《Single-Stage Extensive Semantic Fusion for multi-modal sarcasm detection》

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文章题目含义

多模态讽刺语检测的单阶段扩展语义融合

ABSTRACT

With the rise of social media and online interactions, there is a growing need for analytical models capable of understanding the nuanced, multi-modal communication inherent in platforms, especially for detecting sarcasm. Existing research employs multi-stage models along with extensive semantic information extractions and single-modal encoders. These models often struggle with efficient aligning and fusing multi-modal representations. Addressing these shortcomings, we introduce the Single-Stage Extensive Semantic Fusion (SSESF) model, designed to concurrently process multi-modal inputs in a unified framework, which performs encoding and fusing in the same architecture with shared parameters. A projection mechanism is employed to overcome the challenges posed by the diversity of inputs and the integration of a wide range of semantic information. Additionally, we design a multi-objective optimization that enhances the model’s ability to learn latent semantic nuances with supervised contrastive learning. The unified framework emphasizes the interaction and integration of multi-modal data, while multi-objective optimization preserves the complexity of semantic nuances for sarcasm detection. Experimental results on a public multi-modal sarcasm dataset demonstrate the superiority of our model, achieving state-of-the-art performance. The findings highlight the model’s capability to integrate extensive semantic information, demonstrating its effectiveness in the simultaneous interpretation and fusion of multi-modal data for sarcasm detection.随着社交媒体和在线互动的兴起，人们越来越需要能够理解平台中固有的细微差别、多模态通信的分析模型，特别是用于检测讽刺。现有的研究采用多阶段模型沿着大量的语义信息提取和单模态编码器。这些模型通常难以有效地对齐和融合多模态表示。针对这些缺点，我们引入了单阶段广泛语义融合（SSESF）模型，旨在同时处理多模态输入在一个统一的框架，它执行编码和融合在同一架构中共享参数。采用投影机制来克服输入的多样性和广泛的语义信息的整合所带来的挑战。此外，我们设计了一个多目标优化，提高了模型的能力，学习潜在的语义细微差别与监督对比学习。统一的框架强调多模态数据的交互和集成，而多目标优化保留了讽刺检测的语义细微差别的复杂性。在公共多模态讽刺数据集上的实验结果证明了该模型的优越性，达到了最先进的性能。研究结果突出了该模型的能力，整合广泛的语义信息，证明了其有效性的同声传译和融合的多模态数据的讽刺检测。

Keywords

Multi-modal sarcasm detection 多模态讽刺检测
Multi-modal representation learning 多模态表示学习
Multi-modal fusion 多模态融合

1. Introduction

Sarcasm represents a linguistic phenomenon where the intended meaning often diverges from the literal interpretation of the words employed. It typically manifests through irony, mockery, or humorous derision, making its detection particularly challenging. Understanding sarcasm is a challenge due to its reliance on contextual cues, tone of voice, and nuanced expressions. In Fig. 1(a), an image shows a pile of snow in front of a house, accompanied by the text, ‘‘the snowplow went by this morning and dropped off a present in my driveway’’. The incongruity between the image and the text conveys sarcasm, as the snow is sarcastically referred to as a ‘‘present’’. This illustrates the need to consider both visual and textual elements for accurate detection. Fig. 1(b) displays an image with text inside ‘‘your joke was soo funny’’. These examples show that the detection of sarcasm requires a thorough consideration of both visual and textual elements, and a deep dig into extensive semantic information that previous models lack.讽刺是一种语言现象，它所表达的意思往往与所用词语的字面意思相背离。它通常表现为讽刺，嘲弄或幽默的嘲笑，使其检测特别具有挑战性。理解讽刺是一个挑战，因为它依赖于上下文线索，语调和微妙的表达。在图1（a）中，图像显示了房子前面的一堆雪，伴随着文本，“今天早上扫雪机经过，在我的车道上放下了一件礼物”。图像和文本之间的不协调传达了讽刺，因为雪被比喻为“礼物”。这说明需要考虑视觉和文本元素以进行准确检测。图1（B）显示了一个图像，其中的文本“你的笑话太有趣了”。这些例子表明，讽刺的检测需要全面考虑视觉和文本元素，并深入挖掘广泛的语义信息，以前的模型缺乏。
In traditional multi-modal tasks, models typically encode each modality separately before aligning and understanding their semantics. Previous studies like [1] extend multi-stage approach to multi-modal sarcasm detection tasks, which requires a more detailed semantic capture. For example, traditional pre-trained image models often overlook text within images and fail to grasp the semantic details of multiple entities, highlighting a limitation in capturing fine-grained semantics. Bridging this gap, subsequent approaches have begun to incorporate extensive semantic information to enhance detection capabilities, including hashtags [2], image attributes [3], and adjective–noun pairs (ANPs) [4], aiming to enrich the model’s understanding of content. Approaches such as [5,6] utilize object detection for dissecting images into region-based objects for granular visual analysis. Additionally, [7] enhances semantic information extraction through image captioning techniques.在传统的多模态任务中，模型通常在对齐和理解它们的语义之前单独编码每个模态。以前的研究如[1]将多阶段方法扩展到多模态讽刺检测任务，这需要更详细的语义捕获。例如，传统的预训练图像模型往往忽略图像中的文本，无法掌握多个实体的语义细节，突出了捕捉细粒度语义的局限性。为了弥合这一差距，随后的方法已经开始纳入广泛的语义信息来增强检测能力，包括主题标签[2]，图像属性[3]和形容词-名词对（ANP）[4]，旨在丰富模型对内容的理解。诸如[5，6]的方法利用对象检测将图像分割成基于区域的对象以进行粒度视觉分析。此外，[7]通过图像字幕技术增强了语义信息提取。
The integration of extensive semantic information into multi-modal understanding requires sophisticated fusion techniques. Methods such as 2D-Intra-Attention [8] and Graph Convolutional Networks (GCN) [6] have advanced the modeling of cross-modal relationships. However, their emphasis on post-encoding fusion may not fully exploit the inherent capabilities of each modality.将大量语义信息集成到多模态理解中需要复杂的融合技术。诸如2D-Intra-Attention [8]和图卷积网络（GCN）[6]等方法已经推进了跨模态关系的建模。然而，他们对编码后融合的强调可能没有充分利用每种模态的固有功能。
During the multi-modal pre-training phase, the prevalent use of image-text contrastive loss aims to align text with the corresponding image semantics [9,10]. While this approach is effective for basic alignment, it may not adequately address the semantic nuances and discrepancies that are critical in tasks requiring detailed semantic understanding, such as multi-modal sarcasm detection.在多模态预训练阶段，图像-文本对比损失的普遍使用旨在将文本与相应的图像语义对齐[9，10]。虽然这种方法对于基本对齐是有效的，但它可能无法充分解决在需要详细语义理解的任务中至关重要的语义细微差别和差异，例如多模态讽刺检测。
To address these challenges, we propose a single-stage model approach that circumvents the reliance on pre-trained single-modality encoders and their limitations in multi-modal representations fusion. Our model introduces a single-stage model with a projection mechanism designed to accept and integrate multi-modal input along with extensive semantic information. This allows for a more comprehensive understanding and fusion of semantic information across modalities. Furthermore, our model addresses the issue of semantic detail loss by optimizing the integration of contrastive learning and transfer learning objective for downstream tasks. Instead of enforcing alignment, it preserves the nuances of various modalities and extensive semantic information. This optimization includes the redesign of multi-task objective, quantization-aware training, ensuring the model not only retains its original capabilities but also adapts effectively to the requirements of downstream tasks.为解决该问题，提出了一种单级模型方法，避免了对预先训练的单模态编码器的依赖以及它们在多模态表示融合中的局限性。我们的模型引入了一个带有投射机制的单阶段模型，该投射机制被设计为接受和集成多模态输入沿着广泛的语义信息。这允许跨模态的语义信息的更全面的理解和融合。此外，该模型通过优化下游任务的对比学习和迁移学习目标的集成，解决了语义细节丢失的问题。它保留了各种模态的细微差别和广泛的语义信息，而不是强制执行对齐。该优化包括重新设计多任务目标、量化训练，保证模型在保持原有性能的同时，有效适应下游任务的需求。
In this work, we introduce the Single-Stage Extensive Semantic Fusion (SSESF) model, a novel approach designed to transcend the limitations inherent in traditional multi-modal sarcasm detection. By integrating text extracted from images through Optical Character Recognition (OCR) as part of our model’s input, we significantly enhance the semantic understanding of image content. Our model operates in a single-stage framework, eliminating the dependency on the performance of single-modality encoders by simultaneously processing and fusing multiple modalities and extensive semantic information. Our contributions are summarized as follows:在这项工作中，我们介绍了单阶段广泛的语义融合（SSESF）模型，一种新的方法，旨在超越传统的多模态讽刺检测固有的局限性。通过将通过光学字符识别（OCR）从图像中提取的文本集成为我们模型的输入的一部分，我们显着增强了对图像内容的语义理解。我们的模型在一个单阶段的框架中运行，通过同时处理和融合多种模态和广泛的语义信息，消除了对单模态编码器性能的依赖。我们的贡献总结如下：
• The application of a single-stage framework that processes and fuses multiple modalities and extensive semantic information simultaneously, enhancing semantic understanding.应用单阶段框架，同时处理和融合多种模态和广泛的语义信息，增强语义理解。
• The introduction of a projection mechanism in a single-stage model, which effectively handle multiple modalities and additional semantic information, including text from images via OCR, image regions via object detection, and image captions. 在单阶段模型中引入投影机制，有效地处理多种模态和附加语义信息，包括通过OCR从图像中提取文本，通过对象检测提取图像区域，以及图像标题。
• A redesigned fine-tuning process with a multi-objective optimization that focuses on preserving and integrating the unique semantic contributions of each modality and extensive semantic information to improve understanding and adaptability to downstream tasks.重新设计的微调过程，具有多目标优化，专注于保留和整合每个模态的独特语义贡献和广泛的语义信息，以提高对下游任务的理解和适应性。
• Demonstrated superior performance on multi-modal sarcasm detection tasks and other tasks, validating the effectiveness of our approach through experimental results.在多模态讽刺检测任务和其他任务上表现出上级性能，通过实验结果验证了我们方法的有效性。
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