Automatic radiology report generation is pivotal in reducing the workload of radiologists, while simultaneously improving diagnostic accuracy and operational efficiency. Current methods face significant challenges, including the effective alignment of medical visual features with textual features and the mitigation of data bias. In this paper, we propose a method for radiology report generation that utilizes a Cross-modal Enhancement and Alignment Adapter (CmEAA) to connect a vision encoder with a frozen large language model. Specifically, we introduce two novel modules within CmEAA: Cross-modal Feature Enhancement (CFE) and Neural Mutual Information Aligner (NMIA). CFE extracts observation-related contextual features to enhance the visual features of lesions and abnormal regions in radiology images through a cross-modal enhancement transformer. NMIA maximizes neural mutual information between visual and textual representations within a low-dimensional alignment embedding space during training and provides potential global alignment visual representations during inference. Additionally, a weights generator is designed to enable the dynamic adaptation of cross-modal enhanced features and vanilla visual features. Experimental results on two prevailing datasets, namely, IU X-Ray and MIMIC-CXR, demonstrate that the proposed model outperforms previous state-of-the-art methods.

Document-level Relation Extraction (DocRE) aims to extract relations from documents. Compared with sentence-level relation extraction, it is necessary to extract long-distance dependencies. Existing methods enhance the output of trained DocRE models either by learning logical rules or by extracting rules from annotated data and then injecting them into the model. However, these approaches can result in suboptimal performance due to incorrect rule set constraints. To mitigate this issue, we propose Context-aware differentiable rule learning or CaDRL for short, a novel differentiable rule-based framework that learns the doc-specific logical rule to avoid generating suboptimal constraints. Specifically, we utilize Transformer-based relation attention to encode document and relation information, thereby learning the contextual information of the relation. We employ a sequence-generated differentiable rule decoder to generate relational probabilistic logic rules at each reasoning step. We also introduce a parameter sharing training mechanism in CaDRL to reconcile the DocRE model and the rule learning module. Extensive experimental results on three DocRE datasets demonstrate that CaDRL outperforms existing rule-based frameworks, significantly improving DocRE performance and making predictions more interpretable and logical.

Text-to-SQL, which maps natural language to SQL queries, has benefited greatly from recent advances in Large Language Models (LLMs).While LLMs offer various paradigms for this task, including prompting and supervised fine-tuning (SFT), SFT approaches still face challenges such as complex multistage pipelines and poor robustness to noisy schema information.To address these limitations, we present JOLT-SQL, a streamlined single-stage SFT framework that jointly optimizes schema linking and SQL generation via a unified loss.JOLT-SQL employs discriminative schema linking, enhanced by local bidirectional attention, alongside a confusion-aware noisy schema sampling strategy with selective attention to improve robustness under noisy schema conditions.Experiments on the Spider and BIRD benchmarks demonstrate that JOLT-SQL achieves state-of-the-art execution accuracy among comparable-size open-source models, while significantly improving both training and inference efficiency.Our code is available at https://github.com/Songjw133/JOLT-SQL.

The medical knowledge graph is the cornerstone of intelligent medical applications. The existing medical knowledge graphs are not enough from the perspectives of scale, specification, taxonomy, formalization as well as the precise description of the knowledge to meet the needs of pediatric epilepsy intelligent medical applications. We apply natural language processing and text mining techniques with a semi-automated approach to develop the chinese pediatric epilepsy knowledge graph(CPeKG). The CPeKG covers typical entitie types such as disease, drug and examination, with about 60,000 entities and 190,000 entity-relationship triplets. This paper presents the description system, key technologies, construction process of CPeKG. The CPeKG can provide external knowledge for pediatric epilepsy intelligent medical applications, and has important practical significance for the diagnosis and treatment of pediatric epilepsy.

Ningyu Zhang, Mosha Chen, Zhen Bi, Xiaozhuan Liang, Lei Li, Xin Shang, Kangping Yin, Chuanqi Tan, Jian Xu, Fei Huang, Luo Si, Yuan Ni, Guotong Xie, Zhifang Sui, Baobao Chang, Hui Zong, Zheng Yuan, Linfeng Li, Jun Yan, Hongying Zan, Kunli Zhang, Buzhou Tang, Qingcai Chen. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2022.