ZhichunRoad at Amazon KDD Cup 2022: MultiTask Pre-Training for E-Commerce Product Search

• URL: http://arxiv.org/abs/2301.13455v1
• Date: Tue, 31 Jan 2023 07:31:34 GMT
• Title: ZhichunRoad at Amazon KDD Cup 2022: MultiTask Pre-Training for E-Commerce Product Search
• Authors: Xuange Cui, Wei Xiong, Songlin Wang
• Abstract summary: We propose a robust multilingual model to improve the quality of search results. In pre-training stage, we adopt mlm task, classification task and contrastive learning task. In fine-tuning stage, we use confident learning, exponential moving average method (EMA), adversarial training (FGM) and regularized dropout strategy (R-Drop)
• Score: 4.220439000486713
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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