An Empirical Comparison of Generative Approaches for Product Attribute-Value Identification

TL;DR

This paper empirically evaluates different generative methods for Product Attribute-Value Identification, demonstrating that an end-to-end approach with encoder-decoder models is most effective across multiple datasets.

Contribution

It introduces a comprehensive evaluation of generative strategies for PAVI, highlighting the superiority of end-to-end models and analyzing effects of model size and architecture.

Findings

End-to-end AVG approach outperforms other strategies.

Model size and architecture influence performance.

The study provides a benchmark for future PAVI research.

Abstract

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