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Evaluating Span Extraction in Generative Paradigm: A Reflection on Aspect-Based Sentiment Analysis

Soyoung Yang, Won Ik Cho

TL;DR

This paper addresses ABSA evaluation in the era of generative language models, arguing that exact-match based metrics fail to capture span-level and quadruple predictions. It surveys ABSA tasks and the ASQP quadruple representation, discusses NULL handling, and explains how GLMs blur the lines between extraction and generation. Through comparison of evaluation schemes and a case study, it highlights strengths and pitfalls and refrains from prescribing a single metric. The authors propose future directions, including partial-match metrics, element-wise vs quadruple-level aggregation, and cautious use of NLG-based similarity metrics to reflect generative capabilities.

Abstract

In the era of rapid evolution of generative language models within the realm of natural language processing, there is an imperative call to revisit and reformulate evaluation methodologies, especially in the domain of aspect-based sentiment analysis (ABSA). This paper addresses the emerging challenges introduced by the generative paradigm, which has moderately blurred traditional boundaries between understanding and generation tasks. Building upon prevailing practices in the field, we analyze the advantages and shortcomings associated with the prevalent ABSA evaluation paradigms. Through an in-depth examination, supplemented by illustrative examples, we highlight the intricacies involved in aligning generative outputs with other evaluative metrics, specifically those derived from other tasks, including question answering. While we steer clear of advocating for a singular and definitive metric, our contribution lies in paving the path for a comprehensive guideline tailored for ABSA evaluations in this generative paradigm. In this position paper, we aim to provide practitioners with profound reflections, offering insights and directions that can aid in navigating this evolving landscape, ensuring evaluations that are both accurate and reflective of generative capabilities.

Evaluating Span Extraction in Generative Paradigm: A Reflection on Aspect-Based Sentiment Analysis

TL;DR

This paper addresses ABSA evaluation in the era of generative language models, arguing that exact-match based metrics fail to capture span-level and quadruple predictions. It surveys ABSA tasks and the ASQP quadruple representation, discusses NULL handling, and explains how GLMs blur the lines between extraction and generation. Through comparison of evaluation schemes and a case study, it highlights strengths and pitfalls and refrains from prescribing a single metric. The authors propose future directions, including partial-match metrics, element-wise vs quadruple-level aggregation, and cautious use of NLG-based similarity metrics to reflect generative capabilities.

Abstract

In the era of rapid evolution of generative language models within the realm of natural language processing, there is an imperative call to revisit and reformulate evaluation methodologies, especially in the domain of aspect-based sentiment analysis (ABSA). This paper addresses the emerging challenges introduced by the generative paradigm, which has moderately blurred traditional boundaries between understanding and generation tasks. Building upon prevailing practices in the field, we analyze the advantages and shortcomings associated with the prevalent ABSA evaluation paradigms. Through an in-depth examination, supplemented by illustrative examples, we highlight the intricacies involved in aligning generative outputs with other evaluative metrics, specifically those derived from other tasks, including question answering. While we steer clear of advocating for a singular and definitive metric, our contribution lies in paving the path for a comprehensive guideline tailored for ABSA evaluations in this generative paradigm. In this position paper, we aim to provide practitioners with profound reflections, offering insights and directions that can aid in navigating this evolving landscape, ensuring evaluations that are both accurate and reflective of generative capabilities.
Paper Structure (22 sections, 1 figure, 2 tables)

This paper contains 22 sections, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Four elements of ABSA
  4. Subtasks of ABSA
  5. ABSA Models in Paradigm Shift
  6. Inference De Facto
  7. Recent Attempts with GLM
  8. Evaluation Schemes
  9. ABSA Evaluation Schemes
  10. Studies on NLG Evaluation
  11. Discussion
  12. Comparison of Evaluation Schemes
  13. Using Exact Match vs. Partial Match
  14. Total vs. Element-Wise Evaluation
  15. NLG Metrics
  16. ...and 7 more sections

Figures (1)

  • Figure 1: Aspect sentiment quad prediction (ASQP) examples from ACOS cai-2021-acos rest16 dataset. Each quadruple is extracted from a given sentence in the order of (aspect term $a$, aspect category $c$, opinion term $o$, sentiment polarity $s$). Example (A) is an explicit case where the mentions of aspect and opinion terms are described in the given sentence, while (B) is an implicit one where the aspect term is not found in the sentence.