A Re-ranking Model for Dependency Parser with Recursive Convolutional Neural Network

In this work, we address the problem to model all the nodes (words or phrases) in a dependency tree with the dense representations. We propose a recursive convolutional neural network (RCNN) architecture to capture syntactic and compositional-semantic representations of phrases and words in a dependency tree. Different with the original recursive neural network, we introduce the convolution and pooling layers, which can model a variety of compositions by the feature maps and choose the most informative compositions by the pooling layers. Based on RCNN, we use a discriminative model to re-rank a $k$-best list of candidate dependency parsing trees. The experiments show that RCNN is very effective to improve the state-of-the-art dependency parsing on both English and Chinese datasets

Learning "O" Helps for Learning More: Handling the Concealed Entity Problem for Class-incremental NER

As the categories of named entities rapidly increase in real-world applications, class-incremental learning for NER is in demand, which continually learns new entity classes while maintaining the old knowledge. Due to privacy concerns and storage constraints, the model is required to update without any annotations of the old entity classes. However, in each step on streaming data, the "O" class in each step might contain unlabeled entities from the old classes, or potential entities from the incoming classes. In this work, we first carry out an empirical study to investigate the concealed entity problem in class-incremental NER. We find that training with "O" leads to severe confusion of "O" and concealed entity classes, and harms the separability of potential classes. Based on this discovery, we design a rehearsal-based representation learning approach for appropriately learning the "O" class for both old and potential entity classes. Additionally, we provide a more realistic and challenging benchmark for class-incremental NER which introduces multiple categories in each step. Experimental results verify our findings and show the effectiveness of the proposed method on the new benchmark

Task-oriented Dialogue System for Automatic Disease Diagnosis via Hierarchical Reinforcement Learning

In this paper, we focus on automatic disease diagnosis with reinforcement learning (RL) methods in task-oriented dialogues setting. Different from conventional RL tasks, the action space for disease diagnosis (i.e., symptoms) is inevitably large, especially when the number of diseases increases. However, existing approaches to this problem employ a flat RL policy, which typically works well in simple tasks but has significant challenges in complex scenarios like disease diagnosis. Towards this end, we propose to integrate a hierarchical policy of two levels into the dialogue policy learning. The high level policy consists of a model named master that is responsible for triggering a model in low level, the low level policy consists of several symptom checkers and a disease classifier. Experimental results on both self-constructed real-world and synthetic datasets demonstrate that our hierarchical framework achieves higher accuracy in disease diagnosis compared with existing systems. Besides, the datasets (http://www.sdspeople.fudan.edu.cn/zywei/data/Fudan-Medical-Dialogue2.0) and codes (https://github.com/nnbay/MeicalChatbot-HRL) are all available now