benefits of utilizing single elements for tuple prediction. In this paper, we introduce Element to Tuple Prompting (E2TP), employing a two-step architecture. The former step focuses on predicting single elements, while the latter step completes the process by mapping these predicted elements to their corresponding tuples. E2TP is inspired by human problem-solving, breaking down tasks into manageable parts, using the first step's output as a guide in the second step. Within this strategy, three types of paradigms, namely E2TP($diet$), E2TP($f_1$), and E2TP($f_2$), are designed to facilitate the training process. Beyond dataset-specific experiments, our paper addresses cross-domain scenarios, demonstrating the effectiveness and generalizability of the approach. By conducting a comprehensive analysis on various benchmarks, we show that E2TP achieves new state-of-the-art results in nearly all cases.

E2TP: Element to Tuple Prompting Improves Aspect Sentiment Tuple Prediction

TL;DR

This paper introduces E2TP, a two-step prompting framework for ABSA that first predicts single elements and then maps them to full sentiment tuples. It defines three paradigms—, , and —with distinct template styles and data-augmentation strategies, including cross-domain BGCA integration. Across TASD, ASTE, ASQP, and ACOS tasks, E2TP achieves new state-of-the-art results in most settings, notably improving over strong baselines and outperforming cross-domain methods. The approach offers data-efficient and highly augmentable configurations, highlighting the practical benefits of decomposing complex tuple predictions into manageable subproblems and demonstrating resilience to cross-domain challenges. The work suggests broad applicability of element-to-tuple prompting beyond ABSA and provides detailed analyses of first- and second-step behaviors and case studies to illustrate robustness and limitations.

Abstract

Generative approaches have significantly influenced Aspect-Based Sentiment Analysis (ABSA), garnering considerable attention. However, existing studies often predict target text components monolithically, neglecting the benefits of utilizing single elements for tuple prediction. In this paper, we introduce Element to Tuple Prompting (E2TP), employing a two-step architecture. The former step focuses on predicting single elements, while the latter step completes the process by mapping these predicted elements to their corresponding tuples. E2TP is inspired by human problem-solving, breaking down tasks into manageable parts, using the first step's output as a guide in the second step. Within this strategy, three types of paradigms, namely E2TP(), E2TP(), and E2TP(), are designed to facilitate the training process. Beyond dataset-specific experiments, our paper addresses cross-domain scenarios, demonstrating the effectiveness and generalizability of the approach. By conducting a comprehensive analysis on various benchmarks, we show that E2TP achieves new state-of-the-art results in nearly all cases.
Paper Structure (29 sections, 4 equations, 5 figures, 6 tables)

This paper contains 29 sections, 4 equations, 5 figures, 6 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Aspect-based Sentiment Analysis
  4. Generative ABSA
  5. Other ABSA Approaches
  6. Cross-domain ABSA
  7. Methodology
  8. Problem Definition
  9. Training
  10. First Step Input and Target Format
  11. Second Step Input and Target Format
  12. Paradigms:
  13. Cross-domain
  14. Inference
  15. Experiments
  16. ...and 14 more sections

Figures (5)

  • Figure 1: E2TP Framework Illustration. † indicates prompt elements permutation (1st fixed) described in section \ref{['sec:pepf']}
  • Figure 2: Propagated error effect
  • Figure 3: Pure analysis of second step model
  • Figure 4: Results of the first-step model in aspect term extraction (ATE), opinion term extraction (OTE), aspect category detection (ACD), and sentiment polarity detection (SPD) subtasks.
  • Figure 4: The case study of E2TP presents the input, model outputs from both steps, and the gold output.