Accurate precipitation nowcasting is of great significance for flood prevention, agricultural production, and public safety. In recent years, spatiotemporal sequence models based on deep learning have been widely used for precipitation nowcasting and have achieved better prediction results than traditional methods. These models commonly use radar echo extrapolation and utilize the Z-R relationship between radar and rainfall to predict rainfall. However, radar echo data can be affected by various noises, and the Z-R correlation linking radar and rainfall encompasses several variables influenced by factors like terrain, climate, and seasonal variations. To solve this problem, we propose a dual-source attention dynamic neural network (DSADNet) for precipitation nowcasting, which is a network model that utilizes a fusion module to extract valid information from radar maps and rainfall maps, together with dynamic convolution and the attention mechanism, to directly predict future rainfall through encoding and decoding structure. We conducted experiments on a real dataset in Jiangsu, China, and the experimental results show that our model had better performance than the other examined models.

Humor is a special human expression style, an important “lubricant” for daily communication for people; people can convey emotional messages that are not easily expressed through humor. At present, artificial intelligence is one of the popular research domains; “discourse understanding” is also an important research direction, and how to make computers recognize and understand humorous expressions similar to humans has become one of the popular research domains for natural language processing researchers. In this paper, a humor recognition model (MLSN) based on current humor theory and popular deep learning techniques is proposed for the humor recognition task. The model automatically identifies whether a sentence contains humor expression by capturing the inconsistency, phonetic features, and ambiguity of a joke as semantic features. The model was experimented on three publicly available wisecrack datasets and compared with state-of-the-art language models, and the results demonstrate that the proposed model has better humor recognition accuracy and can contribute to the research on discourse understanding.

A pun is always humorous and has strong interactive value in people’s daily communication. It creates a humorous effect in a certain context, in which a word implies two or more meanings by using polysemy (homographic pun) or phonological similarity to another word (heterographic pun). Pun location is a task to identify the pun word in a given text, which is of great significance to understand humorous texts. Existing methods generally adopt single long sequence structure but cannot well capture the rich semantics of pun words in sentences. We present an approach that considers long-distance and short-distance semantic relations between words simultaneously. For the long-distance semantic relation, we introduce multi-level embeddings to represent the most relevant aspects of the data. For the short-distance semantic relation, we exploit the complex-valued model with a self-adaptive selection mechanism based on multi-scale of input information. Meanwhile, we propose a new classification task to distinguish the homographic pun and heterographic pun. We introduce it as an auxiliary to jointly train the original pun location task, which first learns the location of different types of puns together. Experiment results show that the latest state-of-the-art results can be achieved through our model.