Unlike traditional learning to rank models that depend on hand-crafted
features, neural representation learning models learn higher level features for
the ranking task by training on large datasets. Their ability to learn new
features directly from the data, however, may come at a price. Without any
special supervision, these models learn relationships that may hold only in the
domain from which the training data is sampled, and generalize poorly to
domains not observed during training. We study the effectiveness of adversarial
learning as a cross domain regularizer in the context of the ranking task. We
use an adversarial discriminator and train our neural ranking model on a small
set of domains. The discriminator provides a negative feedback signal to
discourage the model from learning domain specific representations. Our
experiments show consistently better performance on held out domains in the
presence of the adversarial discriminator---sometimes up to 30% on precision@1.

通过使用对抗生成网络作为跨领域正则化器，研究利用其在排名任务的效果来消除神经表示学习模型在训练过程中学习到的领域特定表示形式，从而在领域之间更好地泛化。