Hateful memes spread quickly online and harm society, necessitating effective detection methods. Detecting these memes is challenging due to the need for comprehensive reasoning. Though great efforts have been made, existing detection methods ...

Humor, a fundamental aspect of human communication, poses a formidable challenge for computational systems. Drawing inspiration from the theory of incongruity, we introduce a novel Dual Graph Attention Network-based Feature Extraction Model (DGFEM)...

Textual personality detection aims to identify personality traits by analyzing user-generated content. To achieve this effectively, it is essential to thoroughly examine user-generated content from various perspectives. However, previous studies ...

News recommendation emerges as a primary means for users to access content of interest from the vast amount of news. The title clickbait extensively exists in news domain and increases the difficulty for news recommendation to offer satisfactory ...${}^{\mathrm{}}$${}^{\mathrm{}}$

The task of Named Entity Recognition (NER) is an important component of information extraction tasks. Currently, span-based approaches are receiving widespread research attention. Despite their success in many aspects, these approaches also suffer ...

By integrating various modules with the Visual Transformer (ViT), we facilitate a interpretation of image processing across each layer and attention head. This method allows us to explore the connections both within and across the layers, enabling a ...

Understanding a meme is a challenging task, due to the metaphorical information contained in the meme that requires intricate interpretation to grasp its intended meaning fully. In previous works, attempts have been made to facilitate computational ...

Malevolence detection in dialogues aims to identify harmful or inappropriate utterances, significantly impacting dialogue quality and user satisfaction. Although existing studies have shown promising performance by modeling interaction patterns from ...

Publicly available design pattern resources make it increasingly costly for developers to access critical content among the many pieces of information. However, current approaches are based on word granularity, which generates semantic errors when ...

Biomedical relation extraction has long been considered a challenging task due to the specialization and complexity of biomedical texts. Syntactic knowledge has been widely employed in existing research to enhance relation extraction, providing ...

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Automatic diagnosis, as an important research direction in artificial intelligence engineering, has advanced significantly in recent years. In real diagnostic scenarios, after the patient informs the doctor about their most obvious symptoms, the ...

The combination of deep learning and the medical field has recently achieved great success, particularly in recommending medicine for patients. However, patients’ clinical records often contain repeated medical information that can significantly ...

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• An attention aggregation model KEAN is proposed for medicine recommendation.
• The enhanced graph convolution module is employed to capture domain knowledge from medicine combinations.
• The proposed KEAN achieves the state-of-the-art ...

Automated medical code assignment aims to allocate disease and procedure codes to patient's discharge summaries, which is crucial for health statistics, medical decision-making, and reimbursement. To alleviate the burden on manual coders and ...

Sequential recommendation is dedicated to offering items of interest for users based on their history behaviors. The attribute-opinion pairs, expressed by users in their reviews for items, provide the potentials to capture user preferences and item ...

Session-based recommendation aims to predict intents of anonymous users based on their limited behaviors. Modeling user behaviors involves two distinct rationales: co-occurrence patterns reflected by item IDs, and fine-grained preferences represented by ...

Graph Convolutional Network (GCN) has been extensively studied in the task of short text classification (STC), utilizing global graphs that incorporate texts at different levels of granularity to learn text embeddings. However, the GCN-based ...

Task-oriented dialogue (ToD) system aims to assist users in completing various tasks, which has attracted great interest from researchers. However, the current models introduce delexicalization prepossessing to improve the generalization ability ...

• The multi-copy network is designed to copy dialogue slots into the response.
• The schema graph helps the model capture the relations between different slots.
• Extensive experimental results demonstrate the advantages of our approach.

Task-oriented Dialogue system (ToD) has gained significant attention due to its aim to assist users in accomplishing various tasks. However, the neural network-based dialogue system is like a black box, which may lead to erroneous responses and ...

• An adapter module integrates the knowledge base into the pre-trained language model (PLM).
• A fusion mechanism is designed to filter out irrelevant knowledge base information.
• Extensive experimental results demonstrate the ...

Source code is an intermediary through which humans communicate with computer systems. It contains a large amount of domain knowledge which can be learned by statistical models. Furthermore, this knowledge can be used to build software engineering ...

In this work, the IRGAN model is revisited to tackle semi-supervised information retrieval (IR) problems, considering the premature convergence of IRGAN caused by mismatching the guidance information between the generator G and the discriminator ...

• The COGAN framework is proposed for web search task.
• A dual cycle enhanced strategy is proposed in two adversarial games.
• The COGAN framework achieves significant improvements over IRGAN.