DI2KG 2020 : Proceedings of the 2nd International Workshop on Challenges and Experiences from Data Integration to Knowledge Graphs co-located with 46th International Conference on Very Large Data Bases (VLDB 2020), Tokyo, Japan, August 31, 2020
Transformer-based models like BERT have pushed the state-of the-art for a wide range of tasks in natural language processing.
General-purpose pre-training on large corpora allows Transformers to yield good performance even with small amounts of training data for task-specific fine-tuning. In this work, we apply BERT to the task of product matching in e-commerce and show that BERT is much more training data efficient than other state-of-the-art methods. Moreover, we show that we can further boost its effectiveness through an intermediate training step, exploiting large collections of product offers. Our intermediate training leads to strong performance (>90% F1) on new, unseen products without any product-specific fine-tuning. Further fine-tuning yields additional gains, resulting in improvements of up to 12% F1 for small training sets. Adding the masked language modeling objective in the intermediate training step in order to further adapt the language model to the application domain leads to an additional increase of up to 3% F1.
Dieser Eintrag ist Teil der Universitätsbibliographie.
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