Summary of An Empirical Comparison Of Generative Approaches For Product Attribute-value Identification, by Kassem Sabeh et al.
An Empirical Comparison of Generative Approaches for Product Attribute-Value Identification
by Kassem Sabeh, Robert Litschko, Mouna Kacimi, Barbara Plank, Johann Gamper
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel approach to identify product attributes and their corresponding values, a crucial task for e-commerce platforms. The authors formulate this problem as a generation task and provide the most comprehensive evaluation of Product Attribute and Value Identification (PAVI) yet. They compare three different strategies for attribute-value generation (AVG), fine-tuning encoder-decoder models on three datasets. Results show that the end-to-end AVG approach, which is computationally efficient, outperforms other methods. However, there are differences depending on model sizes and underlying language models. The code to reproduce all experiments is available online. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to identify important information about products, like their features and characteristics. This matters because it can improve search and recommendation systems for online shopping. The authors compared different ways to do this using special computer models. They found that one approach was the best and most efficient. However, they also discovered that the results depend on the size of the model and its foundation in language. You can access the code used in their experiments online. |
Keywords
» Artificial intelligence » Encoder decoder » Fine tuning
