Modeling social media users is the core of social governance in the digital society. Existing works have incorporated different digital traces to better learn the representations of social media users, including text information encoded by pre-trained language models and social network information encoded by graph models. However, limited by overloaded text information and hard-to-collect social network information, they cannot utilize global text information and cannot be generalized without social relationships. In this paper, we propose a Pre-training Architecture for Social Media User Modeling based on Text Graph(PASUM). We aggregate all microblogs to represent social media users based on the text graph model and learn the mapping from microblogs to user representation. We further design inter-user and intra-user contrastive learning tasks to inject general structural information into the mapping. In different scenarios, we can represent users based on text, even without social network information. Experimental results on various downstream tasks demonstrate the effectiveness and superiority of our framework.

Dongkyu Lee, Zhiliang Tian, Lanqing Xue, Nevin L. Zhang. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Lanqing Xue, Kaitao Song, Duocai Wu, Xu Tan, Nevin L. Zhang, Tao Qin, Wei-Qiang Zhang, Tie-Yan Liu. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

Although deep neural networks generally have fixed network structures, the concept of dynamic mechanism has drawn more and more attention in recent years. Attention mechanisms compute input-dependent dynamic attention weights for aggregating a sequence of hidden states. Dynamic network configuration in convolutional neural networks (CNNs) selectively activates only part of the network at a time for different inputs. In this paper, we combine the two dynamic mechanisms for text classification tasks. Traditional attention mechanisms attend to the whole sequence of hidden states for an input sentence, while in most cases not all attention is needed especially for long sequences. We propose a novel method called Gated Attention Network (GA-Net) to dynamically select a subset of elements to attend to using an auxiliary network, and compute attention weights to aggregate the selected elements. It avoids a significant amount of unnecessary computation on unattended elements, and allows the model to pay attention to important parts of the sequence. Experiments in various datasets show that the proposed method achieves better performance compared with all baseline models with global or local attention while requiring less computation and achieving better interpretability. It is also promising to extend the idea to more complex attention-based models, such as transformers and seq-to-seq models.

Neural conversation models are known to generate appropriate but non-informative responses in general.A scenario where informativeness can be significantly enhanced is Conversing by Reading (CbR), where conversations take place with respect to a given external document.In previous work, the external document is utilized by (1) creating a contextaware document memory that integrates information from the document and the conversational context, and then (2) generating responses referring to the memory.In this paper, we propose to create the document memory with some anticipated responses in mind.This is achieved using a teacher-student framework.The teacher is given the external document, the context, and the ground-truth response, and learns how to build a response-aware document memory from three sources of information.The student learns to construct a response-anticipated document memory from the first two sources, and the teacher's insight on memory creation.Empirical results show that our model outperforms the previous stateof-the-art for the CbR task.