Spoken Language Understanding (SLU) consists of two sub-tasks: intent detection (ID) and slot filling (SF). Given its broad range of real-world applications, enhancing SLU for practical deployment is increasingly critical. Profile-based SLU addresses ambiguous user utterances by incorporating context awareness (CA), user profiles (UP), and knowledge graphs (KG) to support disambiguation, thereby advancing SLU research toward real-world applicability. However, existing SLU datasets still fall short in representing real-world scenarios. Specifically, (1) CA uses one-hot vectors for representation, which is overly idealized, and (2) models typically focuses solely on predicting intents and slot labels, neglecting the reasoning process that could enhance performance and interpretability. To overcome these limitations, we introduce VRSLU, a novel SLU dataset that integrates both Visual images and explicit Reasoning. For over-idealized CA, we use GPT-4o and FLUX.1-dev to generate images reflecting users’ environments and statuses, followed by human verification to ensure quality. For reasoning, GPT-4o is employed to generate explanations for predicted labels, which are then refined by human annotators to ensure accuracy and coherence. Additionally, we propose an instructional template, LR-Instruct, which first predicts labels and then generates corresponding reasoning. This two-step approach helps mitigate the influence of reasoning bias on label prediction. Experimental results confirm the effectiveness of incorporating visual information and highlight the promise of explicit reasoning in advancing SLU.

Empathy detection (ED) and empathy intent classification (EIC) aim to identify the empathy direction expressed in user utterances and the underlying empathy intent behind them. Previous studies show that facilitating information transfer between tasks can enhance model performance. However, the interaction between ED and EIC in few-shot learning remains underexplored. To this end, we identify the challenges in jointly training ED and EIC in a few-shot setting: establishing effective information transfer between them and improving the model’s generalization capability. We propose a novel model called USB. For information transfer, the interactive module maps empathy and empathy intent labels through utterances to model task correlations. For generalization capability, after capturing empathy and empathy intent representations with an adaptive fusion module, we introduce a multi-level contrastive learning strategy to optimize representations at task and label levels, enhancing generalization. Experimental results on two public datasets show that our model outperforms all baselines.

Empathy detection (ED) aims to understand the user’s empathy direction, while empathy intent classification (EIC) focuses on identifying the empathy intent behind the user’s utterance. Both tasks have garnered significant attention. Recent studies have shown that jointly training these tasks can improve model performance, as their correlation enhances the diversity of information. However, previous studies have relied solely on shallow information transfer between two task representations, failing to fully leverage the inter-task correlation, thus limiting performance. To this end, we propose a novel Label Co-occurrence Transformation Network (LCoT-Net), which models the correlation between the two tasks using the co-occurrence matrix of empathy and empathy intent labels as a medium. By performing category feature transformation at both the label and utterance levels, we achieve two-level mutual task guidance. Experimental results demonstrate that our model achieves competitive performance across various settings on two public datasets.

Multi-intent spoken language understanding consists of two typical subtasks: multi-intent detection and slot filling. Existing approach suffers from two limitations: (1) It fails to explicitly model the information transfer between slots associated within the same intent clause; (2) Using a co-occurrence matrix of both label encodings introduces needless slot positional information such as the prefix ‘B-’ or ‘I-’. For (1), we propose a Gaussian Graph Attention Network that allows interaction to focus not only on the connection between slots within the current intent clause, but also on the connection between intent, and between intent and slot. For (2), we use a co-occurrence matrix of intent categories and slot types to model the knowledge transfer between the two subtasks in the corpus-level interaction, bypassing the introduction of slot positional information. Our framework achieves significant accuracy gains on both the MixATIS and MixSNIPS datasets.

Stereo matching in remote sensing has recently garnered increased attention, primarily focusing on supervised learning. However, datasets with ground truth generated by expensive airbone Lidar exhibit limited quantity and diversity, constraining the effectiveness of supervised networks. In contrast, unsupervised learning methods can leverage the increasing availability of very-high-resolution (VHR) remote sensing images, offering considerable potential in the realm of stereo matching. Motivated by this intuition, we propose a novel unsupervised stereo matching network for VHR remote sensing images. A light-weight module to bridge confidence with predicted error is introduced to refine the core model. Robust unsupervised losses are formulated to enhance network convergence. The experimental results on US3D and WHU-Stereo datasets demonstrate that the proposed network achieves superior accuracy compared to other unsupervised networks and exhibits better generalization capabilities than supervised models. Our code will be available at https://github.com/Elenairene/CBEM.

Optical and synthetic aperture radar (SAR) image matching presents a formidable challenge due to their pronounced geometric and radiometric distinctions arising from multimodality. The distinct imaging mechanisms of optical and SAR sensors make it challenging to identify essentially homologous points in the physical sense, raising concerns about the accuracy and repeatability of correspondences in current feature matching methods. In this study, we introduce a detector-free feature matching algorithm specifically designed to match optical and SAR images through a two-step strategy. In the initial phase, our proposed method conducts pixelwise matching (PM) using downsampled feature descriptors, eliminating the necessity to identify repeatable keypoints. To mitigate complexity, we enforce a pseudo-epipolar constraint (PEC) to reduce computational costs by constraining the search range. Subsequently, refined matching is performed on the initial correspondences to rectify inaccuracies in the PM localization of the first step. Both matching steps are implemented on a graphics processing unit (GPU) to ensure high efficiency. The proposed algorithm attains an average matching accuracy of 2.39 pixels and operates with an efficiency of 1.09 s for 1108 image pairs, underscoring its superior comprehensive performance compared to various state-of-the-art algorithms, including handcrafted methods and deep learning networks.

Empathy plays an important role in the human dialogue.Detecting the empathetic direction expressed by the user is necessary for empathetic dialogue systems because it is highly relevant to understanding the user's needs.Several studies have shown that empathy intent information improves the ability to response capacity of empathetic dialogue.However, the interaction between empathy detection and empathy intent recognition has not been explored.To this end, we invite 3 experts to manually annotate the healthy empathy detection datasets IEMPATHIZE and TwittEmp with 8 empathy intent labels, and perform joint training for the two tasks.Empirical study has shown that the introduction of empathy intent recognition task can improve the accuracy of empathy detection task, and we analyze possible reasons for this improvement.To make joint training of the two tasks more challenging, we propose a novel framework, Cascaded Label Signal Network, which uses the cascaded interactive attention module and the label signal enhancement module to capture feature exchange information between empathy and empathy intent representations.Experimental results show that our framework outperforms all baselines under both settings on the two datasets.1

The goal of an empathetic dialogue generative sys-tem is to generate coherently and relate emotional responses after finite turns of dialogue. Most of the previous work guided the dialogue system to generate empathetic responses from the dialogue history or emotional reasons. However, in people's daily communication, the empathetic response is often generated by deep reasoning through dialogue history and existing commonsense knowledge. To fill this gap, we propose an empathetic dialogue model of unidirectional synchronous interactive reasoning, Emp- Usir.Specifically, according to the existing commonsense knowledge base, we extract commonsense knowledge with emotional representation from it, and use it as the connection feature of dialogue history and remaining common-sense knowledge to guide the interactive reasoning between multi-turns dialogue history and commonsense knowledge, to imitate human reasoning based on commonsense to generate responses. Then, we propose a cross-token level attention mechanism to learn emotional dependence from reasoning features, to produce more smooth and diverse empathy responses. The experimental results and manual evaluation prove the effectiveness of the Emp-USIR model proposed in this paper on the widely-used benchmark dataset.