The scarcity of high-quality large-scale labeled datasets poses a huge challenge for employing deep learning models in video deception detection. To address this issue, inspired by the psychological theory on the relation between deception and expressions, we propose a novel method called AFFAKT in this paper, which enhances the classification performance by transferring useful and correlated knowledge from a large facial expression dataset. Two key challenges in knowledge transfer arise: 1) how much knowledge of facial expression data should be transferred and 2) how to effectively leverage transferred knowledge for the deception classification model during inference. Specifically, the optimal relation mapping between facial expression classes and deception samples is firstly quantified using proposed H-OTKT module and then transfers knowledge from the facial expression dataset to deception samples. Moreover, a correlation prototype within another proposed module SRKB is well designed to retain the invariant correlations between facial expression classes and deception classes through momentum updating. During inference, the transferred knowledge is fine-tuned with the correlation prototype using a sample-specific re-weighting strategy. Experimental results on two deception detection datasets demonstrate the superior performance of our proposed method. The interpretability study reveals high associations between deception and negative affections, which coincides with the theory in psychology.

ABSTRACT Despite numerous applications of deep learning technologies on critical tasks in various domains, advanced deep neural networks (DNNs) face persistent safety and security challenges, such as the overconfidence in predicting out‐of‐distribution samples and susceptibility to adversarial examples. Thorough testing by exploring the input space serves as a key strategy to ensure their robustness and trustworthiness of these networks. However, existing testing methods focus on disclosing more erroneous model behaviours, overlooking the validity of the generated test inputs. To mitigate this issue, we investigate devising valid test input generation method for DNNs from a predictive uncertainty perspective. Through a large‐scale empirical study across 11 predictive uncertainty metrics for DNNs, we explore the correlation between validity and uncertainty of test inputs. Our findings reveal that the predictive entropy‐based and ensemble‐based uncertainty metrics effectively characterize the input validity demonstration. Building on these insights, we introduce UCTest, an uncertainty‐guided deep learning testing approach, to efficiently generate valid and authentic test inputs. We formulate a joint optimization objective: to uncover the model's misbehaviours by maximizing the loss function and concurrently generate valid test input by minimizing uncertainty. Extensive experiments demonstrate that our approach outperforms the current testing methods in generating valid test inputs. Furthermore, incorporating natural variation through data augmentation techniques into UCTest effectively boosts the diversity of generated test inputs.

The task of zero-shot relation extraction is a very important research topic in the field of information extraction, which can effectively alleviate the issue of no training samples for some relations. Existing zero-shot relation extraction methods based on pre-trained language models (PLMs) always extract features from sentences, but cannot provide satisfactory semantic representations when there are conflicts between the task and the knowledge contained in PLMs. To address the issue, based on Prompt paradigm, a novel dynamic Prompt-driven method is proposed, aimed at fully stimulating the knowledge from PLMs to promote relation extraction. Specifically, the task of zero-shot relation extraction is defined as a masked language model (MLM) task, where[MASK]representation is qualified as relation representation for classification. Further, the key problems of Prompt paradigm for zero-shot relation extraction are explored, including the effect of template in Prompt and representation degradation. On this basis, in the new model, we utilize dynamic template to provide greater flexibility and introduce contrastive learning to optimize the semantic representation. Extensive experiments are conducted on three benchmark datasets (FewRel, TACRED, and Wiki-ZSL), demonstrating that the proposed model achieves the state-of-the-art performance with solving the existing problems.

Shyness trait is a double-edged personality trait, and could pose a risk in early childhood for later adjustment difficulties. Therefore, it is necessary to pay attention to children with shyness trait and properly supervise them in early education, where the key problem is shyness trait recognition. Although some psychological methods of shyness measurement have been presented, they are high-cost and can only get a one-sided result limited by the targeted subjects. To develop an automated method of shyness trait recognition, we collect a dataset, containing online writing data of 1,754 schoolchildren from an educational website and ground truth labels obtained by a professional scale. The natural implicitness makes shyness trait difficult to be observed from a single point of view and the class imbalanced problem increases the challenge. In this article, a novel shyness trait recognition framework is proposed, which extracts multi-view features of online writing, including document-view, sentence-view and temporality-view ones. Different strategies with different features are applied to each single-view prediction and the multi-view prediction is made by a weighted voting ensemble. To verify the effectiveness, extensive experiments are conducted on the real-world dataset, demonstrating that the multi-view prediction significantly outperforms each single-view prediction and some advanced models of multi-view learning.

Abstract Nowadays, automated essay evaluation (AEE) systems play an important role in evaluating essays and have been successfully used in large-scale writing assessments. However, existing AEE systems mostly focus on grammar or shallow content measurements rather than higher-order traits such as ideas. This paper proposes a new formulation of graph-based features for concept maps using word embeddings to evaluate the quality of ideas for Chinese compositions. The concept map derived from the student’s composition is composed of the concepts appearing in the essay and the co-occurrence relationship between the concepts. By utilizing real compositions written by eighth-grade students from a large-scale assessment, the scoring accuracy of the computer evaluation system (named AECC-I: Automated Evaluation for Chinese Compositions—Ideas) is higher than the baselines. The results indicate that the proposed method deepens the construct-relevant coverage of automatic ideas evaluation in compositions and that it can provide constructive feedback for students.

Xuetao Tian, Liping Jing, Lu He, Feng Liu. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.