ChengYan Wu, Yiqiang Cai, Yang Liu, Pengxu Zhu, Yun Xue, Ziwei Gong, Julia Hirschberg, Bolei Ma. Findings of the Association for Computational Linguistics: EMNLP 2025. 2025.

Targeted sentiment classification aims to predict the emotional trend of a specific goal. Currently, most methods (e.g., recurrent neural networks and convolutional neural networks combined with an attention mechanism) are not able to fully capture the semantic information of the context and they also lack a mechanism to explain the relevant syntactical constraints and long-range word dependencies. Therefore, syntactically irrelevant context words may mistakenly be recognized as clues to predict the target sentiment. To tackle these problems, this paper considers that the semantic information, syntactic information, and their interaction information are very crucial to targeted sentiment analysis, and propose an attentional-encoding-based graph convolutional network (AEGCN) model. Our proposed model is mainly composed of multi-head attention and an improved graph convolutional network built over the dependency tree of a sentence. Pre-trained BERT is applied to this task, and new state-of-art performance is achieved. Experiments on five datasets show the effectiveness of the model proposed in this paper compared with a series of the latest models.

In this letter, we study the deep reinforcement learning (DRL)-based routing, modulation and spectrum assignment problem in the elastic optical networks. We emphasize the importance of proper reward design of the DRL framework and we propose to include some heuristic information to the reward design. This introduction of human knowledge to the machine learning, is to reduce the exploration blindness of the latter and lead to more efficient learning of better policies. We make it clear that what kind of heuristic information should be included in the reward design is an open question. Specifically, we propose to consider the spectrum fragmentation level of each candidate path as the heuristic information. As a result, the DRL agent is more inclined to choose the candidate path that leads to lower spectrum fragmentation level, which is more friendly for future traffic requests. Simulation results show that the proposed heuristic reward design scheme outperforms both the simple-reward DRL based approaches and the heuristic rule-based approaches.