Evaluating Span Extraction in Generative Paradigm: A Reflection on Aspect-Based Sentiment Analysis
It tackles evaluation problems for practitioners in natural language processing, but it is incremental as it builds on existing practices without introducing new methods or data.
This paper addresses the challenges of evaluating aspect-based sentiment analysis (ABSA) in the generative paradigm, highlighting issues with aligning generative outputs to existing metrics and proposing guidelines for more accurate evaluations.
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.