The global aging population faces considerable challenges, particularly in communication, due to the prevalence of hearing and speech impairments. To address these, we introduce the AVE speech, a comprehensive multimodal dataset for speech recognition tasks. The dataset includes a 100-sentence Mandarin corpus with audio signals, lip-region video recordings, and six-channel electromyography data, collected from 100 participants. Each subject read the entire corpus ten times, with each sentence averaging approximately two seconds in duration, resulting in over 55 hours of multimodal speech data per modality. Experiments demonstrate that combining these modalities significantly improves recognition performance, particularly in cross-subject and high-noise environments. To our knowledge, this is the first publicly available sentence-level dataset integrating these three modalities for large-scale Mandarin speech recognition. We expect this dataset to drive advancements in both acoustic and nonacoustic speech recognition research, enhancing cross-modal learning and human–machine interaction.

Multi-view spectral clustering has attracted considerable attention since it can explore common geometric structures from diverse views. Nevertheless, existing min-min framework-based models adopt internal minimization to find the view combination with the minimized within-cluster variance, which will lead to effectiveness loss since the real clusters often exhibit high within-cluster variance. To address this issue, we provide a novel scalable min-max multi-view spectral clustering (SMMSC) model to improve clustering performance. Besides, anchor graphs, rather than full sample graphs, are utilized to reduce the computational complexity of graph construction and singular value decomposition, thereby enhancing the applicability of SMMSC to large-scale applications. Then, we rewrite the min-max model as a minimized optimal value function, demonstrate its differentiability, and develop an efficient gradient descent-based algorithm to optimize it with linear computational complexity. Moreover, we demonstrate that the resultant solution of the proposed algorithm is the global optimum. Numerous experiments on different real-world datasets, including some large-scale datasets, demonstrate that SMMSC outperforms existing state-of-the-art multi-view clustering methods regarding clustering performance.

Due to its high computational complexity, graph-based methods have limited applicability in large-scale multiview clustering tasks. To address this issue, many accelerated algorithms, especially anchor graph-based methods and indicator learning-based methods, have been developed and made a great success. Nevertheless, since the restrictions of the optimization strategy, these accelerated methods still need to approximate the discrete graph-cutting problem to a continuous spectral embedding problem and utilize different discretization strategies to obtain discrete sample categories. To avoid the loss of effectiveness and efficiency caused by the approximation and discretization, we establish a discrete fast multiview anchor graph clustering (FMAGC) model that first constructs an anchor graph of each view and then generates a discrete cluster indicator matrix by solving the discrete multiview graph-cutting problem directly. Since the gradient descent-based method makes it hard to solve this discrete model, we propose a fast coordinate descent-based optimization strategy with linear complexity to solve it without approximating it as a continuous one. Extensive experiments on widely used normal and large-scale multiview datasets show that FMAGC can improve clustering effectiveness and efficiency compared to other state-of-the-art baselines.

Event Argument Extraction (EAE) aims to identify and extract key information such as entities, times, and locations related to specific events from text and serves as a fundamental task for many NLP applications. Recent researches have utilized large language models (LLMs) for EAE, effectively addressing the resource-intensive nature of annotating training datasets for this task. However, when performing EAE on longer texts (document-level EAE), the presence of descriptions unrelated to the events within document-level EAE can lead LLMs to identify incorrect arguments. To address this issue, we propose Simple-Hard Refining: a novel prompt framework that segments EAE into straightforward and complex extraction tasks. Based on the complexity of inference, we divide EAE task into simple-argument extraction and hard-argument extraction. By utilizing a chain of prompt to perform simple and hard argument extraction sequentially, noise introduced by irrelevant description for simple-argument extraction can be effectively alleviated. Furthermore, we explore the potential of LLMs to furnish dependable explanations for their extraction outcomes. We design an explanation-based prompting method that involves a three-step explanation process: relevant sentence extraction, argument role semantic analysis, and argument role entity localization. This method further enhances the extraction accuracy at each stage of the framework. Our experiments demonstrate that our method achieves state-of-the-art performance, surpassing various baselines that utilize LLMs for the EAE task. Ablation studies further verify the effectiveness of each stage of our framework and show the ability of our proposed approach to effectively mitigate noise. Our work contributes to the structured extraction of event argument information using LLMs.