论文标题
使用对抗训练和鲁棒建模技术预测查询项目的关系
Predicting Query-Item Relationship using Adversarial Training and Robust Modeling Techniques
论文作者
论文摘要
我们提出了一种预测搜索查询项目关系的有效方法。我们结合了预训练的变压器和LSTM模型，并使用对抗性训练，指数移动平均值，多样采样的辍学和基于多样性的集合来提高模型鲁棒性，以解决一个非常困难的问题，即预测以前从未见过的查询。我们所有的策略都集中在提高深度学习模型的鲁棒性上，并适用于使用深度学习模型的任何任务。采用我们的策略，我们在KDD CUP 2022产品替换分类任务中获得了第十名。
We present an effective way to predict search query-item relationship. We combine pre-trained transformer and LSTM models, and increase model robustness using adversarial training, exponential moving average, multi-sampled dropout, and diversity based ensemble, to tackle an extremely difficult problem of predicting against queries not seen before. All of our strategies focus on increasing robustness of deep learning models and are applicable in any task where deep learning models are used. Applying our strategies, we achieved 10th place in KDD Cup 2022 Product Substitution Classification task.
