An Empirical Comparison of Generative Approaches for Product Attribute-Value Identification
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866909235632144384 |
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| author | Sabeh, Kassem Litschko, Robert Kacimi, Mouna Plank, Barbara Gamper, Johann |
| author_facet | Sabeh, Kassem Litschko, Robert Kacimi, Mouna Plank, Barbara Gamper, Johann |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01137 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | An Empirical Comparison of Generative Approaches for Product Attribute-Value Identification Sabeh, Kassem Litschko, Robert Kacimi, Mouna Plank, Barbara Gamper, Johann Computation and Language Artificial Intelligence 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 |
| title | An Empirical Comparison of Generative Approaches for Product Attribute-Value Identification |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2407.01137 |