pwc-archive/papers-with-abstracts huggingface dataset:
A Hierarchically-Labeled Portuguese Hate Speech Dataset
Authors: Paula Fortuna, Jo{\~a}o Rocha da Silva, Juan Soler-Company, Leo Wanner, S{\'e}rgio Nunes
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Word Embeddings

Over the past years, the amount of online offensive speech has been growing steadily. To successfully cope with it, machine learning are applied. However, ML-based techniques require sufficiently large annotated datasets. In the last years, different datasets were published, mainly for English. In this paper, we present a new dataset for Portuguese, which has not been in focus so far. The dataset is composed of 5,668 tweets. For its annotation, we defined two different schemes used by annotators with different levels of expertise. Firstly, non-experts annotated the tweets with binary labels ({`}hate{'} vs. {`}no-hate{'}). Secondly, expert annotators classified the tweets following a fine-grained hierarchical multiple label scheme with 81 hate speech categories in total. The inter-annotator agreement varied from category to category, which reflects the insight that some types of hate speech are more subtle than others and that their detection depends on personal perception. This hierarchical annotation scheme is the main contribution of the presented work, as it facilitates the identification of different types of hate speech and their intersections. To demonstrate the usefulness of our dataset, we carried a baseline classification experiment with pre-trained word embeddings and LSTM on the binary classified data, with a state-of-the-art outcome.
A System to Monitor Cyberbullying based on Message Classification and Social Network Analysis
Authors: Stefano Menini, Giovanni Moretti, Michele Corazza, Elena Cabrio, Sara Tonelli, Serena Villata
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Abusive Language, General Classification

Social media platforms like Twitter and Instagram face a surge in cyberbullying phenomena against young users and need to develop scalable computational methods to limit the negative consequences of this kind of abuse. Despite the number of approaches recently proposed in the Natural Language Processing (NLP) research area for detecting different forms of abusive language, the issue of identifying cyberbullying phenomena at scale is still an unsolved problem. This is because of the need to couple abusive language detection on textual message with network analysis, so that repeated attacks against the same person can be identified. In this paper, we present a system to monitor cyberbullying phenomena by combining message classification and social network analysis. We evaluate the classification module on a data set built on Instagram messages, and we describe the cyberbullying monitoring user interface.
L-HSAB: A Levantine Twitter Dataset for Hate Speech and Abusive Language
Authors: Hala Mulki, Hatem Haddad, Chedi Bechikh Ali, Halima Alshabani
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Abusive Language, Hate Speech Detection

Hate speech and abusive language have become a common phenomenon on Arabic social media. Automatic hate speech and abusive detection systems can facilitate the prohibition of toxic textual contents. The complexity, informality and ambiguity of the Arabic dialects hindered the provision of the needed resources for Arabic abusive/hate speech detection research. In this paper, we introduce the first publicly-available Levantine Hate Speech and Abusive (L-HSAB) Twitter dataset with the objective to be a benchmark dataset for automatic detection of online Levantine toxic contents. We, further, provide a detailed review of the data collection steps and how we design the annotation guidelines such that a reliable dataset annotation is guaranteed. This has been later emphasized through the comprehensive evaluation of the annotations as the annotation agreement metrics of Cohen{'}s Kappa (k) and Krippendorff{'}s alpha (Î±) indicated the consistency of the annotations.
At the Lower End of Language---Exploring the Vulgar and Obscene Side of German
Authors: Elisabeth Eder, Ulrike Krieg-Holz, Udo Hahn
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

In this paper, we describe a workflow for the data-driven acquisition and semantic scaling of a lexicon that covers lexical items from the lower end of the German language register{---}terms typically considered as rough, vulgar or obscene. Since the fine semantic representation of grades of obscenity can only inadequately be captured at the categorical level (e.g., obscene vs. non-obscene, or rough vs. vulgar), our main contribution lies in applying best-worst scaling, a rating methodology that has already been shown to be useful for emotional language, to capture the relative strength of obscenity of lexical items. We describe the empirical foundations for bootstrapping such a low-end lexicon for German by starting from manually supplied lexicographic categorizations of a small seed set of rough and vulgar lexical items and automatically enlarging this set by means of distributional semantics. We then determine the degrees of obscenity for the full set of all acquired lexical items by letting crowdworkers comparatively assess their pejorative grade using best-worst scaling. This semi-automatically enriched lexicon already comprises 3,300 lexical items and incorporates 33,000 vulgarity ratings. Using it as a seed lexicon for fully automatic lexical acquisition, we were able to raise its coverage up to slightly more than 11,000 entries.
Preemptive Toxic Language Detection in Wikipedia Comments Using Thread-Level Context
Authors: Mladen Karan, Jan {\v{S}}najder
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

We address the task of automatically detecting toxic content in user generated texts. We fo cus on exploring the potential for preemptive moderation, i.e., predicting whether a particular conversation thread will, in the future, incite a toxic comment. Moreover, we perform preliminary investigation of whether a model that jointly considers all comments in a conversation thread outperforms a model that considers only individual comments. Using an existing dataset of conversations among Wikipedia contributors as a starting point, we compile a new large-scale dataset for this task consisting of labeled comments and comments from their conversation threads.
A Platform Agnostic Dual-Strand Hate Speech Detector
Authors: Johannes Skjeggestad Meyer, Bj{\"o}rn Gamb{\"a}ck
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Word Embeddings

Hate speech detectors must be applicable across a multitude of services and platforms, and there is hence a need for detection approaches that do not depend on any information specific to a given platform. For instance, the information stored about the text{'}s author may differ between services, and so using such data would reduce a system{'}s general applicability. The paper thus focuses on using exclusively text-based input in the detection, in an optimised architecture combining Convolutional Neural Networks and Long Short-Term Memory-networks. The hate speech detector merges two strands with character n-grams and word embeddings to produce the final classification, and is shown to outperform comparable previous approaches.
Detecting Aggression and Toxicity using a Multi Dimension Capsule Network
Authors: Saurabh Srivastava, Prerna Khurana
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Classification, General Classification, Toxic Comment Classification

In the era of social media, hate speech, trolling and verbal abuse have become a common issue. We present an approach to automatically classify such statements, using a new deep learning architecture. Our model comprises of a Multi Dimension Capsule Network that generates the representation of sentences which we use for classification. We further provide an analysis of our model{'}s interpretation of such statements. We compare the results of our model with state-of-art classification algorithms and demonstrate our model{'}s ability. It also has the capability to handle comments that are written in both Hindi and English, which are provided in the TRAC dataset. We also compare results on Kaggle{'}s Toxic comment classification dataset.
An Impossible Dialogue! Nominal Utterances and Populist Rhetoric in an Italian Twitter Corpus of Hate Speech against Immigrants
Authors: Com, Gloria ini, Viviana Patti
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Articles

The paper proposes an investigation on the role of populist themes and rhetoric in an Italian Twitter corpus of hate speech against immigrants. The corpus had been annotated with four new layers of analysis: Nominal Utterances, that can be seen as consistent with populist rhetoric; In-out-group rhetoric, a very common populist strategy to polarize public opinion; Slogan-like nominal utterances, that may convey the call for severe illiberal policies against immigrants; News, to recognize the role of newspapers (headlines or reference to articles) in the Twitter political discourse on immigration featured by hate speech.
``Condescending, Rude, Assholes'': Framing gender and hostility on Stack Overflow
Authors: Sian Brooke
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Clustering

The disciplines of Gender Studies and Data Science are incompatible. This is conventional wisdom, supported by how many computational studies simplify gender into an immutable binary categorization that appears crude to the critical social researcher. I argue that the characterization of gender norms is context specific and may prove valuable in constructing useful models. I show how gender can be framed in computational studies as a stylized repetition of acts mediated by a social structure, and not a possessed biological category. By conducting a review of existing work, I show how gender should be explored in multiplicity in computational research through clustering techniques, and layout how this is being achieved in a study in progress on gender hostility on Stack Overflow.
Online aggression from a sociological perspective: An integrative view on determinants and possible countermeasures
Authors: Sebastian Weingartner, Lea Stahel
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Survey

The present paper introduces a theoretical model for explaining aggressive online comments from a sociological perspective. It is innovative as it combines individual, situational, and social-structural determinants of online aggression and tries to theoretically derive their interplay. Moreover, the paper suggests an empirical strategy for testing the model. The main contribution will be to match online commenting data with survey data containing rich background data of non- /aggressive online commentators.
Towards a Resource Grammar for Runyankore and Rukiga
Authors: David Bamutura, Peter Ljungl{\"o}f
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation

Currently, there is a lack of computational grammar resources for many under-resourced languages which limits the ability to develop Natural Language Processing (NLP) tools and applications such as Multilingual Document Authoring, Computer-Assisted Language Learning (CALL) and Low-Coverage Machine Translation (MT) for these languages. In this paper, we present our attempt to formalise the grammar of two such languages: Runyankore and Rukiga. For this formalisation we use the Grammatical Framework (GF) and its Resource Grammar Library (GF-RGL).
Speech Recognition for Tigrinya language Using Deep Neural Network Approach
Authors: Hafte Abera, Sebsibe H/mariam
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: speech-recognition, Speech Recognition

This work presents a speech recognition model for Tigrinya language .The Deep Neural Network is used to make the recognition model. The Long Short-Term Memory Network (LSTM), which is a special kind of Recurrent Neural Network composed of Long Short-Term Memory blocks, is the primary layer of our neural network model. The 40-dimensional features are MFCC-LDA-MLLT-fMLLR with CMN were used. The acoustic models are trained on features that are obtained by projecting down to 40 dimensions using linear discriminant analysis (LDA). Moreover, speaker adaptive training (SAT) is done using a single feature-space maximum likelihood linear regression (FMLLR) transform estimated per speaker. We train and compare LSTM and DNN models at various numbers of parameters and configurations. We show that LSTM models converge quickly and give state of the art speech recognition performance for relatively small sized models. Finally, the accuracy of the model is evaluated based on the recognition rate.
Knowledge-Based Word Sense Disambiguation with Distributional Semantic Expansion
Authors: Hossein Rouhizadeh, Mehrnoush Shamsfard, Masoud Rouhizadeh
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Word Sense Disambiguation

In this paper, we presented a WSD system that uses LDA topics for semantic expansion of document words. Our system also uses sense frequency information from SemCor to give higher priority to the senses which are more probable to happen.
AspeRa: Aspect-Based Rating Prediction Based on User Reviews
Authors: Elena Tutubalina, Valentin Malykh, Sergey Nikolenko, Anton Alekseev, Ilya Shenbin
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Aspect Extraction, Prediction

We propose a novel Aspect-based Rating Prediction model (AspeRa) that estimates user rating based on review texts for the items. It is based on aspect extraction with neural networks and combines the advantages of deep learning and topic modeling. It is mainly designed for recommendations, but an important secondary goal of AspeRa is to discover coherent aspects of reviews that can be used to explain predictions or for user profiling. We conduct a comprehensive empirical study of AspeRa, showing that it outperforms state-of-the-art models in terms of recommendation quality and produces interpretable aspects. This paper is an abridged version of our work (Nikolenko et al., 2019)
A Framework for Relation Extraction Across Multiple Datasets in Multiple Domains
Authors: Geeticka Chauhan, Matthew McDermott, Peter Szolovits
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Relation, Relation Extraction

In this work, we aim to build a unifying framework for relation extraction (RE), applying this on 3 highly used datasets with the ability to be extendable to new datasets. At the moment, the domain suffers from lack of reproducibility as well as a lack of consensus on generalizable techniques. Our framework will be open-sourced and will aid in performing systematic exploration on the effect of different modeling techniques, pre-processing, training methodologies and evaluation metrics on the 3 datasets to help establish a consensus.
Learning and Understanding Different Categories of Sexism Using Convolutional Neural Network's Filters
Authors: Sima Sharifirad, Alon Jacovi
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Clustering

Sexism is very common in social media and makes the boundaries of free speech tighter for female users. Automatically flagging and removing sexist content requires niche identification and description of the categories. In this study, inspired by social science work, we propose three categories of sexism toward women as follows: {``}Indirect sexism{''}, {``}Sexual sexism{''} and {``}Physical sexism{''}. We build classifiers such as Convolutional Neural Network (CNN) to automatically detect different types of sexism and address problems of annotation. Even though inherent non-interpretability of CNN is a challenge for users who detect sexism, as the reason classifying a given speech instance with regard to sexism is difficult to glance from a CNN. However, recent research developed interpretable CNN filters for text data. In a CNN, filters followed by different activation patterns along with global max-pooling can help us tease apart the most important ngrams from the rest. In this paper, we interpret a CNN model trained to classify sexism in order to understand different categories of sexism by detecting semantic categories of ngrams and clustering them. Then, these ngrams in each category are used to improve the performance of the classification task. It is a preliminary work using machine learning and natural language techniques to learn the concept of sexism and distinguishes itself by looking at more precise categories of sexism in social media along with an in-depth investigation of CNN{'}s filters.
Modeling Five Sentence Quality Representations by Finding Latent Spaces Produced with Deep Long Short-Memory Models
Authors: Pablo Rivas
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Sentence

We present a study in which we train neural models that approximate rules that assess the quality of English sentences. We modeled five rules using deep LSTMs trained over a dataset of sentences whose quality is evaluated under such rules. Preliminary results suggest the neural architecture can model such rules to high accuracy.
English-Ethiopian Languages Statistical Machine Translation
Authors: Solomon Teferra Abate, Michael Melese, Martha Yifiru Tachbelie, Million Meshesha, Solomon Atinafu, Wondwossen Mulugeta, Yaregal Assabie, Hafte Abera, Biniyam Ephrem, Tewodros Gebreselassie, Wondimagegnhue Tsegaye Tufa, Amanuel Lemma, Tsegaye Andargie, Seifedin Shifaw
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation

In this paper, we describe an attempt towards the development of parallel corpora for English and Ethiopian Languages, such as Amharic, Tigrigna, Afan-Oromo, Wolaytta and Ge{'}ez. The corpora are used for conducting bi-directional SMT experiments. The BLEU scores of the bi-directional SMT systems show a promising result. The morphological richness of the Ethiopian languages has a great impact on the performance of SMT especially when the targets are Ethiopian languages.
An automatic discourse relation alignment experiment on TED-MDB
Authors: Sibel Ozer, Deniz Zeyrek
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Relation

This paper describes an automatic discourse relation alignment experiment as an empirical justification of the planned annotation projection approach to enlarge the 3600-word multilingual corpus of TED Multilingual Discourse Bank (TED-MDB). The experiment is carried out on a single language pair (English-Turkish) included in TED-MDB. The paper first describes the creation of a large corpus of English-Turkish bi-sentences, then it presents a sense-based experiment that automatically aligns the relations in the English sentences of TED-MDB with the Turkish sentences. The results are very close to the results obtained from an earlier semi-automatic post-annotation alignment experiment validated by human annotators and are encouraging for future annotation projection tasks.
The Design and Construction of the Corpus of China English
Authors: lixin xia, Yun Xia
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Cultural Vocal Bursts Intensity Prediction, Philosophy

The paper describes the development a corpus of an English variety, i.e. China English, in or-der to provide a linguistic resource for researchers in the field of China English. The Corpus of China English (CCE) was built with due consideration given to its representativeness and authenticity. It was composed of more than 13,962,102 tokens in 15,333 texts evenly divided between the following four genres: newspapers, magazines, fiction and academic writings. The texts cover a wide range of domains, such as news, financial, politics, environment, social, culture, technology, sports, education, philosophy, literary, etc. It is a helpful resource for research on China English, computational linguistics, natural language processing, corpus linguistics and English language education.
Learning Trilingual Dictionaries for Urdu -- Roman Urdu -- English
Authors: Moiz Rauf, Sebastian Pad{\'o}
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Machine Translation, Translation, Word Alignment

In this paper, we present an effort to generate a joint Urdu, Roman Urdu and English trilingual lexicon using automated methods. We make a case for using statistical machine translation approaches and parallel corpora for dictionary creation. To this purpose, we use word alignment tools on the corpus and evaluate translations using human evaluators. Despite different writing script and considerable noise in the corpus our results show promise with over 85{\%} accuracy of Roman Urdu{--}Urdu and 45{\%} English{--}Urdu pairs.
Joint Inference on Bilingual Parse Trees for PP-attachment Disambiguation
Authors: Geetanjali Rakshit
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8
Tasks: Sentence

Prepositional Phrase (PP) attachment is a classical problem in NLP for languages like English, which suffer from structural ambiguity. In this work, we solve this problem with the help of another language free from such ambiguities, using the parse tree of the parallel sentence in the other language, and word alignments. We formulate an optimization framework that encourages agreement between the parse trees for two languages, and solve it using a novel Dual Decomposition (DD) based algorithm. Experiments on the English-Hindi language pair show promising improvements over the baseline.
Using Attention-based Bidirectional LSTM to Identify Different Categories of Offensive Language Directed Toward Female Celebrities
Authors: Sima Sharifirad, Stan Matwin
Date: 2019-08-01 00:00:00
Proceeding: WS 2019 8

Social media posts reflect the emotions, intentions and mental state of the users. Twitter users who harass famous female figures may do so with different intentions and intensities. Recent studies have published datasets focusing on different types of online harassment, vulgar language, and emotional intensities. We trained, validate and test our proposed model, attention-based bidirectional neural network, on the three datasets:{''}online harassment{''}, {``}vulgar language{''} and {``}valance{''} and achieved state of the art performance in two of the datasets. We report F1 score for each dataset separately along with the final precision, recall and macro-averaged F1 score. In addition, we identify ten female figures from different professions and racial backgrounds who have experienced harassment on Twitter. We tested the trained models on ten collected corpuses each related to one famous female figure to predict the type of harassing language, the type of vulgar language and the degree of intensity of language occurring on their social platforms. Interestingly, the achieved results show different patterns of linguistic use targeting different racial background and occupations. The contribution of this study is two-fold. From the technical perspective, our proposed methodology is shown to be effective with a good margin in comparison to the previous state-of-the-art results on one of the two available datasets. From the social perspective, we introduce a methodology which can unlock facts about the nature of offensive language targeting women on online social platforms. The collected dataset will be shared publicly for further investigation.