In this paper, we propose a robust multilingual model to improve the quality of search results. Our model not only leverage the processed class-balanced dataset, but also benefit from multitask pre-training that leads to more general representations. In pre-training stage, we adopt mlm task, classification task and contrastive learning task to achieve considerably performance. In fine-tuning stage, we use confident learning, exponential moving average method (EMA), adversarial training (FGM) and regularized dropout strategy (R-Drop) to improve the model's generalization and robustness. Moreover, we use a multi-granular semantic unit to discover the queries and products textual metadata for enhancing the representation of the model. Our approach obtained competitive results and ranked top-8 in three tasks. We release the source code and pre-trained models associated with this work.
@article{arxiv.2301.13455,
title = {ZhichunRoad at Amazon KDD Cup 2022: MultiTask Pre-Training for E-Commerce Product Search},
author = {Xuange Cui and Wei Xiong and Songlin Wang},
journal= {arXiv preprint arXiv:2301.13455},
year = {2023}
}