The rapid development of social media platforms has accelerated the generation and spread of fake news. News on different platforms varies significantly in content and audience. It makes most existing fake news detection models, which rely on single-source datasets, struggle to perform well on news from other sources. Many social platforms also lack high-quality annotated data. To address this problem, we propose a novel cross-source multi-modal fake news detection model named CMFNThinker. CMFNThinker simulates human thinking patterns. It detects fake news across platforms in three stages: summarizing the news content, retrieving similar news posts and reasoning the truthfulness of the news. We conducted extensive experiments on multi-source datasets. The results show that our model outperforms state-of-the-art baseline models by at least 11.3% in macro F1 for cross-source fake news detection.

The proliferation of misinformation on the internet and social media platforms poses a significant challenge to public discourse integrity. Traditional Fact Verification methods rely on costly, domain-specific annotated datasets, limiting their adaptability. While Large Language Models (LLM) offer new possibilities, existing LLM-based techniques face critical limitations: (1) Dependence on simplistic pipelines: Lacking robustness to handle contradictory evidence or ambiguous claims. (2) Underutilization of multi-agent interactions: Restricting thorough claim assessments from multiple perspectives. To address these challenges, we propose a novel Fact - based Debate (FBD) framework, which combines retrieval-augmented generation (RAG) with iterative argumentation using structured multi-agent debate. On the one hand, the FBD framework is introduced to enhance the robustness and reliability of fact - checking. On the other hand, the FBD is designed with a three - stage pipeline for knowledge retrieval, acquisition, and refinement based on authoritative sources. Extensive experiments on five real - world datasets demonstrate that our proposed FBD outperforms existing methods, achieving an average relative improvement of approximately 2.36% compared to the optimal baseline.

Document-level event argument extraction aims to identify the event arguments and predict their roles in a document. There are many non-argument entities that play an essential role in understanding events in the document, which we call event-linking entities. However, recent work on document-level event argument extraction overlooks these entities and gets suboptimal performance. Moreover, a document usually contains multiple events, and they are interconnected with each other. Most recent work models each event in isolation without considering their interconnection. To tackle these problems, we propose a Generative Dual Representations Fusion network (GDRF) for document-level event argument extraction to introduce the significance of event-linking entities for the first time. GDRF retrieves event-linking entities and tags them in the document using the Event-linking Entity based Document Tagging module. To introduce contextual information from event-linking entities and capture the interconnection between multiple events, we propose a Dual Representations Fusion module to fuse original and tagged context representations with a fusion loss. Empirical results on the WIKIEVENTS dataset demonstrate that our model outperforms previous methods, achieving state-of-the-art performance.

Event detection is an eminent task in natural language processing, which aims at detecting event triggers in sentences and classifying them into specific event types. Some recent work has achieved good results in event detection tasks using syntactic dependency structures. However, the syntactic structure of parsing does not only bring benefits. It can also introduce noise or even misleading judgments. Moreover, most recent work focuses only on the contribution of syntactic information while ignoring the impact of semantic information. In this paper, we propose a novel Event Detection Model Based on the Fusion of Hierarchical Syntactic and Type Semantic Features (FHSTSF), which consists of a syntactic feature extractor for capturing hierarchical syntactic dependency features and a semantic feature extractor for extracting semantic features of event type labels. We conducted experiments on two public benchmark datasets, MAVEN and ACE-2005. The experimental results show that our model improves by 1.75% and 4.17% of the F1 Value on both benchmarks than the current best models, respectively.

Conversational Causal Emotion Entailment (C2E2) aims to identify the causes of a target emotion in a non-neutral conversation. Most models treat C2E2 as an independent utterance pair classification problem that ignores the contextual information. Furthermore, most recent works focus only on the contribution of utterance information while ignoring the impact of speaker and emotional information. To solve these problems, we propose a joint model with the contextual and speaker information for conversational causal emotion entailment. We introduce the temporal convolutional network structure to effectively capture contextual information and help the model better understand and analyze emotional changes in conversations. At the same time, a multi-feature interaction network is proposed to use multiple features of utterance to analyze the causes of the emotion, the network extracts location information from the position-aware graph and uses speaker and emotion information to help the model understand the causes behind emotion generation. The experimental results demonstrate that our method outperforms the baseline method and can infer the causes of different emotions in more complex contexts.

Abstract Event Detection (ED) is a crucial information extraction task that aims to identify the event triggers and classify them into predefined event types. However, most existing methods did not perform well when processing events with implicit triggers. And most methods considered ED as a sentence-level task, lacking effective context for event semantics. Moreover, how to maintain good performance under low resource conditions still needs further study. To address these problems, we propose a novel end-to-end ED model called DE3TC, which Detects Events with Effective Event Type Information and Context. We construct an event type-specific Clue to capture the interaction between event type name and trigger words, providing event type information for implicit triggers. For accessing the effective context of event semantics for sentence-level ED, we consider the correlations between types and select similar types’ descriptions as context. With contextualized representation from a contextual encoder, DE3TC learns the event type information for all events including implicit ones. And it performs sentence-level ED efficiently with effective contexts. The empirical results on ACE 2005 and MAVEN datasets show that: (i) DE3TC obtains state-of-the-art performance compared with previous methods. (ii) DE3TC is also excelled under low-resource conditions.

Aspect-based sentiment analysis (ABSA) aims to determine the sentiment polarity of the specific aspect for a given sentence. Attention-based models are widely used in this task because they can extract semantic information between context words to make up for the deficiency of sequence models in semantic encoding. In order to enhance the extraction of high-quality semantic information, we propose a novel joint model with Second-Order Features and Matching Attention (SOMA) for aspect-based sentiment analysis. Firstly, we introduce the second-order statistics to extract vital information and interact with the first-order features to generate the interaction representation. Secondly, we adopt Euclidean distance to replace the traditional matrix transformation to capture the semantic similarity between aspect terms and context words. Finally, we form a joint representation to focus on the meaningful words in the sentence. We conduct extensive experiments and comparisons on SemEval 2014, SemEval 2016, and Twitter datasets. Experimental results demonstrate the effectiveness of our model.