近日，2024年国际计算语言学、语言资源与评价国际联合会议(LREC-COLING 2024)公布了录用论文列表，大连理工大学信息检索研究室共有五篇论文录用为长文。国际计算语言学大会(International Conference on Computational Linguistics，COLING)，是自然语言处理和计算语言学领域的重要国际学术会议，每两年召开一次，被CCF推荐为B类会议。
论文一：”Barking Up the Right Tree”, a GAN-Based Pun Generation Model through Semantic Pruning摘要：In the realm of artificial intelligence and linguistics, the automatic generation of humor, particularly puns, remains a complex task. This paper introduces an innovative approach that employs a Generative Adversarial Network (GAN) and semantic pruning techniques to generate humorous puns. We initiate our process by identifying potential pun candidates via semantic pruning. This is followed by the use of contrastive learning to decode the unique characteristics of puns, emphasizing both correct and incorrect interpretations. The learned features from contrastive learning are utilized within our GAN model to better capture the semantic nuances of puns. Specifically, the generator exploits the pruned semantic tree to generate pun texts, while the discriminator evaluates the generated puns, ensuring both linguistic correctness and humor. Evaluation results highlight our model's capacity to produce semantically coherent and humorous puns, demonstrating an enhancement over prior methods and approach human-level performance. This work contributes significantly to the field of computational humor, advancing the capabilities of automatic pun generation.在人工智能和语言学领域，自动生成幽默，特别是双关语，仍然是一项复杂的任务。本文介绍了一种创新方法，该方法采用生成对抗网络（GAN）和语义修剪技术来生成幽默的双关语。我们首先通过语义修剪识别潜在的双关语候选词。接下来，使用对比学习来解码双关语的独特特征，强调正确和错误的解释。对比学习学到的特征被用于我们的GAN模型中，以更好地捕捉双关语的语义细微差别。具体来说，生成器利用修剪后的语义树来生成双关文本，而鉴别器评估生成的双关语，确保语言的正确性和幽默感。评估结果突显了我们模型产生语义连贯和幽默双关语的能力，显示出相比以往方法的提升，并接近人类水平的表现。这项工作对计算幽默领域做出了重大贡献，推进了自动双关语生成的能力。
论文二：Leveraging Social Context for Humor Recognition and Sense of Humor Evaluation in Social Media with a New Chinese Humor Corpus - HumorWB
作者：博士生曾泽渊 等
摘要：With the development of the Internet, social media has produced a large amount of user-generated data, which brings new challenges for humor computing. Traditional humor computing research mainly focuses on the content, while neglecting the information of interaction relationships in social media. In addition, both content and users are important in social media, while existing humor computing research mainly focuses on content rather than people. To address these problems, we model the information transfer and entity interactions in social media as a heterogeneous graph, and create the first dataset which introduces the social context information - HumorWB, which is collected from Chinese social media - Weibo. Two humor-related tasks are designed in the dataset. One is a content-oriented humor recognition task, and the other is a novel humor evaluation task. For the above tasks, we propose a graph-based model that uses heterogeneous graph neural networks to optimize node representation for downstream tasks. Experimental results demonstrate the effectiveness of feature extraction and graph representation learning methods in the model, as well as the necessity of introducing social context information.
随着互联网的发展，社交媒体产生了大量的用户生成数据，这给幽默计算带来了新的挑战。传统的幽默计算研究主要集中在内容方面，而忽视了社交媒体中的交互关系信息。此外，内容和用户在社交媒体中都很重要，而现有的幽默计算研究主要集中在内容而不是人。为了解决这些问题，我们将社会媒体中的信息传递和实体交互作为一个异构图进行建模，并创建了第一个引入社交背景信息的数据集——HumorWB，这是从中国社会媒体-微博收集的。在数据集中设计了两个与幽默相关的任务。一个是以内容为导向的幽默识别任务，另一个是新颖的幽默感评价任务。针对上述任务，我们提出了一个基于图的模型，该模型使用异构图神经网络来优化下游任务的节点表示。实验结果表明了该模型中特征提取和图表示学习方法的有效性，以及引入社交背景信息的必要性。
论文三：Take its Essence, Discard its Dross! Debiasing for Toxic Language Detection via Counterfactual Causal Effect
作者：博士生卢俊宇 等
摘要：Current methods of toxic language detection (TLD) typically rely on specific tokens to conduct decisions, which makes them suffer from lexical bias, leading to inferior performance and generalization. Lexical bias has both “useful” and “misleading” impacts on understanding toxicity. Unfortunately, instead of distinguishing between these impacts, current debiasing methods typically eliminate them indiscriminately, resulting in a degradation in the detection accuracy of the model. To this end, we propose a Counterfactual Causal Debiasing Framework (CCDF) to mitigate lexical bias in TLD. It preserves the “useful impact” of lexical bias and eliminates the “misleading impact”. Specifically, we first represent the total effect of the original sentence and biased tokens on decisions from a causal view. We then conduct counterfactual inference to exclude the direct causal effect of lexical bias from the total effect. Empirical evaluations demonstrate that the debiased TLD model incorporating CCDF achieves state-of-the-art performance in both accuracy and fairness compared to competitive baselines applied on several vanilla models. The generalization capability of our model outperforms current debiased models for out-of-distribution data.
目前的有毒言论检测(TLD)方法通常依赖于特定的令牌来进行决策，这使得它们受到词汇偏差的影响，导致性能和泛化性能较差。词汇偏差对理解毒性既有“有用的”影响，也有“误导的”影响。不幸的是，目前的去偏方法通常不加区别地消除这些影响，而不是对影响加以区分，从而导致模型检测精度的下降。为此，我们提出了一个反事实因果去偏框架(CCDF)来减轻TLD中的词汇偏倚。它保留了词汇偏见的“有用的”影响，消除了“误导的”影响。具体来说，我们首先从因果关系的角度表示原始句子和有偏见的标记对决策的总影响。然后，我们进行反事实推理，从总效应中排除词汇偏差的直接因果效应。实证评估表明，与应用于几个底座模型的竞争性基线相比，引入CCDF的去偏见TLD模型在准确性和公平性方面都达到了最先进的水平。我们的模型的泛化能力优于当前分布外数据的去偏模型。
论文四：RENN: A Rule Embedding Enhanced Neural Network Framework for Temporal Knowledge Graph Completion.
作者：徐博老师 等
摘要：Temporal knowledge graph completion is a critical task within the knowledge graph domain. Existing approaches encompass deep neural network-based methods for temporal knowledge graph embedding and rule-based logical symbolic reasoning. However, the former may not adequately account for structural dependencies between relations. Conversely, the latter method relies heavily on strict logical rule reasoning and lacks robustness in the face of fuzzy or noisy data. In response to these challenges, we present RENN, a groundbreaking framework that enhances temporal knowledge graph completion through rule embedding. RENN employs a three-step approach. First, it utilizes a temporary random walk to extract temporal logic rules. Then, it pre-trains by learning embeddings for each logical rule and its associated relations, thereby enhancing the likelihood of existing quadruples and logical rules. Finally, it incorporates the embeddings of logical rules into the deep neural network. Our methodology has been validated through experiments conducted on various temporal knowledge graph models and datasets, consistently demonstrating its effectiveness and potential in improving temporal knowledge graph completion.
时间知识图补全是知识图领域的一项关键任务。现有的方法包括基于深度神经网络的时态知识图嵌入方法和基于规则的逻辑符号推理方法。然而，前者可能无法充分解释关系之间的结构性依赖关系。相反，后一种方法严重依赖于严格的逻辑规则推理，在面对模糊或噪声数据时缺乏鲁棒性。为了应对这些挑战，我们提出了RENN，这是一个突破性的框架，通过规则嵌入增强了时间知识图的完成。人人网采用了三步走的方法。首先，它利用临时随机漫步来提取时间逻辑规则。然后，它通过学习每个逻辑规则及其关联关系的嵌入来进行预训练，从而提高现有四重组和逻辑规则的可能性。最后，将逻辑规则嵌入到深度神经网络中。我们的方法已经在各种时间知识图模型和数据集上进行了实验验证，一致地证明了它在提高时间知识图完成度方面的有效性和潜力。
论文五：Temporal Knowledge Graph Reasoning with Dynamic Hypergraph Embedding
作者：徐博老师 等
摘要：Reasoning over the Temporal Knowledge Graph (TKG) that predicts facts in the future has received much attention. Most previous works attempt to model temporal dynamics with knowledge graphs and graph convolution networks. However, these methods lack the consideration of high-order interactions between objects in TKG, which is an important factor to predict future facts. To address this problem, we introduce dynamic hypergraph embedding for temporal knowledge graph reasoning. Specifically, we obtain high-order interactions by constructing hypergraphs based on temporal knowledge graphs at different timestamps. Besides, we integrate the differences caused by time into the hypergraph representation in order to fit TKG. Then, we adapt dynamic meta-embedding for temporal hypergraph representation that allows our model to choose the appropriate high-order interactions for downstream reasoning. Experimental results on public TKG datasets show that our method outperforms the baselines. Furthermore, the analysis part demonstrates that the proposed method brings good interpretation for the predicted results.
预测未来事实的时间知识图(TKG)推理受到了广泛关注。大多数先前的工作都试图用知识图和图卷积网络来建模时间动力学。然而，这些方法缺乏对TKG中物体之间高阶相互作用的考虑，而这是预测未来事实的重要因素。为了解决这个问题，我们引入了动态超图嵌入来进行时态知识图推理。具体而言，我们通过构造基于时间知识图的超图来获得不同时间戳下的高阶交互。此外，为了拟合TKG，我们将时间引起的差异整合到超图表示中。然后，我们将动态元嵌入用于时间超图表示，使我们的模型能够为下游推理选择适当的高阶交互。在公共TKG数据集上的实验结果表明，我们的方法优于基线。分析部分表明，该方法对预测结果具有较好的解释效果。