pwc-archive/papers-with-abstracts huggingface dataset:
KDEHatEval at SemEval-2019 Task 5: A Neural Network Model for Detecting Hate Speech in Twitter
Authors: Umme Aymun Siddiqua, Abu Nowshed Chy, Masaki Aono
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Sentence, Sentence Embedding, Sentence-Embedding

In the age of emerging volume of microblog platforms, especially twitter, hate speech propagation is now of great concern. However, due to the brevity of tweets and informal user generated contents, detecting and analyzing hate speech on twitter is a formidable task. In this paper, we present our approach for detecting hate speech in tweets defined in the SemEval-2019 Task 5. Our team KDEHatEval employs different neural network models including multi-kernel convolution (MKC), nested LSTMs (NLSTMs), and multi-layer perceptron (MLP) in a unified architecture. Moreover, we utilize the state-of-the-art pre-trained sentence embedding models including DeepMoji, InferSent, and BERT for effective tweet representation. We analyze the performance of our method and demonstrate the contribution of each component of our architecture.
ABARUAH at SemEval-2019 Task 5 : Bi-directional LSTM for Hate Speech Detection
Authors: Arup Baruah, Ferdous Barbhuiya, Kuntal Dey
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Hate Speech Detection

In this paper, we present the results obtained using bi-directional long short-term memory (BiLSTM) with and without attention and Logistic Regression (LR) models for SemEval-2019 Task 5 titled {''}HatEval: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter{''}. This paper presents the results obtained for Subtask A for English language. The results of the BiLSTM and LR models are compared for two different types of preprocessing. One with no stemming performed and no stopwords removed. The other with stemming performed and stopwords removed. The BiLSTM model without attention performed the best for the first test, while the LR model with character n-grams performed the best for the second test. The BiLSTM model obtained an F1 score of 0.51 on the test set and obtained an official ranking of 8/71.
CIC at SemEval-2019 Task 5: Simple Yet Very Efficient Approach to Hate Speech Detection, Aggressive Behavior Detection, and Target Classification in Twitter
Authors: Iqra Ameer, Muhammad Hammad Fahim Siddiqui, Grigori Sidorov, Alex Gelbukh, er
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Hate Speech Detection

In recent years, the use of social media has in-creased incredibly. Social media permits Inter-net users a friendly platform to express their views and opinions. Along with these nice and distinct communication chances, it also allows bad things like usage of hate speech. Online automatic hate speech detection in various aspects is a significant scientific problem. This paper presents the Instituto Polit{\'e}cnico Nacional (Mexico) approach for the Semeval 2019 Task-5 [Hateval 2019] (Basile et al., 2019) competition for Multilingual Detection of Hate Speech on Twitter. The goal of this paper is to detect (A) Hate speech against immigrants and women, (B) Aggressive behavior and target classification, both for English and Spanish. In the proposed approach, we used a bag of words model with preprocessing (stem-ming and stop words removal). We submitted two different systems with names: (i) CIC-1 and (ii) CIC-2 for Hateval 2019 shared task. We used TF values in the first system and TF-IDF for the second system. The first system, CIC-1 got 2nd rank in subtask B for both English and Spanish languages with EMR score of 0.568 for English and 0.675 for Spanish. The second system, CIC-2 was ranked 4th in sub-task A and 1st in subtask B for Spanish language with a macro-F1 score of 0.727 and EMR score of 0.705 respectively.
CiTIUS-COLE at SemEval-2019 Task 5: Combining Linguistic Features to Identify Hate Speech Against Immigrants and Women on Multilingual Tweets
Authors: Sattam Almatarneh, Pablo Gamallo, Francisco J. Ribadas Pena
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

This article describes the strategy submitted by the CiTIUS-COLE team to SemEval 2019 Task 5, a task which consists of binary classi- fication where the system predicting whether a tweet in English or in Spanish is hateful against women or immigrants or not. The proposed strategy relies on combining linguis- tic features to improve the classifier{'}s perfor- mance. More precisely, the method combines textual and lexical features, embedding words with the bag of words in Term Frequency- Inverse Document Frequency (TF-IDF) repre- sentation. The system performance reaches about 81{\%} F1 when it is applied to the training dataset, but its F1 drops to 36{\%} on the official test dataset for the English and 64{\%} for the Spanish language concerning the hate speech class
Grunn2019 at SemEval-2019 Task 5: Shared Task on Multilingual Detection of Hate
Authors: Mike Zhang, Roy David, Leon Graumans, Gerben Timmerman
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

Hate speech occurs more often than ever and polarizes society. To help counter this polarization, SemEval 2019 organizes a shared task called the Multilingual Detection of Hate. The first task (A) is to decide whether a given tweet contains hate against immigrants or women, in a multilingual perspective, for English and Spanish. In the second task (B), the system is also asked to classify the following sub-tasks: hateful tweets as aggressive or not aggressive, and to identify the target harassed as individual or generic. We evaluate multiple models, and finally combine them in an ensemble setting. This ensemble setting is built of five and three submodels for the English and Spanish task respectively. In the current setup it shows that using a bigger ensemble for English tweets performs mediocre, while a slightly smaller ensemble does work well for detecting hate speech in Spanish tweets. Our results on the test set for English show 0.378 macro F1 on task A and 0.553 macro F1 on task B. For Spanish the results are significantly higher, 0.701 macro F1 on task A and 0.734 macro F1 for task B.
GSI-UPM at SemEval-2019 Task 5: Semantic Similarity and Word Embeddings for Multilingual Detection of Hate Speech Against Immigrants and Women on Twitter
Authors: Diego Benito, Oscar Araque, Carlos A. Iglesias
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: POS, Position, Semantic Similarity, Semantic Textual Similarity, Word Embeddings

This paper describes the GSI-UPM system for SemEval-2019 Task 5, which tackles multilingual detection of hate speech on Twitter. The main contribution of the paper is the use of a method based on word embeddings and semantic similarity combined with traditional paradigms, such as n-grams, TF-IDF and POS. This combination of several features is fine-tuned through ablation tests, demonstrating the usefulness of different features. While our approach outperforms baseline classifiers on different sub-tasks, the best of our submitted runs reached the 5th position on the Spanish sub-task A.
HATEMINER at SemEval-2019 Task 5: Hate speech detection against Immigrants and Women in Twitter using a Multinomial Naive Bayes Classifier
Authors: Nikhil Chakravartula
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: General Classification, Hate Speech Detection, Sentence

This paper describes our participation in the SemEval 2019 Task 5 - Multilingual Detection of Hate. This task aims to identify hate speech against two specific targets, immigrants and women. We compare and contrast the performance of different word and sentence level embeddings on the state-of-the-art classification algorithms. Our final submission is a Multinomial binarized Naive Bayes model for both the subtasks in the English version.
HATERecognizer at SemEval-2019 Task 5: Using Features and Neural Networks to Face Hate Recognition
Authors: Victor Nina-Alcocer
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Data Augmentation

This paper presents a detailed description of our participation in task 5 on SemEval-2019. This task consists of classifying English and Spanish tweets that contain hate towards women or immigrants. We carried out several experiments; for a finer-grained study of the task, we analyzed different features and designing architectures of neural networks. Additionally, to face the lack of hate content in tweets, we include data augmentation as a technique to in- crease hate content in our datasets.
GL at SemEval-2019 Task 5: Identifying hateful tweets with a deep learning approach.
Authors: Gretel Liz De la Pe{\~n}a
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Sentence, Word Embeddings

This paper describes the system we developed for SemEval 2019 on Multilingual detection of hate speech against immigrants and women in Twitter (HatEval - Task 5). We use an approach based on an Attention-based Long Short-Term Memory Recurrent Neural Network. In particular, we build a Bidirectional LSTM to extract information from the word embeddings over the sentence, then apply attention over the hidden states to estimate the importance of each word and finally feed this context vector to another LSTM model to get a representation. Then, the output obtained with this model is used to get the prediction of each of the sub-tasks.
INF-HatEval at SemEval-2019 Task 5: Convolutional Neural Networks for Hate Speech Detection Against Women and Immigrants on Twitter
Authors: Alison Ribeiro, N{\'a}dia Silva
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Hate Speech Detection, Word Embeddings

In this paper, we describe our approach to detect hate speech against women and immigrants on Twitter in a multilingual context, English and Spanish. This challenge was proposed by the SemEval-2019 Task 5, where participants should develop models for hate speech detection, a two-class classification where systems have to predict whether a tweet in English or in Spanish with a given target (women or immigrants) is hateful or not hateful (Task A), and whether the hate speech is directed at a specific person or a group of individuals (Task B). For this, we implemented a Convolutional Neural Networks (CNN) using pre-trained word embeddings (GloVe and FastText) with 300 dimensions. Our proposed model obtained in Task A 0.488 and 0.696 F1-score for English and Spanish, respectively. For Task B, the CNN obtained 0.297 and 0.430 EMR for English and Spanish, respectively.
JCTDHS at SemEval-2019 Task 5: Detection of Hate Speech in Tweets using Deep Learning Methods, Character N-gram Features, and Preprocessing Methods
Authors: Yaakov HaCohen-Kerner, Elyashiv Shayovitz, Shalom Rochman, Eli Cahn, Gal Didi, Ziv Ben-David
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Binary Classification, General Classification, Position

In this paper, we describe our submissions to SemEval-2019 contest. We tackled subtask A - {``}a binary classification where systems have to predict whether a tweet with a given target (women or immigrants) is hateful or not hateful{''}, a part of task 5 {``}Multilingual detection of hate speech against immigrants and women in Twitter (hatEval){''}. Our system JCTDHS (Jerusalem College of Technology Detects Hate Speech) was developed for tweets written in English. We applied various supervised ML methods, various combinations of n-gram features using the TF-IDF scheme and. In addition, we applied various combinations of eight basic preprocessing methods. Our best submission was a special bidirectional RNN, which was ranked at the 11th position out of 68 submissions.
Know-Center at SemEval-2019 Task 5: Multilingual Hate Speech Detection on Twitter using CNNs
Authors: Kevin Winter, Roman Kern
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Hate Speech Detection, Word Embeddings

This paper presents the Know-Center system submitted for task 5 of the SemEval-2019 workshop. Given a Twitter message in either English or Spanish, the task is to first detect whether it contains hateful speech and second, to determine the target and level of aggression used. For this purpose our system utilizes word embeddings and a neural network architecture, consisting of both dilated and traditional convolution layers. We achieved average F1-scores of 0.57 and 0.74 for English and Spanish respectively.
LT3 at SemEval-2019 Task 5: Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter (hatEval)
Authors: Nina Bauwelinck, Gilles Jacobs, V{\'e}ronique Hoste, Els Lefever
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Classification, General Classification

This paper describes our contribution to the SemEval-2019 Task 5 on the detection of hate speech against immigrants and women in Twitter (hatEval). We considered a supervised classification-based approach to detect hate speech in English tweets, which combines a variety of standard lexical and syntactic features with specific features for capturing offensive language. Our experimental results show good classification performance on the training data, but a considerable drop in recall on the held-out test set.
ltl.uni-due at SemEval-2019 Task 5: Simple but Effective Lexico-Semantic Features for Detecting Hate Speech in Twitter
Authors: Huangpan Zhang, Michael Wojatzki, Tobias Horsmann, Torsten Zesch
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

In this paper, we present our contribution to SemEval 2019 Task 5 Multilingual Detection of Hate, specifically in the Subtask A (English and Spanish). We compare different configurations of shallow and deep learning approaches on the English data and use the system that performs best in both sub-tasks. The resulting SVM-based system with lexicosemantic features (n-grams and embeddings) is ranked 23rd out of 69 on the English data and beats the baseline system. On the Spanish data our system is ranked 25th out of 39.
MineriaUNAM at SemEval-2019 Task 5: Detecting Hate Speech in Twitter using Multiple Features in a Combinatorial Framework
Authors: Luis Enrique Argota Vega, Jorge Carlos Reyes-Maga{\~n}a, Helena G{\'o}mez-Adorno, Gemma Bel-Enguix
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: General Classification, POS

This paper presents our approach to the Task 5 of Semeval-2019, which aims at detecting hate speech against immigrants and women in Twitter. The task consists of two sub-tasks, in Spanish and English: (A) detection of hate speech and (B) classification of hateful tweets as aggressive or not, and identification of the target harassed as individual or group. We used linguistically motivated features and several types of n-grams (words, characters, functional words, punctuation symbols, POS, among others). For task A, we trained a Support Vector Machine using a combinatorial framework, whereas for task B we followed a multi-labeled approach using the Random Forest classifier. Our approach achieved the highest F1-score in sub-task A for the Spanish language.
MITRE at SemEval-2019 Task 5: Transfer Learning for Multilingual Hate Speech Detection
Authors: Abigail Gertner, John Henderson, Elizabeth Merkhofer, Amy Marsh, Ben Wellner, Guido Zarrella
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: All, Hate Speech Detection, regression, Transfer Learning

This paper describes MITRE{'}s participation in SemEval-2019 Task 5, HatEval: Multilingual detection of hate speech against immigrants and women in Twitter. The techniques explored range from simple bag-of-ngrams classifiers to neural architectures with varied attention mechanisms. We describe several styles of transfer learning from auxiliary tasks, including a novel method for adapting pre-trained BERT models to Twitter data. Logistic regression ties the systems together into an ensemble submitted for evaluation. The resulting system was used to produce predictions for all four HatEval subtasks, achieving the best mean rank of all teams that participated in all four conditions.
Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter at SemEval-2019 Task 5: Frequency Analysis Interpolation for Hate in Speech Detection
Authors: {\`O}scar Garibo i Orts
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

This document describes a text change of representation approach to the task of Multilingual Detection of Hate Speech Against Immigrants and Women in Twitter, as part of SemEval-2019 1 . The task is divided in two sub-tasks. Sub-task A consists in classifying tweets as being hateful or not hateful, whereas sub-task B requires fine tuning the classification by classifying the hateful tweets as being directed to single individuals or generic, if the tweet is aggressive or not. Our approach consists of a change of the space of representation of text into statistical descriptors which characterize the text. In addition, dimensional reduction is performed to 6 characteristics per class in order to make the method suitable for a Big Data environment. Frequency Analysis Interpolation (FAI) is the approach we use to achieve rank 5th in Spanish language and 9th in English language in sub-task B in both cases.
STUFIIT at SemEval-2019 Task 5: Multilingual Hate Speech Detection on Twitter with MUSE and ELMo Embeddings
Authors: Michal Bojkovsk{\'y}, Mat{\'u}{\v{s}} Pikuliak
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Hate Speech Detection, Word Embeddings

We present a number of models used for hate speech detection for Semeval 2019 Task-5: Hateval. We evaluate the viability of multilingual learning for this task. We also experiment with adversarial learning as a means of creating a multilingual model. Ultimately our multilingual models have had worse results than their monolignual counterparts. We find that the choice of word representations (word embeddings) is very crucial for deep learning as a simple switch between MUSE and ELMo embeddings has shown a 3-4{\%} increase in accuracy. This also shows the importance of context when dealing with online content.
Saagie at Semeval-2019 Task 5: From Universal Text Embeddings and Classical Features to Domain-specific Text Classification
Authors: Miriam Benballa, Sebastien Collet, Romain Picot-Clemente
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: General Classification, text-classification, Text Classification

This paper describes our contribution to SemEval 2019 Task 5: Hateval. We propose to investigate how domain-specific text classification task can benefit from pretrained state of the art language models and how they can be combined with classical handcrafted features. For this purpose, we propose an approach based on a feature-level Meta-Embedding to let the model choose which features to keep and how to use them.
SINAI at SemEval-2019 Task 5: Ensemble learning to detect hate speech against inmigrants and women in English and Spanish tweets
Authors: Flor Miriam Plaza-del-Arco, M. Dolores Molina-Gonz{\'a}lez, Maite Martin, L. Alfonso Ure{\~n}a-L{\'o}pez
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Ensemble Learning

Misogyny and xenophobia are some of the most important social problems. With the in- crease in the use of social media, this feeling ofhatred towards women and immigrants can be more easily expressed, therefore it can cause harmful effects on social media users. For this reason, it is important to develop systems ca- pable of detecting hateful comments automatically. In this paper, we describe our system to analyze the hate speech in English and Spanish tweets against Immigrants and Women as part of our participation in SemEval-2019 Task 5: hatEval. Our main contribution is the integration of three individual algorithms of predic- tion in a model based on Vote ensemble classifier.
SINAI-DL at SemEval-2019 Task 5: Recurrent networks and data augmentation by paraphrasing
Authors: Arturo Montejo-R{\'a}ez, Salud Mar{\'\i}a Jim{\'e}nez-Zafra, Miguel A. Garc{\'\i}a-Cumbreras, Manuel Carlos D{\'\i}az-Galiano
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Data Augmentation, Word Embeddings

This paper describes the participation of the SINAI-DL team at Task 5 in SemEval 2019, called HatEval. We have applied some classic neural network layers, like word embeddings and LSTM, to build a neural classifier for both proposed tasks. Due to the small amount of training data provided compared to what is expected for an adequate learning stage in deep architectures, we explore the use of paraphrasing tools as source for data augmentation. Our results show that this method is promising, as some improvement has been found over non-augmented training sets.
sthruggle at SemEval-2019 Task 5: An Ensemble Approach to Hate Speech Detection
Authors: Aria Nourbakhsh, Frida Vermeer, Gijs Wiltvank, Rob van der Goot
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Hate Speech Detection, Word Embeddings

In this paper, we present our approach to detection of hate speech against women and immigrants in tweets for our participation in the SemEval-2019 Task 5. We trained an SVM and an RF classifier using character bi- and trigram features and a BiLSTM pre-initialized with external word embeddings. We combined the predictions of the SVM, RF and BiLSTM in two different ensemble models. The first was a majority vote of the binary values, and the second used the average of the confidence scores. For development, we got the highest accuracy (75{\%}) by the final ensemble model with majority voting. For testing, all models scored substantially lower and the scores between the classifiers varied more. We believe that these large differences between the higher accuracies in the development phase and the lower accuracies we obtained in the testing phase have partly to do with differences between the training, development and testing data.
The binary trio at SemEval-2019 Task 5: Multitarget Hate Speech Detection in Tweets
Authors: Patricia Chiril, Farah Benamara Zitoune, V{\'e}ronique Moriceau, Abhishek Kumar
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Feature Engineering, Hate Speech Detection

The massive growth of user-generated web content through blogs, online forums and most notably, social media networks, led to a large spreading of hatred or abusive messages which have to be moderated. This paper proposes a supervised approach to hate speech detection towards immigrants and women in English tweets. Several models have been developed ranging from feature-engineering approaches to neural ones.
The Titans at SemEval-2019 Task 5: Detection of hate speech against immigrants and women in Twitter
Authors: Avishek Garain, Arpan Basu
Date: 2019-06-01 00:00:00
Proceeding: SEMEVAL 2019 6

This system paper is a description of the system submitted to {''}SemEval-2019 Task 5{''} Task B for the English language, where we had to primarily detect hate speech and then detect aggressive behaviour and its target audience in Twitter. There were two specific target audiences, immigrants and women. The language of the tweets was English. We were required to first detect whether a tweet is containing hate speech. Thereafter we were required to find whether the tweet was showing aggressive behaviour, and then we had to find whether the targeted audience was an individual or a group of people.