Emotion Cause Extraction Field (ECEF) focuses on the cause that triggers an emotion in a document and mainly includes Emotion Cause Extraction (ECE) and Emotion Cause Pair Extraction (ECPE). Traditional ECE aims to extract the cause based on a given emotion while ECPE aims to extract both the emotion and its corresponding cause. Recently, ECEF has attracted a lot of attention and most of the advances have benefited from significant developments in deep learning techniques, especially machine reading comprehension and neural-network-based information retrieval. The large pre-trained language model of BERT has also shown effectiveness in this field. Following the proposal of ECPE, the development of ECEF has accelerated. However, a comprehensive review of existing approaches and recent trends in the field is lacking. To address this issue, this survey presents a thorough review to summarise existing methods and recent key advances, illustrate the general technical architecture of traditional ECE, introduce several important variants, in particular ECPE, and provide a detailed comparison of several public datasets. Finally, the limitations of existing work and the prospects for further technological advances in ECEF are discussed.

De novo design of antimicrobial peptides (AMPs) is challenging due to the vast combinatorial space and unknown mechanisms. We propose EBAMP, a generative-discriminative framework for de novo broad-spectrum AMP design targeting bacteria and fungi. EBAMP combines a Transformer-based generative model with advanced feature-based screening to explore peptide space and select multiobjective candidates. Experimental testing of 256 designed sequences shows that 96 (37.5%) display bactericidal ability. The top 10 sequences exhibit low cytotoxicity, low hemolysis, and strong antibacterial effect (2 μg/mL) against multidrug-resistant bacteria and fungi. In vivo mouse full-thickness wound infection model demonstrates inhibitory effects against Acinetobacter baumannii (bacterium) and Candida auris (fungus), with therapeutic efficiency comparable to antibiotics but lower resistance propensity. Alanine substitution analysis and molecular dynamics reveal functionally critical positions. EBAMP showcases large generative models for broad-spectrum AMP discovery and addresses antibiotic resistance.

Region similarity learning plays an essential role in applications such as business site selection, region recommendation, and urban planning. Earlier studies mainly represent regions as bags of points of interest (POIs) for region similarity comparisons, which cannot fully exploit the spatial features of the regions. Recently, researchers propose to use deep neural networks to exploit spatial features such as POI geo-coordinates and categories, which have produced more accurate and robust region similarity learning results. However, many useful features such as the height and size of a POI, and the distance and relative importance between the POIs, are still overlooked in these methods. To take advantage of such features, we propose to represent regions as graphs, where nodes are POIs with rich features such as height, size, and hexagonal coordinates, while edges are the relationships between POIs formulated by their road network distances. To capture POIs’ importance, we weigh them by their height and size. Since there is limited availability of ground-truth region similarity data, we propose a contrastive learning-based multi-relational graph neural network (C-MPGCN) for region similarity learning based on the graph representations. To generate data for model training, we propose a soft graph edit distance (SGED) based algorithm to generate triples of similar and dissimilar graphs of a given graph (representing a given region) based on the POI weights. Experimental results show that C-MPGCN outperforms the state-of-the-art methods for region similarity learning consistently with an improvement of at least 8.6% and 9.4% in terms of MRR and HR@1, respectively.

Menglong Lu, Zhen Huang, Yunxiang Zhao, Zhiliang Tian, Yang Liu, Dongsheng Li. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.

Emotion Cause Pair Extraction (ECPE) aims to extract emotions and their causes from a document. Powerful emotion and cause extraction abilities have proven essential in achieving accurate ECPE. However, most existing methods employ shared feature learning of emotion extraction and cause extraction, which can harm the abilities of both tasks as they focus on different information (i.e., task-specific features). Moreover, shared feature learning of the two tasks also leads to the label imbalance problem. To address these issues, this paper proposes a multi-task learning framework named Hierarchical Shared Encoder with Task-specific Transformer Layer Selection (HSE-TTLS). The model achieves ECPE via two subtasks: Emotion Extraction (EE) and Emotion Cause Extraction (ECE). The design of two subtasks for ECPE corresponds to the fact that cause clauses are emotion-dependent and significantly alleviates the label imbalance problem. To effectively extract task-specific features for EE and ECE, we employ BERT as the token-level encoder and select task-specific optimal layers for the two subtasks. Focal loss is used as the objective function for EE to further alleviate the label imbalance problem. Extensive experiments on benchmark ECPE corpus demonstrate the effectiveness of HSE-TTLS, which outperforms state-of-the-art baseline methods by at least 1.56% on the F1 score.