Paper Info
Title
Masked-field Pre-training for User Intent Prediction
Abstract
For many applications, predicting the users' intents can help the system provide the solutions or recommendations to the users. It improves the user experience, and brings economic benefits. The main challenge of user intent prediction is that we lack enough labeled data for training, and some intents (labels) are sparse in the training set. This is a general problem for many real-world prediction tasks. To overcome data sparsity, we propose a masked-field pre-training framework. In pre-training, we exploit massive unlabeled data to learn useful feature interaction patterns. We do this by masking partial field features, and learning to predict them from other unmasked features. We then finetune the pre-trained model for the target intent prediction task. This framework can be used to train various deep models. In the intent prediction task, each intent is only relevant to partial features. To tackle this problem, we propose a Field-Independent Transformer network. This network generates separate representation for each field, and aggregates the relevant field representations with attention mechanism for each intent. We test our method on intent prediction datasets in customer service scenarios as well as several public datasets. The results show that the masked-field pre-training framework significantly improves the prediction precision for deep models. And the Field-Independent Transformer network trained with the masked-field pre-training framework outperforms the state-of-the-art methods in the user intent prediction.
Year
DOI
Venue
2020
10.1145/3340531.3412726
CIKM '20: The 29th ACM International Conference on Information and Knowledge Management Virtual Event Ireland October, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-6859-9
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Peng Wang100.34
Jiang Xu200.34
Chunyi Liu371.76
Hao Feng440932.15
Zang Li515310.80
Jieping Ye66943351.37