Hai-Tao Yu

Hai-Tao Yu

Deep neural networks has become the first choice for researchers working on algorithmic aspects of learning-to-rank. Unfortunately, it is not trivial to find the optimal setting of hyper-parameters that achieves the best ranking performance. As a result, it becomes more and more difficult to develop a new model and conduct a fair comparison with prior methods, especially for newcomers. In this work, we propose PT-Ranking, an open-source project based on PyTorch for developing and evaluating learning-to-rank methods using deep neural networks as the basis to construct a scoring function. On one hand, PT-Ranking includes many representative learning-to-rank methods. Besides the traditional optimization framework via empirical risk minimization, adversarial optimization framework is also integrated. Furthermore, PT-Ranking's modular design provides a set of building blocks that users can leverage to develop new ranking models. On the other hand, PT-Ranking supports to compare different learning-to-rank methods based on the widely used datasets (e.g., MSLR-WEB30K, Yahoo!LETOR and Istella LETOR) in terms of different metrics, such as precision, MAP, nDCG, nERR. By randomly masking the ground-truth labels with a specified ratio, PT-Ranking allows to examine to what extent the ratio of unlabelled query-document pairs affects the performance of different learning-to-rank methods. We further conducted a series of demo experiments to clearly show the effect of different factors on neural learning-to-rank methods, such as the activation function, the number of layers and the optimization strategy.

Yao Wang, Xin Liu, Weikun Kong et al.

Named Entity Recognition and Relation Extraction are two crucial and challenging subtasks in the field of Information Extraction. Despite the successes achieved by the traditional approaches, fundamental research questions remain open. First, most recent studies use parameter sharing for a single subtask or shared features for both two subtasks, ignoring their semantic differences. Second, information interaction mainly focuses on the two subtasks, leaving the fine-grained informtion interaction among the subtask-specific features of encoding subjects, relations, and objects unexplored. Motivated by the aforementioned limitations, we propose a novel model to jointly extract entities and relations. The main novelties are as follows: (1) We propose to decouple the feature encoding process into three parts, namely encoding subjects, encoding objects, and encoding relations. Thanks to this, we are able to use fine-grained subtask-specific features. (2) We propose novel inter-aggregation and intra-aggregation strategies to enhance the information interaction and construct individual fine-grained subtask-specific features, respectively. The experimental results demonstrate that our model outperforms several previous state-of-the-art models. Extensive additional experiments further confirm the effectiveness of our model.

Hai-Tao Yu

Learning-to-rank has been intensively studied and has shown significantly increasing values in a wide range of domains. The performance of learning-to-rank methods is commonly evaluated using rank-sensitive metrics, such as average precision (AP) and normalized discounted cumulative gain (nDCG). Unfortunately, how to effectively optimize rank-sensitive objectives is far from being resolved, which has been an open problem since the dawn of learning-to-rank over a decade ago. In this paper, we introduce a simple yet effective framework for directly optimizing information retrieval (IR) metrics. Specifically, we propose a novel twin-sigmoid function for deriving the exact rank positions of documents during the optimization process instead of using approximated rank positions or relying on the traditional sorting algorithms. Thanks to this, the rank positions are differentiable, enabling us to reformulate the widely used IR metrics as differentiable ones and directly optimize them based on neural networks. Furthermore, by carrying out an in-depth analysis of the gradients, we pinpoint the potential limitations inherent with direct optimization of IR metrics based on the vanilla sigmoid. To break the limitations, we propose different strategies by explicitly modifying the gradient computation. To validate the effectiveness of the proposed framework for direct optimization of IR metrics, we conduct a series of experiments on the widely used benchmark collection MSLRWEB30K.