Extreme Multi-label text Classification (XMC) aims to find the most relevant labels (i.e., the positives) for a document from an extremely large label set.The remaining labels are regarded as the negatives.Recently, the deep learning-based methods have been widely used to solve XMC, most of which use sigmoid as the activation function of output layer and use binary cross entropy loss as the learning objective.However, the existing methods suffer from the following limitations.First, the score of each label is predicted independently, where the label rank-missing problem is ignored.Second, the cardinalities of the positives and the negatives are extremely unbalanced in XMC, which makes the classifier more biased towards the majority one.In this paper, we use label group to denote the positive and the negative labels and propose a novel XMC model leveraging Group-wise label Ranking (X-GRank) to address those limitations.Specifically, X-GRank uses the newly proposed GRank loss to rank the label groups.Then, X-GRank solves the label imbalance problem by constraining the backward gradient amplitude between label groups.Extensive experiments show that X-GRank outperforms the state-of-the-art methods on five widely used datasets.

Nowadays, there are more and more papers submitted to various periodicals and conferences. Typically, reviewers need to read through the paper and give a review comment and score to it based on somehow certain criterion. This review process is labor intensive and time-consuming. Recently, AI technology is widely used to alleviate human labor burden. Can machine learn from human to review papers automatically? In this paper, we propose a collaborative grammar and innovation model - DeepReviewer to achieve automatic paper review. This model learning the semantic, grammar and innovative features of an article by three main well-designed components simultaneously. Moreover, these three factors are integrated by an attention layer to get the final review score of the paper. We crawled paper review data from Openreview and built a real data set. Experimental results demonstrate that our model exceeds many baselines.