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Commonsense knowledge bases play an essential role in a wide range of natural language processing tasks. This paper studies the problem of representation learning for commonsense knowledge bases to effectively incorporate their knowledge into numerical models. Most existing knowledge base representation learning methods are difficult to apply to commonsense knowledge bases since they are much sparser than general knowledge bases. Hence, in this paper, we propose a novel method for commonsense knowledge base representation learning. Specifically, we first model the nodes from multiple views, including word/phase information, context information, and graph information. Then, we design a scoring function to measure whether the commonsense triplets are established through relation representation learning. We conduct extensive experiments on two tasks and the results show that our proposed model outperforms other knowledge base representation learning methods. [ABSTRACT FROM AUTHOR]

Human behavior may be one of the most challenging phenomena to model and validate. This paper proposes a method for automatically extracting and compiling evidence on human behavior determinants into a knowledge graph. The method (1) extracts associations of behavior determinants and choice options in relation to study groups and moderators from published studies using Natural Language Processing and Deep Learning, (2) synthesizes the extracted evidence into a knowledge graph, and (3) sub-selects the model components and relationships that are relevant and robust. The method can be used to either (4a) construct a structurally valid simulation model before proceeding with calibration or (4b) to validate the structure of existing simulation models. To demonstrate the feasibility of the method, we discuss an example implementation with mode of transport as behavior choice. We find that including non-frequently studied significant behavior determinants drastically improves the model's explanatory power in comparison to only including frequently studied variables. The paper serves as a proof-of-concept which can be reused, extended or adapted for various purposes. • The structure of behavior models should be validated using existing evidence. • Deep Learning can help automatize behavior evidence extraction and synthesis. • Infrequently studied significant variables improve model performance drastically. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment classification has become a popular topic in natural language processing. Exploiting dependency syntactic information with graph neural networks has recently become a popular trend. Despite their success, methods that rely heavily on a dependency tree face major challenges. This concerns the alignment of aspects and their word sentiments due to the richness of the language and the fact that a dependency tree might produce noisy signals from unrelated associations. This paper introduces a Dual-Relational Graph Attention Network (DRGAT) that fully exploits syntactic structural information and then the sentiment-aware context (e.g., phrase segmentation and hierarchical structure) of the constituent tree of a sentence. Additional constituency and dependency attention mechanisms provide comprehensive syntactic information across words, thereby enabling an accurate connection between aspect words and corresponding sentiment words. Considering that the original parsed constituency tree may have a large depth, this could lead to words being far apart increasing the computational overhead. The constituency tree of each sentence is dynamically reconstructed by determining the importance of each relational node. Extensive experimental results on six English datasets demonstrated that fully exploiting syntactic information can achieve excellent sentiment classification results. [ABSTRACT FROM AUTHOR]

Recent research has shown that the unaware automation of high-risk decision-making tasks can result in unfair decisions being made. The most common approaches to address this problem adopt definitions of fairness based on protected attributes. Precise annotation of protected attributes enables the application of bias mitigation techniques to commonly unlabeled kinds of data (e.g., images, text, etc.). This paper proposes a framework to automatically annotate protected attributes in data collections. The framework focuses on providing a single interface to annotate protected attributes of different types (e.g., gender, race, etc.) and from different kinds of data. Internally, the framework coordinates multiple sensors to produce the final annotation. Several sensors for textual data are proposed. An optimization search technique is designed to tune the framework to specific domains. Additionally, a small dataset of movie reviews —annotated with gender and sentiment— was created. The evaluation in datasets of texts from diverse domains shows the quality of the annotations and their effectiveness to be used as a proxy to estimate fairness in datasets and machine learning models. The source code is available online for the research community. • A framework to automatically annotate protected attributes in datasets. • Techniques to annotate gender in textual collections with fairness considerations. • Optimization search approach for tuning the framework to custom domains. • Small dataset of movie reviews annotated with gender and sentiment. • Effective use of annotations as a proxy to estimate fairness in datasets. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) backdoor attacks have become a hidden threat to modern NLP applications. Most of the existing defense methods defend against specific types of backdoor attacks, and they generally fail to defend against invisible backdoors with syntactically correct triggers. This paper proposes NLPSweep, a comprehensive defense scheme that can defend against five common types of backdoor attacks, namely, character, word, sentence, homograph, and learnable textual attacks. Specifically, we propose a framework that can discover an effective defense solution without prior knowledge of the attacks. The defense solution is optimized from the framework and can defend against various attacks while ensuring high accuracy. Finally, we verify the effectiveness of NLPSweep on two pretrained models (BERT and XLNET) on three classic datasets (SST-2, IMDB, and OLID) and compare it with five state-of-the-art defense methods, namely, ONION, Pred, RAP, Fine-pruning, and STRIP. The experimental results demonstrate that NLPSweep has an average model accuracy (ACC) greater than 0.922 and that the average attack success rate (ASR) is only 0.202, outperforming the compared methods. Furthermore, NLPSweep is tested on the real-world Yelp dataset and it can effectively defend against backdoor attacks with the ASR less than 0.07 and the ACC greater than 0.973.1 • Existing defense methods only defend against specific backdoor attacks. • Our defense framework can discover an effective defense solution without prior knowledge of the attacks. • Our defense solution can defend against various attacks while ensuring high accuracy. • Tests on three classic datasets show that our defense scheme outperforms recent work. • Our scheme demonstrates operational success on the real-world Yelp dataset, effectively defending against backdoor attacks. [ABSTRACT FROM AUTHOR]

Identifying causality between events is a crucial research task in natural language processing. However, existing methods either ignore background knowledge of events or do not consider interference of knowledge graph noise on event representations. In this paper, we propose a novel Graph Contrast-based Knowledge Augmented Network (GCKAN) for event causality identification task, which integrates comprehensive background knowledge of events from knowledge graphs and alleviates the knowledge graph noise problem. First, a descriptive knowledge augmentation module is proposed to aggregate one-hop neighbor information in knowledge graphs and learn meaningful descriptive knowledge of events. Then, a relational knowledge augmentation module is designed to encode multi-hop path information and learn latent reasoning knowledge between event pairs. In addition, trustworthiness-based and degree-based graph contrastive learning schemas are devised in the two modules respectively, which suppress knowledge graph noise during information aggregation and derive more robust knowledge-aware event representations. Extensive experiments on three public datasets demonstrate the consistent superiority of GCKAN over state-of-the-art knowledge-based techniques. Noise interference experiments and cross-topic adaptation experiments further verify the robustness and generalization of GCKAN. • A graph contrast-based knowledge augmented network for event causality identification is proposed. • The model aggregates descriptive and relational knowledge from knowledge graphs and alleviates labeled data scarcity issue. • Graph contrastive learning schemes are devised to suppress knowledge noise and improve robustness of event representations. • Experimental results demonstrate effectiveness and robustness of the method. [ABSTRACT FROM AUTHOR]