BERT Goes Shopping: Comparing Distributional Models for Product Representations

Federico Bianchi, Bingqing Yu, Jacopo Tagliabue


Abstract
Word embeddings (e.g., word2vec) have been applied successfully to eCommerce products through prod2vec. Inspired by the recent performance improvements on several NLP tasks brought by contextualized embeddings, we propose to transfer BERT-like architectures to eCommerce: our model - Prod2BERT - is trained to generate representations of products through masked session modeling. Through extensive experiments over multiple shops, different tasks, and a range of design choices, we systematically compare the accuracy of Prod2BERT and prod2vec embeddings: while Prod2BERT is found to be superior in several scenarios, we highlight the importance of resources and hyperparameters in the best performing models. Finally, we provide guidelines to practitioners for training embeddings under a variety of computational and data constraints.
Anthology ID:
2021.ecnlp-1.1
Volume:
Proceedings of The 4th Workshop on e-Commerce and NLP
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | ECNLP | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–12
Language:
URL:
https://aclanthology.org/2021.ecnlp-1.1
DOI:
10.18653/v1/2021.ecnlp-1.1
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/2021.ecnlp-1.1.pdf