pwc-archive/papers-with-abstracts huggingface dataset:
A Comprehensive Dataset for German Offensive Language and Conversation Analysis
Authors: Christoph Demus, Jonas Pitz, Mina Schütz, Nadine Probol, Melanie Siegel, Dirk Labudde
Date: 2022-07-01 00:00:00
Proceeding: NAACL (WOAH) 2022 7
Tasks: Hate Speech Detection

In this work, we present a new publicly available offensive language dataset of 10.278 German social media comments collected in the first half of 2021 that were annotated by in total six annotators. With twelve different annotation categories, it is far more comprehensive than other datasets, and goes beyond just hate speech detection. The labels aim in particular also at toxicity, criminal relevance and discrimination types of comments.Furthermore, about half of the comments are from coherent parts of conversations, which opens the possibility to consider the comments’ contexts and do conversation analyses in order to research the contagion of offensive language in conversations.
Distributional properties of political dogwhistle representations in Swedish BERT
Authors: Niclas Hertzberg, Robin Cooper, Elina Lindgren, Björn Rönnerstrand, Gregor Rettenegger, Ellen Breitholtz, Asad Sayeed
Date: 2022-07-01 00:00:00
Proceeding: NAACL (WOAH) 2022 7
Tasks: Sentence, Survey

“Dogwhistles” are expressions intended by the speaker have two messages: a socially-unacceptable “in-group” message understood by a subset of listeners, and a benign message intended for the out-group. We take the result of a word-replacement survey of the Swedish population intended to reveal how dogwhistles are understood, and we show that the difficulty of annotating dogwhistles is reflected in the separability in the space of a sentence-transformer Swedish BERT trained on general data.
Accounting for Offensive Speech as a Practice of Resistance
Authors: Mark Diaz, Razvan Amironesei, Laura Weidinger, Iason Gabriel
Date: 2022-07-01 00:00:00
Proceeding: NAACL (WOAH) 2022 7
Tasks: Hate Speech Detection, Philosophy

Tasks such as toxicity detection, hate speech detection, and online harassment detection have been developed for identifying interactions involving offensive speech. In this work we articulate the need for a relational understanding of offensiveness to help distinguish denotative offensive speech from offensive speech serving as a mechanism through which marginalized communities resist oppressive social norms. Using examples from the queer community, we argue that evaluations of offensive speech must focus on the impacts of language use. We call this the cynic perspective– or a characteristic of language with roots in Cynic philosophy that pertains to employing offensive speech as a practice of resistance. We also explore the degree to which NLP systems may encounter limits to modeling relational context.
Towards a Multi-Entity Aspect-Based Sentiment Analysis for Characterizing Directed Social Regard in Online Messaging
Authors: Joan Zheng, Scott Friedman, Sonja Schmer-Galunder, Ian Magnusson, Ruta Wheelock, Jeremy Gottlieb, Diana Gomez, Christopher Miller
Date: 2022-07-01 00:00:00
Proceeding: NAACL (WOAH) 2022 7
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis

Online messaging is dynamic, influential, and highly contextual, and a single post may contain contrasting sentiments towards multiple entities, such as dehumanizing one actor while empathizing with another in the same message.These complexities are important to capture for understanding the systematic abuse voiced within an online community, or for determining whether individuals are advocating for abuse, opposing abuse, or simply reporting abuse. In this work, we describe a formulation of directed social regard (DSR) as a problem of multi-entity aspect-based sentiment analysis (ME-ABSA), which models the degree of intensity of multiple sentiments that are associated with entities described by a text document. Our DSR schema is informed by Bandura’s psychosocial theory of moral disengagement and by recent work in ABSA. We present a dataset of over 2,900 posts and sentences, comprising over 24,000 entities annotated for DSR over nine psychosocial dimensions by three annotators. We present a novel transformer-based ME-ABSA model for DSR, achieving favorable preliminary results on this dataset.
Users Hate Blondes: Detecting Sexism in User Comments on Online Romanian News
Authors: Andreea Moldovan, Karla Csürös, Ana-Maria Bucur, Loredana Bercuci
Date: 2022-07-01 00:00:00
Proceeding: NAACL (WOAH) 2022 7
Tasks: Articles

Romania ranks almost last in Europe when it comes to gender equality in political representation, with about 10${%$ fewer women in politics than the E.U. average. We proceed from the assumption that this underrepresentation is also influenced by the sexism and verbal abuse female politicians face in the public sphere, especially in online media. We collect a novel dataset with sexist comments in Romanian language from newspaper articles about Romanian female politicians and propose baseline models using classical machine learning models and fine-tuned pretrained transformer models for the classification of sexist language in the online medium.
Targeted Identity Group Prediction in Hate Speech Corpora
Authors: Pratik Sachdeva, Renata Barreto, Claudia von Vacano, Chris Kennedy
Date: 2022-07-01 00:00:00
Proceeding: NAACL (WOAH) 2022 7
Tasks: Hate Speech Detection, Prediction

The past decade has seen an abundance of work seeking to detect, characterize, and measure online hate speech. A related, but less studied problem, is the detection of identity groups targeted by that hate speech. Predictive accuracy on this task can supplement additional analyses beyond hate speech detection, motivating its study. Using the Measuring Hate Speech corpus, which provided annotations for targeted identity groups, we created neural network models to perform multi-label binary prediction of identity groups targeted by a comment. Specifically, we studied 8 broad identity groups and 12 identity sub-groups within race and gender identity. We found that these networks exhibited good predictive performance, achieving ROC AUCs of greater than 0.9 and PR AUCs of greater than 0.7 on several identity groups. We validated their performance on HateCheck and Gab Hate Corpora, finding that predictive performance generalized in most settings. We additionally examined the performance of the model on comments targeting multiple identity groups. Our results demonstrate the feasibility of simultaneously identifying targeted groups in social media comments.
Revisiting Queer Minorities in Lexicons
Authors: Krithika Ramesh, Sumeet Kumar, Ashiqur KhudaBukhsh
Date: 2022-07-01 00:00:00
Proceeding: NAACL (WOAH) 2022 7

Lexicons play an important role in content moderation often being the first line of defense. However, little or no literature exists in analyzing the representation of queer-related words in them. In this paper, we consider twelve well-known lexicons containing inappropriate words and analyze how gender and sexual minorities are represented in these lexicons. Our analyses reveal that several of these lexicons barely make any distinction between pejorative and non-pejorative queer-related words. We express concern that such unfettered usage of non-pejorative queer-related words may impact queer presence in mainstream discourse. Our analyses further reveal that the lexicons have poor overlap in queer-related words. We finally present a quantifiable measure of consistency and show that several of these lexicons are not consistent in how they include (or omit) queer-related words.
HATE-ITA: New Baselines for Hate Speech Detection in Italian
Authors: Debora Nozza, Federico Bianchi, Giuseppe Attanasio
Date: 2022-07-01 00:00:00
Proceeding: NAACL (WOAH) 2022 7
Tasks: Benchmarking, Hate Speech Detection, Language Modeling, Language Modelling

Online hate speech is a dangerous phenomenon that can (and should) be promptly counteracted properly. While Natural Language Processing supplies appropriate algorithms for trying to reach this objective, all research efforts are directed toward the English language. This strongly limits the classification power on non-English languages. In this paper, we test several learning frameworks for identifying hate speech in Italian text. We release HATE-ITA, a multi-language model trained on a large set of English data and available Italian datasets. HATE-ITA performs better than mono-lingual models and seems to adapt well also on language-specific slurs. We hope our findings will encourage the research in other mid-to-low resource communities and provide a valuable benchmarking tool for the Italian community.
Graph-based Features for Automatic Online Abuse Detection
Authors: Etienne Papegnies, Vincent Labatut, Richard Dufour, Georges Linares
Date: 2017-08-03 00:00:00
Tasks: Abuse Detection

While online communities have become increasingly important over the years, the moderation of user-generated content is still performed mostly manually. Automating this task is an important step in reducing the financial cost associated with moderation, but the majority of automated approaches strictly based on message content are highly vulnerable to intentional obfuscation. In this paper, we discuss methods for extracting conversational networks based on raw multi-participant chat logs, and we study the contribution of graph features to a classification system that aims to determine if a given message is abusive. The conversational graph-based system yields unexpectedly high performance , with results comparable to those previously obtained with a content-based approach.
TGRMPT: A Head-Shoulder Aided Multi-Person Tracker and a New Large-Scale Dataset for Tour-Guide Robot
Authors: Wen Wang, Shunda Hu, Shiqiang Zhu, Wei Song, Zheyuan Lin, Tianlei Jin, Zonghao Mu, Yuanhai Zhou
Date: 2022-07-08 00:00:00
Tasks: Multi-Object Tracking, Object Tracking

A service robot serving safely and politely needs to track the surrounding people robustly, especially for Tour-Guide Robot (TGR). However, existing multi-object tracking (MOT) or multi-person tracking (MPT) methods are not applicable to TGR for the following reasons: 1. lacking relevant large-scale datasets; 2. lacking applicable metrics to evaluate trackers. In this work, we target the visual perceptual tasks for TGR and present the TGRDB dataset, a novel large-scale multi-person tracking dataset containing roughly 5.6 hours of annotated videos and over 450 long-term trajectories. Besides, we propose a more applicable metric to evaluate trackers using our dataset. As part of our work, we present TGRMPT, a novel MPT system that incorporates information from head shoulder and whole body, and achieves state-of-the-art performance. We have released our codes and dataset in https://github.com/wenwenzju/TGRMPT.
Lessons from Deep Learning applied to Scholarly Information Extraction: What Works, What Doesn't, and Future Directions
Authors: Raquib Bin Yousuf, Subhodip Biswas, Kulendra Kumar Kaushal, James Dunham, Rebecca Gelles, Sathappan Muthiah, Nathan Self, Patrick Butler, Naren Ramakrishnan
Date: 2022-07-08 00:00:00
Tasks: Articles, Knowledge Graphs

Understanding key insights from full-text scholarly articles is essential as it enables us to determine interesting trends, give insight into the research and development, and build knowledge graphs. However, some of the interesting key insights are only available when considering full-text. Although researchers have made significant progress in information extraction from short documents, extraction of scientific entities from full-text scholarly literature remains a challenging problem. This work presents an automated End-to-end Research Entity Extractor called EneRex to extract technical facets such as dataset usage, objective task, method from full-text scholarly research articles. Additionally, we extracted three novel facets, e.g., links to source code, computing resources, programming language/libraries from full-text articles. We demonstrate how EneRex is able to extract key insights and trends from a large-scale dataset in the domain of computer science. We further test our pipeline on multiple datasets and found that the EneRex improves upon a state of the art model. We highlight how the existing datasets are limited in their capacity and how EneRex may fit into an existing knowledge graph. We also present a detailed discussion with pointers for future research. Our code and data are publicly available at https://github.com/DiscoveryAnalyticsCenter/EneRex.
CoCAtt: A Cognitive-Conditioned Driver Attention Dataset (Supplementary Material)
Authors: Yuan Shen, Niviru Wijayaratne, Pranav Sriram, Aamir Hasan, Peter Du, Katherine Driggs-Campbell
Date: 2022-07-08 00:00:00
Tasks: Driver Attention Monitoring, Prediction

The task of driver attention prediction has drawn considerable interest among researchers in robotics and the autonomous vehicle industry. Driver attention prediction can play an instrumental role in mitigating and preventing high-risk events, like collisions and casualties. However, existing driver attention prediction models neglect the distraction state and intention of the driver, which can significantly influence how they observe their surroundings. To address these issues, we present a new driver attention dataset, CoCAtt (Cognitive-Conditioned Attention). Unlike previous driver attention datasets, CoCAtt includes per-frame annotations that describe the distraction state and intention of the driver. In addition, the attention data in our dataset is captured in both manual and autopilot modes using eye-tracking devices of different resolutions. Our results demonstrate that incorporating the above two driver states into attention modeling can improve the performance of driver attention prediction. To the best of our knowledge, this work is the first to provide autopilot attention data. Furthermore, CoCAtt is currently the largest and the most diverse driver attention dataset in terms of autonomy levels, eye tracker resolutions, and driving scenarios. CoCAtt is available for download at https://cocatt-dataset.github.io.
A Multi-tasking Model of Speaker-Keyword Classification for Keeping Human in the Loop of Drone-assisted Inspection
Authors: Yu Li, Anisha Parsan, Bill Wang, Penghao Dong, Shanshan Yao, Ruwen Qin
Date: 2022-07-08 00:00:00

Audio commands are a preferred communication medium to keep inspectors in the loop of civil infrastructure inspection performed by a semi-autonomous drone. To understand job-specific commands from a group of heterogeneous and dynamic inspectors, a model must be developed cost-effectively for the group and easily adapted when the group changes. This paper is motivated to build a multi-tasking deep learning model that possesses a Share-Split-Collaborate architecture. This architecture allows the two classification tasks to share the feature extractor and then split subject-specific and keyword-specific features intertwined in the extracted features through feature projection and collaborative training. A base model for a group of five authorized subjects is trained and tested on the inspection keyword dataset collected by this study. The model achieved a 95.3% or higher mean accuracy in classifying the keywords of any authorized inspectors. Its mean accuracy in speaker classification is 99.2%. Due to the richer keyword representations that the model learns from the pooled training data, adapting the base model to a new inspector requires only a little training data from that inspector, like five utterances per keyword. Using the speaker classification scores for inspector verification can achieve a success rate of at least 93.9% in verifying authorized inspectors and 76.1% in detecting unauthorized ones. Further, the paper demonstrates the applicability of the proposed model to larger-size groups on a public dataset. This paper provides a solution to addressing challenges facing AI-assisted human-robot interaction, including worker heterogeneity, worker dynamics, and job heterogeneity.
MACFE: A Meta-learning and Causality Based Feature Engineering Framework
Authors: Ivan Reyes-Amezcua, Daniel Flores-Araiza, Gilberto Ochoa-Ruiz, Andres Mendez-Vazquez, Eduardo Rodriguez-Tello
Date: 2022-07-08 00:00:00
Tasks: Feature Engineering, feature selection, Meta-Learning

Feature engineering has become one of the most important steps to improve model prediction performance, and to produce quality datasets. However, this process requires non-trivial domain-knowledge which involves a time-consuming process. Thereby, automating such process has become an active area of research and of interest in industrial applications. In this paper, a novel method, called Meta-learning and Causality Based Feature Engineering (MACFE), is proposed; our method is based on the use of meta-learning, feature distribution encoding, and causality feature selection. In MACFE, meta-learning is used to find the best transformations, then the search is accelerated by pre-selecting "original" features given their causal relevance. Experimental evaluations on popular classification datasets show that MACFE can improve the prediction performance across eight classifiers, outperforms the current state-of-the-art methods in average by at least 6.54%, and obtains an improvement of 2.71% over the best previous works.
Dreamento: an open-source dream engineering toolbox for sleep EEG wearables
Authors: Mahdad Jafarzadeh Esfahani, Amir Hossein Daraie, Paul Zerr, Frederik D. Weber, Martin Dresler
Date: 2022-07-08 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Electromyography (EMG), text-to-speech, Text to Speech

We introduce Dreamento (Dream engineering toolbox), an open-source Python package for dream engineering using sleep electroencephalography (EEG) wearables. Dreamento main functions are (1) real-time recording, monitoring, analysis, and sensory stimulation, and (2) offline post-processing of the resulting data, both in a graphical user interface (GUI). In real-time, Dreamento is capable of (1) data recording, visualization, and navigation, (2) power-spectrum analysis, (3) automatic sleep scoring, (4) sensory stimulation (visual, auditory, tactile), (5) establishing text-to-speech communication, and (6) managing annotations of automatic and manual events. The offline functions aid in post-processing the acquired data with features to reformat the wearable data and integrate it with non-wearable recorded modalities such as electromyography (EMG). While Dreamento was primarily developed for (lucid) dreaming studies, its applications can be extended to other areas of sleep research such as closed-loop auditory stimulation and targeted memory reactivation.
CoSIm: Commonsense Reasoning for Counterfactual Scene Imagination
Authors: Hyounghun Kim, Abhay Zala, Mohit Bansal
Date: 2022-07-08 00:00:00
Proceeding: NAACL 2022 7
Tasks: counterfactual

As humans, we can modify our assumptions about a scene by imagining alternative objects or concepts in our minds. For example, we can easily anticipate the implications of the sun being overcast by rain clouds (e.g., the street will get wet) and accordingly prepare for that. In this paper, we introduce a new task/dataset called Commonsense Reasoning for Counterfactual Scene Imagination (CoSIm) which is designed to evaluate the ability of AI systems to reason about scene change imagination. In this task/dataset, models are given an image and an initial question-response pair about the image. Next, a counterfactual imagined scene change (in textual form) is applied, and the model has to predict the new response to the initial question based on this scene change. We collect 3.5K high-quality and challenging data instances, with each instance consisting of an image, a commonsense question with a response, a description of a counterfactual change, a new response to the question, and three distractor responses. Our dataset contains various complex scene change types (such as object addition/removal/state change, event description, environment change, etc.) that require models to imagine many different scenarios and reason about the changed scenes. We present a baseline model based on a vision-language Transformer (i.e., LXMERT) and ablation studies. Through human evaluation, we demonstrate a large human-model performance gap, suggesting room for promising future work on this challenging counterfactual, scene imagination task. Our code and dataset are publicly available at: https://github.com/hyounghk/CoSIm
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems
Authors: Elisa Marcelli, Tommaso Barbariol, Gian Antonio Susto
Date: 2022-07-08 00:00:00
Tasks: Active Learning, Anomaly Detection

The detection of anomalous behaviours is an emerging need in many applications, particularly in contexts where security and reliability are critical aspects. While the definition of anomaly strictly depends on the domain framework, it is often impractical or too time consuming to obtain a fully labelled dataset. The use of unsupervised models to overcome the lack of labels often fails to catch domain specific anomalies as they rely on general definitions of outlier. This paper suggests a new active learning based approach, ALIF, to solve this problem by reducing the number of required labels and tuning the detector towards the definition of anomaly provided by the user. The proposed approach is particularly appealing in the presence of a Decision Support System (DSS), a case that is increasingly popular in real-world scenarios. While it is common that DSS embedded with anomaly detection capabilities rely on unsupervised models, they don't have a way to improve their performance: ALIF is able to enhance the capabilities of DSS by exploiting the user feedback during common operations. ALIF is a lightweight modification of the popular Isolation Forest that proved superior performances with respect to other state-of-art algorithms in a multitude of real anomaly detection datasets.
Memory-free Online Change-point Detection: A Novel Neural Network Approach
Authors: Zahra Atashgahi, Decebal Constantin Mocanu, Raymond Veldhuis, Mykola Pechenizkiy
Date: 2022-07-08 00:00:00
Tasks: Change Point Detection, Time Series, Time Series Analysis

Change-point detection (CPD), which detects abrupt changes in the data distribution, is recognized as one of the most significant tasks in time series analysis. Despite the extensive literature on offline CPD, unsupervised online CPD still suffers from major challenges, including scalability, hyperparameter tuning, and learning constraints. To mitigate some of these challenges, in this paper, we propose a novel deep learning approach for unsupervised online CPD from multi-dimensional time series, named Adaptive LSTM-Autoencoder Change-Point Detection (ALACPD). ALACPD exploits an LSTM-autoencoder-based neural network to perform unsupervised online CPD. It continuously adapts to the incoming samples without keeping the previously received input, thus being memory-free. We perform an extensive evaluation on several real-world time series CPD benchmarks. We show that ALACPD, on average, ranks first among state-of-the-art CPD algorithms in terms of quality of the time series segmentation, and it is on par with the best performer in terms of the accuracy of the estimated change-points. The implementation of ALACPD is available online on Github\footnote{\url{https://github.com/zahraatashgahi/ALACPD}}.