面向产品属性-值识别的生成式方法实证比较
An Empirical Comparison of Generative Approaches for Product Attribute-Value Identification
AI总结：
本文将产品属性-值识别形式化为生成任务，在三个数据集上系统比较三种属性-值生成策略，发现端到端方法计算效率高且效果最优，并公开了复现代码。
摘要：
产品属性对电子商务平台至关重要，支撑着搜索、推荐和问答等应用。产品属性与值识别（PAVI）任务涉及从产品信息中识别属性及其对应的值。本文将 PAVI 形式化为一个生成任务，并据我们所知提供了迄今为止最全面的 PAVI 评估。我们在三个数据集上基于微调编码器-解码器模型，比较了三种不同的属性-值生成（AVG）策略。实验表明，端到端 AVG 方法计算效率高，且优于其他策略。然而，不同模型规模和底层语言模型之间存在差异。复现所有实验的代码可在以下地址获取：https://github.com/kassemsabeh/pavi-avg
英文摘要：
Product attributes are crucial for e-commerce platforms, supporting applications like search, recommendation, and question answering. The task of Product Attribute and Value Identification (PAVI) involves identifying both attributes and their values from product information. In this paper, we formulate PAVI as a generation task and provide, to the best of our knowledge, the most comprehensive evaluation of PAVI so far. We compare three different attribute-value generation (AVG) strategies based on fine-tuning encoder-decoder models on three datasets. Experiments show that end-to-end AVG approach, which is computationally efficient, outperforms other strategies. However, there are differences depending on model sizes and the underlying language model. The code to reproduce all experiments is available at: https://github.com/kassemsabeh/pavi-avg
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
