The performance of various tasks of natural language processing has greatly improved with the emergence of large language models. However, there is still much room for improvement in understanding certain specific linguistic phenomena, such as Chinese idioms, which are usually composed of four characters. Chinese idioms are difficult to understand due to semantic gaps between their literal and actual meanings. Researchers have proposed the Chinese idiom reading comprehension task to examine the ability of large language models to represent and understand Chinese idioms. The task requires choosing the correct Chinese idiom from a list of candidates to complete the sentence. The current research mainly focuses on text-based idiom comprehension. Nevertheless, there are many idiom application scenarios that combine images and text, and we believe that the corresponding images are beneficial for the model's understanding of the idioms. Therefore, to address the above problems, we first construct a large-scale Multimodal Chinese Idiom Reading Comprehension dataset (MChIRC), which contains a total of 44,433 image-text pairs covering 2,926 idioms. Then, we propose a Dual-Contrastive Idiom Graph Network (DCIGN), which employs a dual-contrastive learning module to align the text and image features corresponding to the same Chinese idiom at both coarse and fine levels, while utilizing a graph structure to capture the semantic relationships between idiom candidates. Finally, we use a cross-attention module to fuse multimodal features with graph features of candidate idioms to predict correct answers. The authoritativeness of MChIRC and the effectiveness of DCIGN are demonstrated through a variety of experiments, which provides a new benchmark for the multimodal Chinese idiom reading comprehension task.

Aspect Sentiment Quad Prediction (ASQP) aims to extract all sentiment elements in quads for a given review to explain the reason for the sentiment. Previous table-filling based methods have achieved promising results by modeling word-pair relations. However, these methods decompose the ASQP task into several subtasks without considering the association between sentiment elements. Most importantly, they fail to tackle the situation where a sentence contains multiple implicit expressions. To address these limitations, we propose a simple yet effective Unified Grid Tagging Scheme (UGTS) to extract sentiment quadruplets in one shot, with two additional special tokens from pre-trained models to represent potential implicit aspect and opinion terms. Based on this, we first introduce the adaptive graph diffusion convolution network to construct the direct connection between explicit and implicit sentiment elements from syntactic and semantic views. Next, we utilize conditional layer normalization to refine the mutual indication effect between words for matching valid aspect-opinion pairs. Finally, we employ the triaffine mechanism to integrate heterogeneous word-pair relations to capture higher-order interactions between sentiment elements. Experimental results on four benchmark datasets show the effectiveness and robustness of our model, which achieves state-of-the-art performance.

Euphemisms are commonly used on social media and darknet marketplaces to evade platform regulations by masking their true meanings with innocent ones. For instance, “weed” is used instead of “marijuana” for illicit transactions. Thus, euphemism identification, i.e., mapping a given euphemism (“weed”) to its specific target word (“marijuana”), is essential for improving content moderation and combating underground markets. Existing methods employ self-supervised schemes to automatically construct labeled training datasets for euphemism identification. However, they overlook the text-text domain gap caused by the discrepancy between the constructed training data and the test data, leading to performance deterioration. In this paper, we present the text-text domain gap and explain how it forms in terms of the data distribution and the cone effect. Moreover, to bridge this gap, we introduce a feature alignment network (FA-Net), which can both align the in-domain and cross-domain features, thus mitigating the domain gap from training data to test data and improving the performance of the base models for euphemism identification. We apply this FA-Net to the base models, obtaining markedly better results, and creating a state-of-the-art model which beats the large language models.

Chinese idioms pose a significant challenge for machine reading comprehension due to their metaphorical meanings often diverging from their literal counterparts, leading to metaphorical inconsistency. Furthermore, the same idiom can have different meanings in different contexts, resulting in contextual inconsistency. Although deep learning-based methods have achieved some success in idioms reading comprehension, existing approaches still struggle to accurately capture idiom representations due to metaphorical inconsistency and contextual inconsistency of idioms. To address these challenges, we propose a novel model, Multi-Semantic Contrastive Learning Method (MSCLM), which simultaneously addresses metaphorical inconsistency and contextual inconsistency of idioms. To mitigate metaphorical inconsistency, we propose a metaphor contrastive learning module based on the prompt method, bridging the semantic gap between literal and metaphorical meanings of idioms. To mitigate contextual inconsistency, we propose a multi-semantic cross-attention module to explore semantic features between different metaphors of the same idiom in various contexts. Our model has been compared with multiple current latest models (including GPT-3.5) on multiple Chinese idiom reading comprehension datasets, and the experimental results demonstrate that MSCLM outperforms state-of-the-art models.

Chinese idioms on social media demand a nuanced understanding for correct usage. The Chinese idiom cloze test poses a unique challenge for machine reading comprehension due to the figurative meanings of idioms deviating from their literal interpretations, resulting in a semantic bias in models’ comprehension of idioms. Furthermore, given that the figurative meanings of many idioms are similar, their use as suboptimal options can interfere with optimal selection. Despite achieving some success in the Chinese idiom cloze test, existing methods based on deep learning still struggle to comprehensively grasp idiom semantics due to the aforementioned issues. To tackle these challenges, we introduce a Refining Idioms Semantics Comprehension Framework (RISCF) to capture the comprehensive idioms semantics. Specifically, we propose a semantic sense contrastive learning module to enhance the representation of idiom semantics, diminishing the semantic bias between figurative and literal meanings of idioms. Meanwhile, we propose an interference-resistant cross-attention module to attenuate the interference of suboptimal options, which considers the interaction between the candidate idioms and the blank space in the context. Experimental results on the benchmark datasets demonstrate the effectiveness of our RISCF model, which outperforms state-of-the-art methods significantly.

Euphemisms are widely used on social media and darknet markets to evade supervision. For instance, "ice" serves as a euphemism for the target keyword "methamphetamine" in illicit transactions. Thus, euphemism identification which aims to map the euphemism to its secret meaning (target keyword) is a crucial task in ensuring social network security. However, this task poses significant challenges, including resource limitations due to the unavailable of annotated datasets and linguistic challenges arising from subtle differences in meaning between target keywords. Existing methods employed self-supervised schemes to automatically construct labeled training data, addressing the resource limitations. Yet, these methods rely on static embedding methods that fail to distinguish between target keywords with similar meanings. In addition, we observe that different euphemisms in similar contexts confuse the identification results. To overcome these obstacles, we propose a feature fusion and individualization (FFI) method for euphemism identification. First, we reformulate the task as a cloze task, making it more feasible. Next, we develop a feature fusion module to capture both dynamic global and static local features, enhancing discrimination between different euphemisms in similar contexts. Additionally, we employ a feature individualization module to ensure each target keyword has a unique feature representation by projecting features into their orthogonal space. As a result, FFI can effectively identify similar euphemisms that refer to target keywords with similar meanings. Experimental results demonstrate that our method outperforms state-of-the-art methods and large language models, providing robust support for its effectiveness.

The existing models have been achieved great success in capturing and fusing miltimodal semantics of news. However, they paid more attention to the global information, ignoring the interactions of global and local semantics and the inconsistency between different modalities. Therefore, we propose an explainable multi-view semantic fusion model (EMSFM), where we aggregate the important inconsistent semantics from local and global views to compensate the global information. Inspired by various forms of artificial fake news and real news, we summarize four views of multimodal correlation: consistency and inconsistency in the local and global views. Integrating these four views, our EMSFM can interpretatively establish global and local fusion between consistent and inconsistent semantics in multimodal relations for fake news detection. The extensive experimental results show that the EMSFM can improve the performance of multimodal fake news detection and provide a novel paradigm for explainable multi-view semantic fusion.

Multimodal fake news detection has become a topical research of fake news detection. Existing models have made great efforts in capturing and fusing multimodal semantics of news for classification. However, they overlooked mitigating inconsistency between different modalities, which may result in learning biased statistical information. Therefore, we propose a mitigating multimodal inconsistency contrastive learning framework (MMICF), which mitigates inconsistency in multi-modal relations for fake news detection. Inspired by various forms of artificial fake news, we summarize two patterns of multimodal inconsistency: local and global inconsistency. To mitigate local inconsistency in multimodal relations, we use a causal-relation reasoning module by causally removing the direct effects of the textual and visual entities. Considering the influence of global inconsistency in multimodal semantics, our contrastive learning framework mitigates the semantic deviation of contrastive text-image objectives, which are constrained into a unified semantic space by a modal unified module. Thus, our MMICF can jointly mitigate local and global inconsistency for further maximally exploiting multimodal consistent semantics for fake news detection. The extensive experimental results show that the MMICF can improve the performance of multimodal fake news detection and provide a novel paradigm for mitigating multimodal inconsistency contrastive learning.