Reviewerly/exHarmony huggingface dataset:
The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning

Multilingual semantic parsing aims to leverage the knowledge from the high-resource languages to improve low-resource semantic parsing, yet commonly suffers from the data imbalance problem.Prior works propose to utilize the translations by either humans or machines to alleviate such issues.However, human translations are expensive, while machine translations are cheap but prone to error and bias.In this work, we propose an active learning approach that exploits the strengths of both human and machine translations by iteratively adding small batches of human translations into the machine-translated training set.Besides, we propose novel aggregated acquisition criteria that help our active learning method select utterances to be manually translated.Our experiments demonstrate that an ideal utterance selection can significantly reduce the error and bias in the translated data, resulting in higher parser accuracies than the parsers merely trained on the machine-translated data.
Using Collostructional Analysis to evaluate BERT’s representation of linguistic constructions

Collostructional analysis is a technique devised to find correlations between particular words and linguistic constructions in order to analyse meaning associations of these constructions. Contrasting collostructional analysis results with output from BERT might provide insights into the way BERT represents the meaning of linguistic constructions. This study tests to what extent English BERT's meaning representations correspond to known constructions from the linguistics literature by means of two tasks that we propose. Firstly, by predicting the words that can be used in open slots of constructions, the meaning associations of more lexicalized constructions can be observed. Secondly, by finding similar sequences using BERT's output embeddings and manually reviewing the resulting sentences, we can observe whether instances of less lexicalized constructions are clustered together in semantic space. These two methods show that BERT represents constructional meaning to a certain extent, but does not separate instances of a construction from a near-synonymous construction that has a different form.
Learning Event-aware Measures for Event Coreference Resolution

Researchers are witnessing knowledge-inspired natural language processing shifts the focus from entity-level to event-level, whereas event coreference resolution is one of the core challenges.This paper proposes a novel model for within-document event coreference resolution.On the basis of event but not entity as before, our model learns and integrates multiple representations from both event alone and event pair.For the former, we introduce multiple linguistics-motivated event alone features for more discriminative event representations.For the latter, we consider multiple similarity measures to capture the distinctions of event pairs.Our proposed model achieves new state-of-theart on the ACE 2005 benchmark, demonstrating the effectiveness of our proposed framework.
Sequential Integrated Gradients: a simple but effective method for explaining language models

Several explanation methods such as Integrated Gradients (IG) can be characterised as path-based methods, as they rely on a straight line between the data and an uninformative baseline. However, when applied to language models, these methods produce a path for each word of a sentence simultaneously, which could lead to creating sentences from interpolated words either having no clear meaning, or having a significantly different meaning compared to the original sentence. In order to keep the meaning of these sentences as close as possible to the original one, we propose Sequential Integrated Gradients (SIG), which computes the importance of each word in a sentence by keeping fixed every other words, only creating interpolations between the baseline and the word of interest. Moreover, inspired by the training procedure of language models, we also propose to replace the baseline token “pad” with the trained token “mask”. While being a simple improvement over the original IG method, we show on various models and datasets that SIG proves to be a very effective method for explaining language models.
Discovering Differences in the Representation of People using Contextualized Semantic Axes

A common paradigm for identifying semantic differences across social and temporal contexts is the use of static word embeddings and their distances.In particular, past work has compared embeddings against "semantic axes" that represent two opposing concepts.We extend this paradigm to BERT embeddings, and construct contextualized axes that mitigate the pitfall where antonyms have neighboring representations.We validate and demonstrate these axes on two people-centric datasets: occupations from Wikipedia, and multi-platform discussions in extremist, men's communities over fourteen years.In both studies, contextualized semantic axes can characterize differences among instances of the same word type.In the latter study, we show that references to women and the contexts around them have become more detestable over time.
Knowledge Prompting in Pre-trained Language Model for Natural Language Understanding

Knowledge-enhanced Pre-trained Language Model (PLM) has recently received significant attention, which aims to incorporate factual knowledge into PLMs.However, most existing methods modify the internal structures of fixed types of PLMs by stacking complicated modules, and introduce redundant and irrelevant factual knowledge from knowledge bases (KBs).In this paper, to address these problems, we introduce a seminal knowledge prompting paradigm and further propose a knowledge-prompting-based PLM framework KP-PLM.This framework can be flexibly combined with existing mainstream PLMs.Specifically, we first construct a knowledge sub-graph from KBs for each context.Then we design multiple continuous prompts rules and transform the knowledge sub-graph into natural language prompts.To further leverage the factual knowledge from these prompts, we propose two novel knowledge-aware self-supervised tasks including prompt relevance inspection and masked prompt modeling.Extensive experiments on multiple natural language understanding (NLU) tasks show the superiority of KP-PLM over other state-of-the-art methods in both full-resource and low-resource settings 1 .
JDDC 2.1: A Multimodal Chinese Dialogue Dataset with Joint Tasks of Query Rewriting, Response Generation, Discourse Parsing, and Summarization

The popularity of multimodal dialogue has stimulated the need for a new generation of dialogue agents with multimodal interactivity.When users communicate with customer service, they may express their requirements by means of text, images, or even videos. Visual information usually acts as discriminators for product models, or indicators of product failures, which play an important role in the E-commerce scenario.On the other hand, detailed information provided by the images is limited, and typically, customer service systems cannot understand the intent of users without the input text.Thus, bridging the gap between the image and text is crucial for communicating with customers.In this paper, we construct JDDC 2.1, a large-scale multimodal multi-turn dialogue dataset collected from a mainstream Chinese E-commerce platform, containing about 246K dialogue sessions, 3M utterances, and 507K images, along with product knowledge bases and image category annotations. Over our dataset, we jointly define four tasks: the multimodal dialogue response generation task,the multimodal query rewriting task, the multimodal dialogue discourse parsing task, and the multimodal dialogue summarization task.JDDC 2.1 is the first corpus with annotations for all the above tasks over the same dialogue sessions, which facilitates the comprehensive research around the dialogue.In addition, we present several text-only and multimodal baselines and show the importance of visual information for these tasks. Our dataset and implements will be publicly available.
Generative Data Augmentation with Contrastive Learning for Zero-Shot Stance Detection

Stance detection aims to identify whether the author of an opinionated text is in favor of, against, or neutral towards a given target.Remarkable success has been achieved when sufficient labeled training data is available.However, it is labor-intensive to annotate sufficient data and train the model for every new target.Therefore, zero-shot stance detection, aiming at identifying stances of unseen targets with seen targets, has gradually attracted attention.Among them, one of the important challenges is to reduce the domain transfer between seen and unseen targets.To tackle this problem, we propose a generative data augmentation approach to generate training samples containing targets and stances for testing data, and map the real samples and generated synthetic samples into the same embedding space with contrastive learning, then perform the final classification based on the augmented data.We evaluate our proposed model on two benchmark datasets.Experimental results show that our approach achieves state of-the-art performance on most topics in the task of zero-shot stance detection.
Are All Spurious Features in Natural Language Alike? An Analysis through a Causal Lens

The term 'spurious correlations' has been used in NLP to informally denote any undesirable feature-label correlations.However, a correlation can be undesirable because (i) the feature is irrelevant to the label (e.g.punctuation in a review), or (ii) the feature's effect on the label depends on the context (e.g.negation words in a review), which is ubiquitous in language tasks.In case (i), we want the model to be invariant to the feature, which is neither necessary nor sufficient for prediction.But in case (ii), even an ideal model (e.g.humans) must rely on the feature, since it is necessary (but not sufficient) for prediction.Therefore, a more fine-grained treatment of spurious features is needed to specify the desired model behavior.We formalize this distinction using a causal model and probabilities of necessity and sufficiency, which delineates the causal relations between a feature and a label.We then show that this distinction helps explain results of existing debiasing methods on different spurious features, and demystifies surprising results such as the encoding of spurious features in model representations after debiasing.
HEGEL: Hypergraph Transformer for Long Document Summarization

Extractive summarization for long documents is challenging due to the extended structured input context.The long-distance sentence dependency hinders cross-sentence relations modeling, the critical step of extractive summarization.This paper proposes HEGEL, a hypergraph neural network for long document summarization by capturing high-order crosssentence relations.HEGEL updates and learns effective sentence representations with hypergraph transformer layers and fuses different types of sentence dependencies, including latent topics, keywords coreference, and section structure.We validate HEGEL by conducting extensive experiments on two benchmark datasets, and experimental results demonstrate the effectiveness and efficiency of HEGEL.
m^4 Adapter: Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter

Multilingual neural machine translation models (MNMT) yield state-of-the-art performance when evaluated on data from a domain and language pair seen at training time.However, when a MNMT model is used to translate under domain shift or to a new language pair, performance drops dramatically.We consider a very challenging scenario: adapting the MNMT model both to a new domain and to a new language pair at the same time.In this paper, we propose m 4 Adapter (Multilingual Multi-Domain Adaptation for Machine Translation with a Meta-Adapter), which combines domain and language knowledge using meta-learning with adapters.We present results showing that our approach is a parameter-efficient solution which effectively adapts a model to both a new language pair and a new domain, while outperforming other adapter methods.An ablation study also shows that our approach more effectively transfers domain knowledge across different languages and language information across different domains. 1
Unsupervised Entity Linking with Guided Summarization and Multiple-Choice Selection

Entity linking, the task of linking potentially ambiguous mentions in texts to corresponding knowledge-base entities, is an important component for language understanding.We address two challenge in entity linking: how to leverage wider contexts surrounding a mention, and how to deal with limited training data.We propose a fully unsupervised model called SumMC that first generates a guided summary of the contexts conditioning on the mention, and then casts the task to a multiple-choice problem where the model chooses an entity from a list of candidates.In addition to evaluating our model on existing datasets that focus on named entities, we create a new dataset that links noun phrases from WikiHow to Wikidata.We show that our SumMC model achieves stateof-the-art unsupervised performance on our new dataset and on existing datasets.
Multitarget-Tracking Method Based on the Fusion of Millimeter-Wave Radar and LiDAR Sensor Information for Autonomous Vehicles

Multitarget tracking based on multisensor fusion perception is one of the key technologies to realize the intelligent driving of automobiles and has become a research hotspot in the field of intelligent driving. However, most current autonomous-vehicle target-tracking methods based on the fusion of millimeter-wave radar and lidar information struggle to guarantee accuracy and reliability in the measured data, and cannot effectively solve the multitarget-tracking problem in complex scenes. In view of this, based on the distributed multisensor multitarget tracking (DMMT) system, this paper proposes a multitarget-tracking method for autonomous vehicles that comprehensively considers key technologies such as target tracking, sensor registration, track association, and data fusion based on millimeter-wave radar and lidar. First, a single-sensor multitarget-tracking method suitable for millimeter-wave radar and lidar is proposed to form the respective target tracks; second, the Kalman filter temporal registration method and the residual bias estimation spatial registration method are used to realize the temporal and spatial registration of millimeter-wave radar and lidar data; third, use the sequential m-best method based on the new target density to find the track the correlation of different sensors; and finally, the IF heterogeneous sensor fusion algorithm is used to optimally combine the track information provided by millimeter-wave radar and lidar, and finally form a stable and high-precision global track. In order to verify the proposed method, a multitarget-tracking simulation verification in a high-speed scene is carried out. The results show that the multitarget-tracking method proposed in this paper can realize the track tracking of multiple target vehicles in high-speed driving scenarios. Compared with a single-radar tracker, the position, velocity, size, and direction estimation errors of the track fusion tracker are reduced by 85.5%, 64.6%, 75.3%, and 9.5% respectively, and the average value of GOSPA indicators is reduced by 19.8%; more accurate target state information can be obtained than a single-radar tracker.
Credit Card Fraud Detection Based on Improved Variational Autoencoder Generative Adversarial Network

The rapid spread of mobile banking and e-commerce has coincided with a dramatic increase in fraudulent online payments in recent years. Although machine learning and deep learning are widely used in credit card fraud detection, the typical credit card transaction data set is unbalanced, and the fraud data is much less than the normal transaction data, limiting the effectiveness of traditional binary classification algorithms. To overcome this issue, researchers oversample minority class data and utilize ensemble learning classification algorithms. However, oversampling still has disadvantages. Hence, we improve the generator part of the Variational Autoencoder Generative Adversarial Network (VAEGAN) and propose a new oversampling method that generates convincing and diverse minority class data. The training set is enhanced by generating minority class fraud data to train the ensemble learning classification model. The method is tested on an open credit card dataset, with the experimental results demonstrating that the oversampling method utilizing the improved VAEGAN is superior to the oversampling method of Generative Adversarial Network (GAN), Variational Autoencoder (VAE), and Synthetic Minority Oversampling Technique (SMOTE) in terms of Precision, F1_score, and other indicators. The oversampling method based on the improved VAEGAN effectively deals with the classification problem of imbalanced data.
Quantum Linear Key-Recovery Attacks Using the QFT

The Quantum Fourier Transform is a fundamental tool in quantum cryptanalysis. In symmetric cryptanalysis, hidden shift algorithms such as Simon’s, which rely on the QFT, have been used to obtain structural attacks on some very specific block ciphers. The Fourier Transform is also used in classical cryptanalysis, for example in FFT-based linear key-recovery attacks introduced by Collard et al. (ICISC 2007). Whether such techniques can be adapted to the quantum setting has remained so far an open question. In this paper, we introduce a new framework for quantum linear key-recovery attacks using the QFT. These attacks loosely follow the classical method of Collard et al., in that they rely on the fast computation of a correlation state in which experimental correlations, rather than being directly accessible, are encoded in the amplitudes of a quantum state. The experimental correlation is a statistic that is expected to be higher for the good key, and on some conditions, the increased amplitude creates a speedup with respect to an exhaustive search of the key. The same method also yields a new family of structural attacks, and new examples of quantum speedups beyond quadratic using classical known-plaintext queries.
Lattice Signature with Efficient Protocols, Application to Anonymous Credentials

Digital signature is an essential primitive in cryptography, which can be used as the digital analogue of handwritten signatures but also as a building block for more complex systems. In the latter case, signatures with specific features are needed, so as to smoothly interact with the other components of the systems, such as zero-knowledge proofs. This has given rise to so-called signatures with efficient protocols, a versatile tool that has been used in countless applications. Designing such signatures is however quite difficult, in particular if one wishes to withstand quantum computing. We are indeed aware of only one post-quantum construction, proposed by Libert et al. at Asiacrypt'16, yielding very large signatures and proofs. In this paper, we propose a new construction that can be instantiated in both standard lattices and structured ones, resulting in each case in dramatic performance improvements. In particular, the size of a proof of message-signature possession, which is one of the main metrics for such schemes, can be brought down to less than 650 KB. As our construction retains all the features expected from signatures with efficient protocols, it can be used as a drop-in replacement in all systems using them, which mechanically improves their own performance, and has thus a direct impact on many applications. It can also be used to easily design new privacy-preserving mechanisms. As an example, we provide the first lattice-based anonymous credentials system.
FLSG: A Novel Defense Strategy Against Inference Attacks in Vertical Federated Learning

As a new machine learning paradigm, Federated Learning (FL) empowers different participants to jointly train a more effective model than traditional Machine Learning (ML). Unlike Horizontal Federated Learning (HFL), which expands the sample space by aggregating local models, Vertical Federated Learning (VFL) is suitable for scenarios where the sample ID between participants is the same but differs in sample characteristics. For a long time, VFL has been considered safe due to no data exchange and heterogeneity between parties. However, the recently proposed label inference attacks pose a significant security threat to VFL. Specifically, by adding randomly initialized layers to the top of local models, the passive label inference attack can infer tens of thousands of local participants’ private data with only 40 auxiliary labels. Since the attack is entirely local, using privacy protection technologies such as differential privacy cannot effectively defend against these attacks. Therefore, we propose a new privacy protection scheme called Federated Learning Similar Gradients (FLSG) to defend against this attack. Unlike differential privacy, the FLSG scheme randomly generates gradients of a Gaussian distribution similar in dimension to the original gradients and calculates their cosine distance. If the distance is less than a certain threshold, the gradients is used instead of the original gradients to pass to the local participants. We conducted extensive evaluations on six real-world datasets, and the results show that FLSG provides a better defensive effect at lower computational overhead than other known methods when defending the passive label inference attack.
The Role of Internal Auditors Characteristics in Cybersecurity Risk Assessment in Financial-Based Business Organisations: A Conceptual Review

Purpose: This paper aims to establish a theoretical framework that will enhance the examination of the role of internal auditors in cybersecurity risk assessment in financial-based business organizations. Financial-based business organizations are institutions or companies that render financial services to public and private stakeholders in an economy. It is a powerful sector in the economy of every country. This drive poses a lot of challenges to organizations. Hence, business organizations strategically devised a means to safeguard the integrity, confidentiality, and availability of information. Also, innovation poses many risks and threats to the internal audit function in an organization. Theoretical Framework/Findings: Using the competency and planned behaviour theories (McClelland 1973 and Ajzen,1991), this study disclosed that the task performance of cybersecurity risk assessment by the internal auditor is influenced by the required internal auditor’s characteristics of professional ethics of integrity and objectivity, personality traits, professional skills competency professional knowledge competency and deterrence and rewards to advise the management on the implications of cyber security risk on business organisations for monitoring and mitigations. Methodology: A literature review approach is adopted to highlight the role of internal auditors in cyber security risk assessment in financial–based business organizations. Research Limitation/Implication: This conceptual paper has consequences for the practice of internal auditing. This approach is helpful to academic scholars in testing it out in the real world. This model is helpful to practitioners when evaluating the function of IAs in the cybersecurity risk assessment context. Originality/Values: Earlier auditing-related studies haven't addressed this problem. This study makes an effort to close such a gap and investigate the subject of the internal auditor’s characteristics and cyber security risk assessment among financial-based organizations.