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A Fast and Simple Model in Practice for Ranking

Recent work has shown success in using neural network as ranking model in question answering(QA). While the precision of these models has increased significantly over time, most models need long training time, which leads to it cannot be used effectively in practical applications. In this paper, we propose a simple model to accelerate training speed with noise-contrastive estimation. This model mainly inspired by an architecture based entirely on convolutional neural networks and noise-contrastive estimation. We investigate the behavior of the model on the financial dataset which we use in practice and show that it reduces the training times by more than an order of magnitude without affecting the quality of the resulting models. The model is also more efficient and more stable than importance sampling because it requires fewer noise samples to perform well.