• We utilize large-scale scholarly datasets to quantify the duration of peer review process for interdisciplinary research.
• Contrary to the widely observed notion that interdisciplinary research often has long-term impact, we find that ...

With the growing prominence of interdisciplinary research and heightened concerns surrounding extended prepublication timelines, we still lack of understanding regarding the interplay between interdisciplinary level and the duration of the peer ...

Large language models (LLMs) that exhibit a remarkable ability by in-context learning (ICL) with bilingual demonstrations have been recognized as a potential solution for machine translation. However, the process of selecting these demonstrations ...

• We explore how to enhance translation ability and efficiency of large language model.
• A new product quantization technique to accelerate selecting demonstrations.
• An innovative template design for in-context learning to implement ...

The objective of the Aspect Sentiment Triplet Extraction (ASTE) task is to identify triplets of (aspect, opinion, sentiment) from user-generated reviews. The current study does not extensively integrate the interaction between word pairs and ...

• We develop SBRS for ASTE to effectively address the problem of overlapping triplets.
• SBRS designs two progressive word levels and word pair levels to refine the text.
• SBRS utilize transfer of temporal semantic information through ...

Self-explainable Graph Neural Networks (GNNs) provide explanations alongside their predictions, making the model transparent and facilitating their wide adoption in high-stakes tasks. Current studies on constructing such GNNs are limited by the ...

• Propose a multi-granularity receptive field to help the acquisition of causal correlations.
• Design sliced architecture to integrate graph embedding with adaptive weights for explanations.
• Design loss functions to get causality to ...

This study presents a financial distress prediction model focusing on the linguistic analysis of risk-related sections of corporate annual reports. Here, we introduce a novel methodology that leverages BERT-based contextualized embedding models ...

• Linguistic analysis of risk-related sections of corporate annual reports.
• BERT-based models used to identify financial sentiment and coherent topics.
• A semi-supervised XGBoost approach used to predict financial distress.
• ...

• Entrepreneurs’ responses on crowdfunding financing are monetized.
• Response strategies are classified into project-oriented and investor-oriented.
• Project-oriented responses are favored than investor-oriented.
• Commenters prefer ...

This paper examines the role of response in crowdfunding to guide fundraisers to monetize their responses better. In all, 6,405 commenters on a large crowdfunding platform in China (Modian.com) are observed. Grounded on the interaction texts, we ...

Network alignment (NA), discovering anchor nodes that represent the same entities across different networks, plays a fundamental role in information fusion. Most existing embedding-based methods rarely study the alignment module, which learns a ...

• Rule knowledge graph (KG) utilizes multi-dimensional semantic associations among rules to alleviate semantic drift in rule discovery.
• Label-aware rule generation approach realizes attentive semantic information propagation based on ...

In weakly supervised learning, labeling rules can automatically label data to train models. However, due to insufficient prior knowledge, rule discovery often suffers from semantic drift. Since misclassified rules are generated from wrongly ...

Stance detection, a fundamental task in natural language processing, identifies user stances in texts towards specific targets. The diverse targets and ever-changing expressions make it challenging to attain comprehensive knowledge from limited ...

• Target-aware knowledge is essential for stance detection in few-shot scenarios.
• Consistency between prior and stance knowledge ensures the power of prompt-tuning.
• The method uses a two-stage framework to ensure fusion knowledge ...

The personalized bundle generation problem, which aims to create a preferred bundle for user from numerous candidate items, receives increasing attention in recommendation. However, existing works ignore the order-invariant nature of the bundle ...

• The bundle generation task is formulated via non-autoregressive mechanism.
• A GNN-based positional encoding module is designed to better capture dependency pattern for the bundle.
• A permutation-equivariant decoder can output the ...

The identification of influential simplices is crucial for understanding higher-order network dynamics. Yet, despite relatively mature research on influential nodes (0-simplices) mining, characterizing simplices’ influence and identifying ...

• Observe the inconsistency between influential nodes/ simplices mining tasks.
• Formulate influential simplices mining as a graph representation learning task.
• Introduce a novel higher-order representation.
• Propose Influential ...

In the finance domain, nested named entities recognition has become a hot topic in named entity recognition tasks. Traditional nested entity recognition methods easily ignore the dependency relationships between entities, and these methods are ...

The task of event relation extraction (ERE) aims to organize multiple events and their relations as a directed graph. However, existing ERE methods exhibit two limitations: (1) Events in a document are typically expressed with merely a trigger ...

• We propose a graph propagation model for event relation extraction.
• We investigate rich event structures for this task with a novel joint model.
• A novel triadic contrastive training method to enable high-order interactions.
• It ...

Network entities have important asset mapping, vulnerability, and service delivery applications. In cyberspace, where the network structure is complex and the number of entities is large, effectively obtaining the relevant attributes of entities ...

Existing benchmarks for Large Language Models (LLMs) mostly focus on general or specific domain capabilities, overlooking structured output capabilities. We introduce SoEval, a benchmark for assessing LLMs’ ability to generate structured outputs ...

• Introducing a novel benchmark for assessing the ability of LLMs to produce structured outputs.
• Presenting a theoretical foundation by analyzing prompt structures and causal graph analysis.
• We Develop the SoEval dataset for 20 ...

Large language models (LLMs) exhibit impressive capabilities across diverse tasks in natural language processing. Nevertheless, challenges arise such as large model parameter size and limited model accessibility through APIs such as ChatGPT and ...

• Finetuning a GIS-specific language model that can answer geospatial questions.
• Providing benchmark datasets for training and evaluating GIS language models.
• A framework for curating datasets and training a specialized large ...

• A novel multi-domain fake news detection model is proposed for domain generalization in fake news detection.
• Entities in news articles are key to learning both domain-invariant and domain-specific news representations.
• We introduce ...

Fake news has become a significant concern in recent times, particularly during the COVID-19 pandemic, as spreading false information can pose significant public health risks. Although many models have been suggested to detect fake news, they are ...

Growing multiple-affiliation collaboration in Artificial Intelligence (AI) can help solve complex integrated problems, but will it trigger the disruption in AI? Scholars have discussed the related topics in other fields. However, these studies ...

Medical Visual Question Answering (MedVQA) is designed to answer natural language questions related to medical images. Existing methods largely adopting the cross-modal pre-training and fine-tuning paradigm, face limitations in accuracy due to ...

Empathetic response generation is a crucial task in natural language processing, enabling emotionally resonant machine–human interactions. In this paper, we introduce the InfRa (Integrating Discourse Features and Response Assessment) model to ...

• Introduce InfRa model for dialogue comprehension and empathetic response generation.
• Optimize feature representation with edge pruning and mutual information learning.
• Employ feedback mechanism for emotional and semantic assessment,...