Sarcasm detection contributes to the understanding of the contrast between the literal meaning of an utterance and the true intention of the speaker, and is considered to be part of the challenge in sentiment analysis. Most models usually focus on prompted inference, ignoring the knowledge hallucination (i.e., the generation of factually incorrect or fabricated information) and inference mistakes generated by large models, which affects the detection accuracy of sarcastic semantics. To solve these complex problems, we propose a novel multidimensional contextual knowledge inference (MCKI) model, which further enhances the contextual inference capability of the model by introducing the multihop chain of thought (CoT) inference technique, combining knowledge enhancement techniques and self-consistency mechanism to better capture the underlying intent of sarcastic text. Specifically, we first utilize multihop context inference techniques to excavate fine-grained contextual and emotional cues in satire through a stepwise inference process. Second, contextual knowledge augmentation is employed to motivate the model to capture deep semantics. Finally, a self-consistency mechanism is adopted to ensure that the generated inference paths are consistent across multiple perspectives, thus improving the accuracy and stability of sarcasm detection. Experimental results illustrate that compared to the state-of-the-art baseline, our model achieves significant improvements on the three baseline datasets, especially on the X dataset by 2.4%, which demonstrates the superior performance of our Multidimensional contextual knowledge Inference model for sarcastic detection.

Today, electric vehicles (EVs) have gained significant recognition in the global market as an innovative mode of transport. However, the development of EVs is based on the interaction with the energy Internet. The increasing number of EVs has presented significant challenges to the existing charging infrastructure. Traditional fixed-location charging stations are increasingly inadequate to meet fluctuating charging demand, leading to inefficiencies such as long waiting times and uneven distribution of charging load. To address the problem, this paper proposes a Mobile-Fixed Charging Stations Collaboration Network (MFCSCN), which integrates fixed charging stations with mobile charging stations to dynamically adapt to real-time charging demands. Firstly, LightGBM is utilized to predict the charging load at fixed charging stations considering historical data and real-time EV mobility patterns. Secondly, the reinforcement learning algorithm is employed to optimize the placement of mobile charging stations based on predicted demand. Third, within the MFCSCN framework, LPPO (LightGBM and Proximal Policy Optimization) is proposed, which combines predictive modeling and reinforcement learning to optimize the dynamic placement of mobile charging stations. Through extensive simulations, we demonstrate that the MFCSCN significantly improves the responsiveness and scalability of the EV charging infrastructure, offering a robust solution to the evolving needs of urban mobility.

The task of recognizing Chinese variant characters aims to address the challenges of semantic ambiguity and confusion, which potentially cause risks to the security of Web content and complicate the governance of sensitive words. Most existing approaches predominantly prioritize the acquisition of contextual knowledge from Chinese corpora and vocabularies during pretraining, often overlooking the inherent phonological and morphological characteristics of the Chinese language. To address these issues, we propose a shared-weight multimodal translation model (SMTM) based on multimodal information of Chinese characters, which integrates the phonology of Pinyin and the morphology of fonts into each Chinese character token to learn the deeper semantics of variant text. Specifically, we encode the Pinyin features of Chinese characters using the embedding layer, and the font features of Chinese characters are extracted based on convolutional neural networks directly. Considering the multimodal similarity between the source and target sentences of the Chinese variant-character-recognition task, we design the shared-weight embedding mechanism to generate target sentences using the heuristic information from the source sentences in the training process. The simulation results show that our proposed SMTM achieves remarkable performance of 89.550% and 79.480% on bilingual evaluation understudy (BLEU) and F1 metrics respectively, with significant improvement compared with state-of-the-art baseline models.

Enabling machines to possess commonsense reasoning within the intelligent Internet of Things (IoT) ecosystem plays a pivotal role in their capacity to attain autonomous decision-making and execution in complex tasks. The Winograd Schema Challenge (WSC) is a fundamental and challenging task in the field of commonsense reasoning, whose pairwise mutually exclusive properties and lack of contextual cues require machines with strong intelligence and skillful inference. Previous works that relied on pretrained models exhibited limited comprehension in commonsense because of overlooking the special property of the WSC task and solely relying on linguistic tendencies to learn superficial cues, failing to grasp the potential commonsense embedded within sentences. To address this issue, we propose a novel triple-wise perspective knowledge learning (TPKL) model for commonsense reasoning. Specifically, we introduce a new paradigm for addressing the WSC task by employing triplets instead of the conventional single or contrastive sentence inputs, which enables better compatibility with pairwise mutually exclusive features in WSC tasks. Additionally, we propose a triple-wise perspective that leverages anchor, positive, and negative sentences in a triplet construction to enable the model to comprehensively learn the pairwise mutually exclusive sentences, which can capture and utilize commonsense knowledge to distinguish between the various word senses under consideration. Extensive experiments conducted on three benchmark data sets demonstrate the superiority of our model over state-of-the-art baselines, improving PDP-60, WSC, and KnowRef benchmark with 3.3%, 4.4%, and 8.1%, respectively.

Text generation is an essential research area in artificial intelligence (AI) technology and natural language processing and provides key technical support for the rapid development of AI-generated content (AIGC). It is based on technologies such as natural language processing, machine learning, and deep learning, which enable learning language rules through training models to automatically generate text that meets grammatical and semantic requirements. In this paper, we sort and systematically summarize the main research progress in text generation and review recent text generation papers, focusing on presenting a detailed understanding of the technical models. In addition, several typical text generation application systems are presented. Finally, we address some challenges and future directions in AI text generation. We conclude that improving the quality, quantity, interactivity, and adaptability of generated text can help fundamentally advance AI text generation development.

Text summarization on non-factoid question answering (NQA) aims at identifying the core information of redundant answer guidance using questions, which can dramatically improve answer readability and comprehensibility. Most existing approaches focus on extracting query-related sentences to construct a summary, where the logical connection of natural language and the hierarchical interpretable semantic association are often neglected, thus degrading performance. To address these issues, we propose a novel question-driven abstractive answer summarization model, called the H ierarchical S liding I nference G enerator (HSIG), to form inferable and interpretable summaries by explicitly introducing hierarchical information reasoning between questions and corresponding answers. Specifically, we first apply an elaborately designed hierarchical sliding fusion inference model to determine the most relevant question sentence-level representation that provides a deeper interpretable basis for sentence selection in summarization, which further increases computational performance on the premise of following the semantic inheritance structure. Additionally, to improve summary fluency, we construct a double-driven selective generator to integrate various semantic information from two mutual question-and-answer perspectives. Experimental results illustrate that compared with state-of-the-art baselines, our model achieves remarkable improvement on two benchmark datasets and specifically improves the 2.46 ROUGE-1 points on PubMedQA, which demonstrates the superiority of our model on abstractive summarization with hierarchical sequential reasoning.