Reviewerly/exHarmony huggingface dataset:
Sense Embeddings are also Biased – Evaluating Social Biases in Static and Contextualised Sense Embeddings

Sense embedding learning methods learn different embeddings for the different senses of an ambiguous word.One sense of an ambiguous word might be socially biased while its other senses remain unbiased.In comparison to the numerous prior work evaluating the social biases in pretrained word embeddings, the biases in sense embeddings have been relatively understudied.We create a benchmark dataset for evaluating the social biases in sense embeddings and propose novel sense-specific bias evaluation measures.We conduct an extensive evaluation of multiple static and contextualised sense embeddings for various types of social biases using the proposed measures.Our experimental results show that even in cases where no biases are found at word-level, there still exist worrying levels of social biases at senselevel, which are often ignored by the word-level bias evaluation measures. 1* Danushka Bollegala holds concurrent appointments as a Professor at University of Liverpool and as an Amazon Scholar.This paper describes work performed at the University of Liverpool and is not associated with Amazon.1 The dataset and evaluation scripts are available at github.
Explainable and efficient deep early warning system for cardiac arrest prediction from electronic health records

Cardiac arrest (CA) is a fatal acute event. The development of new CA early warning system based on time series of vital signs from electronic health records (EHR) has great potential to reduce CA damage. In this process, recursive architecture-based deep learning, as a powerful tool for time series data processing, enables automatically extract features from various monitoring clinical parameters and to further improve the performance for acute critical illness prediction. However, the unexplainable nature and excessive time caused by black box structure with poor parallelism are the limitations of its development, especially in the CA clinical application with strict requirement of emergency treatment and low hidden dangers. In this study, we present an explainable and efficient deep early warning system for CA prediction, which features are captured by an efficient temporal convolutional network (TCN) on EHR clinical parameters sequence and explained by deep Taylor decomposition (DTD) theoretical framework. To demonstrate the feasibility of our method and further evaluate its performance, prediction and explanation experiments were performed. Experimental results show that our method achieves superior CA prediction accuracy compared with standard national early warning score (NEWS), in terms of overall AUROC (0.850 Vs. 0.476) and F1-Score (0.750 Vs. 0.450). Furthermore, our method improves the interpretability and efficiency of deep learning-based CA early warning system. It provides the relevance of prediction results for each clinical parameter and about 1.7 times speed enhancement for system calculation compared with the long short-term memory network.
Quality Controlled Paraphrase Generation

Paraphrase generation has been widely used in various downstream tasks.Most tasks benefit mainly from high quality paraphrases, namely those that are semantically similar to, yet linguistically diverse from, the original sentence.Generating high-quality paraphrases is challenging as it becomes increasingly hard to preserve meaning as linguistic diversity increases.Recent works achieve nice results by controlling specific aspects of the paraphrase, such as its syntactic tree.However, they do not allow to directly control the quality of the generated paraphrase, and suffer from low flexibility and scalability.Here we propose QCPG, a quality-guided controlled paraphrase generation model, that allows directly controlling the quality dimensions.Furthermore, we suggest a method that given a sentence, identifies points in the quality control space that are expected to yield optimal generated paraphrases.We show that our method is able to generate paraphrases which maintain the original meaning while achieving higher diversity than the uncontrolled baseline.The models, the code, and the data can be found in https://github.com/IBM/quality-controlled-paraphrase-generation.
Bargaining Game Based Offloading Service Algorithm for Edge-Assisted Distributed Computing Model

Computation offloading is a new paradigm to provide cloud computing capabilities at the edge of pervasive radio access networks in close proximity to mobile users. In this paper, we propose a new computation offloading model for the 5G networks and beyond. Based on the edge computing platform, intensive computing tasks can be partially offloaded from local devices to edge clouds to supplement the computation capability of resource-limited devices. This approach leverages the edge server&#x2019;s idle computing power to assist individual devices in model training. To implement control decision algorithms for the distributed computing process, we adopt the concepts of different bargaining solutions for the dynamic offloading services. According to the cooperative game theory, the proposed method can maximize the full synergy that gives mutual advantages for devices and edge clouds while improving the system efficiency. Therefore, we can take various benefits to reach a fair-efficient consensus under the edge-assisted distributed computing system environment. Finally, experimental results demonstrate the effectiveness of our bargaining based computation offloading scheme by comparing with the existing state-of-the-art distributed computing protocols; we can accelerate training process thanks to our efficient bargaining approach.
Constrained Multi-Task Learning for Bridging Resolution

We examine the extent to which supervised bridging resolvers can be improved without employing additional labeled bridging data by proposing a novel constrained multi-task learning framework for bridging resolution, within which we (1) design cross-task consistency constraints to guide the learning process; (2) pretrain the entity coreference model in the multitask framework on the large amount of publicly available coreference data; and (3) integrate prior knowledge encoded in rule-based resolvers.Our approach achieves state-of-theart results on three standard evaluation corpora.
Identifying Scholarly Search Skills Based on Resource and Document Selection Behavior among Researchers and Master’s Students in Engineering

This study focuses on differences in the scholarly search approaches of researchers and students in contemporary information environments by examining use of academic resources and elements in search results (such as title or abstract) to identify scholarly search knowledge and skills. To examine how users select documents for different search situations, an online survey of resource selection and the use of search result elements was conducted among 48 researchers and 40 master's students in the field of engineering in Japan. The results show that researchers use resources and search result elements differently for each situation, while students lack that ability.
Assesing The Stability And Selection Performance Of Feature Selection Methods Under Different Data Complexity

Our study aims to investigate the stability and the selection accuracy of feature selection performance under different data complexity. The motivation behind this investigation is that there are significant contributions in the research community from examining the effect of complex data characteristics such as overlapping classes or non-linearity of the decision boundaries on the classification algorithm's performance; however, relatively few studies have investigated the stability and the selection accuracy of feature selection methods with such data characteristics. Also, this study is interested in investigating the interactive effects of the classes overlapped with other data challenges such as small sample size, high dimensionality, and imbalance classes to provide meaningful insights into the root causes for feature selection methods misdiagnosing the relevant features among different real-world data challenges. This analysis will be extended to real-world data to guide the practitioners and researchers in choosing the correct feature selection methods that are more appropriate for a particular dataset. Our study outcomes indicate that using feature selection techniques with datasets of different characteristics may generate different subsets of features under variations to the training data showing that small sample size and overlapping classes have the highest impact on the stability and selection accuracy of feature selection performance, among other data challenges that have been investigated in this study. Also, in this study, we will provide a survey on the current state of research in the feature selection stability context to highlight the area that requires more attention for other researchers.
Learning Reasoning Patterns for Relational Triple Extraction with Mutual Generation of Text and Graph

Relational triple extraction is a critical task for constructing knowledge graphs.Existing methods focused on learning text patterns from explicit relational mentions.However, they usually suffered from ignoring relational reasoning patterns, thus failed to extract the implicitly implied triples.Fortunately, the graph structure of a sentence's relational triples can help find multi-hop reasoning paths.Moreover, the type inference logic through the paths can be captured with the sentence's supplementary relational expressions that represent the real-world conceptual meanings of the paths' composite relations.In this paper, we propose a unified framework to learn the relational reasoning patterns for this task.To identify multi-hop reasoning paths, we construct a relational graph from the sentence (text-tograph generation) and apply multi-layer graph convolutions to it.To capture the relation type inference logic of the paths, we propose to understand the unlabeled conceptual expressions by reconstructing the sentence from the relational graph (graph-to-text generation) in a self-supervised manner.Experimental results on several benchmark datasets demonstrate the effectiveness of our method.
A survey on improving NLP models with human explanations

Training a model with access to human explanations can improve data efficiency and model performance on in-and out-of-domain data.Adding to these empirical findings, similarity with the process of human learning makes learning from explanations a promising way to establish a fruitful human-machine interaction.Several methods have been proposed for improving natural language processing (NLP) models with human explanations, that rely on different explanation types and mechanism for integrating these explanations into the learning process.These methods are rarely compared with each other, making it hard for practitioners to choose the best combination of explanation type and integration mechanism for a specific use-case.In this paper, we give an overview of different methods for learning from human explanations, and discuss different factors that can inform the decision of which method to choose for a specific use-case.
What does the sea say to the shore? A BERT based DST style approach for speaker to dialogue attribution in novels

We present a complete pipeline to extract characters in a novel and link them to their direct-speech utterances.Our model is divided into three independent components: extracting direct-speech, compiling a list of characters, and attributing those characters to their utterances.Although we find that existing systems can perform the first two tasks accurately, attributing characters to direct speech is a challenging problem due to the narrator's lack of explicit character mentions, and the frequent use of nominal and pronominal coreference when such explicit mentions are made.We adapt the progress made on Dialogue State Tracking to tackle a new problem: attributing speakers to dialogues.This is the first application of deep learning to speaker attribution, and it shows that is possible to overcome the need for the hand-crafted features and rules used in the past.Our full pipeline improves the performance of state-of-the-art models by a relative 50% in F1-score.
Learning with Limited Text Data

Natural Language Processing (NLP) has achieved great progress in the past decade on the basis of neural models, which often make use of large amounts of labeled data to achieve state-of-the-art performance. The dependence on labeled data prevents NLP models from being applied to low-resource settings and languages because of the time, money, and expertise that is often required to label massive amounts of textual data. Consequently, the ability to learn with limited labeled data is crucial for deploying neural systems to real-world NLP applications. Recently, numerous approaches have been explored to alleviate the need for labeled data in NLP such as data augmentation and semi-supervised learning. This tutorial aims to provide a systematic and up-to-date overview of these methods in order to help researchers and practitioners understand the landscape of approaches and the challenges associated with learning from limited labeled data, an emerging topic in the computational linguistics community. We will consider applications to a wide variety of NLP tasks (including text classification, generation, and structured prediction) and will highlight current challenges and future directions.
A New Framework for Canny Edge Detector in Hexagonal Lattice

Feature extraction is widely used in image processing applications such as face recognition, character recognition, fingerprint identification and medicine. Edge features is among the most important features for such applications. Canny edge detector is the most popular one and has many benefits in comparison with other methods. Since pixels in hexagonal domain have many benefits in comparison with square domain, this paper presents an efficient Canny edge detector in hexagonal domain. The proposed method includes square to hexagonal transformation and edge detection based on a new algorithm. The proposed method has been evaluated on synthetic and real image datasets with different signal to noise ratios (SNRs). Detected edges in synthetic images show that the proposed hexagonal edge detector outperforms existing methods in 44 cases out of 60 cases with respect to figure of merit (FoM). Finally, results of real images demonstrate the superiority of the proposed method in qualitative analysis of sub-images.
IDIAP Submission@LT-EDI-ACL2022: Homophobia/Transphobia Detection in social media comments

The increased expansion of abusive content on social media platforms negatively affects online users.Transphobic/homophobic content indicates hatred comments for lesbian, gay, transgender, or bisexual people.It leads to offensive speech and causes severe social problems that can make online platforms toxic and unpleasant to LGBT+people, endeavoring to eliminate equality, diversity, and inclusion.In this paper, we present our classification system; given comments, it predicts whether or not it contains any form of homophobia/transphobia with a Zero-Shot learning framework.Our system submission achieved 0.40, 0.85, 0.89 F1-score for Tamil and Tamil-English, English with (1 st , 1 st ,8 th ) ranks respectively.We release our codebase here 1 .
Canary Extraction in Natural Language Understanding Models

Natural Language Understanding (NLU) models can be trained on sensitive information such as phone numbers, zip-codes etc.Recent literature has focused on Model Inversion Attacks (ModIvA) that can extract training data from model parameters.In this work, we present a version of such an attack by extracting canaries inserted in NLU training data.In the attack, an adversary with open-box access to the model reconstructs the canaries contained in the model's training set.We evaluate our approach by performing text completion on canaries and demonstrate that by using the prefix (non-sensitive) tokens of the canary, we can generate the full canary.As an example, our attack is able to reconstruct a four digit code in the training dataset of the NLU model with a probability of 0.5 in its best configuration.As countermeasures, we identify several defense mechanisms that, when combined, effectively eliminate the risk of ModIvA in our experiments.
A Study to Examine the Relationship Between Fear of COVID-19, Psychological Resilience, and Life Satisfaction

The main purpose of this study is to examine the effects of fear of COVID-19 and life satisfaction on the psychological resilience of individuals.In this context, the relationship between the fears of COVID-19 and life satisfaction of adults, which emerged during the pandemic period, with their psychological resilience has been discussed.Personal Information Form, COVID-19 Fear Scale, Psychological Resilience Scale and Life Satisfaction Scale were used as data collection tools for this study.The research sample consists of a total of 263 individuals, 206 women (78.3%) and 57 men (21.7%).Frequency, Correlation and Regression Tests were applied by using SPSS 19 package program in the analysis of the data obtained after the application.According to the results of the research, the fear of COVID-19 negatively affects the psychological resilience levels of individuals; life satisfaction has a positive effect on psychological resilience levels.The results of the research were discussed within the framework of the literature and suggestions were made in this direction.
Visual Interpretability of Capsule Network for Medical Image analysis

Deep learning (DL) models are currently not widely deployed for critical tasks such as in health.This is attributable to the "black box," making it difficult to gain the trust of practitioners.This paper proposes the use of visualizations to enhance performance verification, improve monitoring, ensure understandability, and improve interpretability needed to gain practitioners' confidence.These are demonstrated through the development of a CapsNet model for the recognition of gastrointestinal tract infection.The gastrointestinal tract comprises several organs joined in a long tube from the mouth to the anus.It is susceptive to diseases that are difficult for medics to diagnose, since it is not easy to have physical access to the sick regions.Consequently, manual access and analysis of images of the unhealthy parts requires the skills of an expert, as it is tedious, prone to errors, and costly.Experimental results show that visualizations in the form of post-hoc interpretability can demonstrate the reliability and interpretability of the CapsNet model applied to gastrointestinal tract diseases.The outputs can also be explained to gain practitioners' confidence in paving the way for its adoption in critical areas of society.
A Robust Comparative Analysis of Graph Neural Networks on Dynamic Link Prediction

Graph neural networks (GNNs) are rapidly becoming the dominant way to learn on graphstructured data.Link prediction is a near-universal benchmark for new GNN models.Many advanced models such as Dynamic graph neural networks (DGNNs) specifically target dynamic graphs.However, these models, particularly DGNNs, are rarely compared to each other or existing heuristics.Different works evaluate their models in different ways, thus one cannot compare evaluation metrics and their results directly.Motivated by this, we perform a comprehensive comparison study.We compare link prediction heuristics, GNNs, discrete DGNNs, and continuous DGNNs on the dynamic link prediction task.In total we summarize the results of over 4500 experimental runs (≈ 2 years of computation time).We find that simple link prediction heuristics often perform better than GNNs and DGNNs, different sliding window sizes greatly affect performance, and of all examined graph neural networks, that DGNNs consistently outperform static GNNs.This work is a continuation of our previous work, a foundation of dynamic networks and theoretical review of DGNNs.In combination with our survey, we provide both a theoretical and empirical comparison of DGNNs.
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph Experts

Generative commonsense reasoning (GCR) in natural language is to reason about the commonsense while generating coherent text.Recent years have seen a surge of interest in improving the generation quality of commonsense reasoning tasks.Nevertheless, these approaches have seldom investigated diversity in the GCR tasks, which aims to generate alternative explanations for a real-world situation or predict all possible outcomes.Diversifying GCR is challenging as it expects to generate multiple outputs that are not only semantically different but also grounded in commonsense knowledge.In this paper, we propose MoKGE, a novel method that diversifies the generative reasoning by a mixture of expert (MoE) strategy on commonsense knowledge graphs (KG).A set of knowledge experts seek diverse reasoning on KG to encourage various generation outputs.Empirical experiments demonstrated that MoKGE can significantly improve the diversity while achieving on par performance on accuracy on two GCR benchmarks, based on both automatic and human evaluations.
Analysis of Soft Decision Trees for Passive-Expert Reinforcement Learning

This paper explores the use of soft decision trees [1] in basic reinforcement applications to examine the efficacy of using passive-expert like networks for optimal Q-Value learning on Artificial Neural Networks (ANN).The soft decision tree networks were built using the PyTorch machine learning and the OpenAi's Gym environment frameworks.The conducted research study aimed at assessing the performance of soft decision tree networks on Cartpole as provided in the OpenAi Gym software package.The baseline performance metric that the soft decision tree networks were compared against was a simple Deep Neural Network using several linear layers with ReLU and Softmax activation functions for the input and output layers, respectively.All networks were trained using the Backpropagation algorithm provided generically by PyTorch'sAutograd module.
On Efficiently Acquiring Annotations for Multilingual Models

When tasked with supporting multiple languages for a given problem, two approaches have arisen: training a model for each language with the annotation budget divided equally among them, and training on a high-resource language followed by zero-shot transfer to the remaining languages.In this work, we show that the strategy of joint learning across multiple languages using a single model performs substantially better than the aforementioned alternatives.We also demonstrate that active learning provides additional, complementary benefits.We show that this simple approach enables the model to be data efficient by allowing it to arbitrate its annotation budget to query languages it is less certain on.We illustrate the effectiveness of our proposed method on a diverse set of tasks: a classification task with 4 languages, a sequence tagging task with 4 languages and a dependency parsing task with 5 languages.Our proposed method, whilst simple, substantially outperforms the other viable alternatives for building a model in a multilingual setting under constrained budgets.