This paper discusses the emerging challenges in evaluating aspect-based sentiment analysis (ABSA) in the context of the generative paradigm. It highlights the complexities introduced by the transition from traditional extract-and-classify approaches to generative models.
The paper first provides background on the ABSA task, including the four key elements (aspect term, aspect category, opinion term, and sentiment polarity) and the various subtasks involved. It then examines the shift in ABSA inference methodologies, from the traditional bifurcation of extraction and classification to the more recent adoption of generative language models.
The core of the discussion focuses on evaluating ABSA outputs in the generative paradigm. The authors explore the limitations of existing evaluation schemes, such as exact match and F1 metrics, and the need for more lenient alternatives like partial match and semantic similarity measures. They also delve into the challenges of assessing multiple predictions against multiple ground truths, and the implications of diverse responses from generative models.
The paper compares various evaluation approaches, including total vs. element-wise assessment, and the potential role of natural language generation (NLG) metrics. It provides a detailed case study to illustrate the nuances and trade-offs of different evaluation schemes.
Finally, the authors offer suggestions for the future direction of ABSA evaluation in the generative paradigm. They emphasize the need for a balanced approach that considers the unique characteristics of each ABSA element, the incorporation of partial match and semantic similarity metrics, and the potential application of NLG evaluation techniques in specific scenarios.
Overall, this position paper aims to provide practitioners with profound reflections and insights to navigate the evolving landscape of ABSA evaluation, ensuring assessments that are both accurate and reflective of generative capabilities.
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by Soyoung Yang... في arxiv.org 04-18-2024
https://arxiv.org/pdf/2404.11539.pdfاستفسارات أعمق