Posted Content•
Transfer Learning for Hate Speech Detection in Social Media
TL;DR: Developing automated text analytics methods, capable of jointly learning a single representation of hate from several smaller, unrelated data sets, that enables generating an interpretable two-dimensional text visualization called the Map of Hate that is capable of separating different types of hate speech and explaining what makes text harmful.
Abstract: In today's society more and more people are connected to the Internet, and its information and communication technologies have become an essential part of our everyday life. Unfortunately, the flip side of this increased connectivity to social media and other online contents is cyber-bullying and -hatred, among other harmful and anti-social behaviors. Models based on machine learning and natural language processing provide a way to detect this hate speech in web text in order to make discussion forums and other media and platforms safer. The main difficulty, however, is annotating a sufficiently large number of examples to train these models. In this paper, we report on developing automated text analytics methods, capable of jointly learning a single representation of hate from several smaller, unrelated data sets. We train and test our methods on the total of $37,520$ English tweets that have been annotated for differentiating harmless messages from racist or sexists contexts in the first detection task, and hateful or offensive contents in the second detection task. Our most sophisticated method combines a deep neural network architecture with transfer learning. It is capable of creating word and sentence embeddings that are specific to these tasks while also embedding the meaning of generic hate speech. Its prediction correctness is the macro-averaged F1 of $78\%$ and $72\%$ in the first and second task, respectively. This method enables generating an interpretable two-dimensional text visualization --- called the Map of Hate --- that is capable of separating different types of hate speech and explaining what makes text harmful. These methods and insights hold a potential for not only safer social media, but also reduced need to expose human moderators and annotators to distressing online~messaging.
Citations
More filters
TL;DR: This paper systematically reviews abusive language dataset creation and content in conjunction with an open website for cataloguing abusive language data leading to a synthesis providing evidence-based recommendations for practitioners working with this complex and highly diverse data.
Abstract: Data-driven and machine learning based approaches for detecting, categorising and measuring abusive content such as hate speech and harassment have gained traction due to their scalability, robustness and increasingly high performance. Making effective detection systems for abusive content relies on having the right training datasets, reflecting a widely accepted mantra in computer science: Garbage In, Garbage Out. However, creating training datasets which are large, varied, theoretically-informed and that minimize biases is difficult, laborious and requires deep expertise. This paper systematically reviews 63 publicly available training datasets which have been created to train abusive language classifiers. It also reports on creation of a dedicated website for cataloguing abusive language data hatespeechdata.com. We discuss the challenges and opportunities of open science in this field, and argue that although more dataset sharing would bring many benefits it also poses social and ethical risks which need careful consideration. Finally, we provide evidence-based recommendations for practitioners creating new abusive content training datasets.
113 citations
Posted Content•
TL;DR: HateBERT, a re-trained BERT model for abusive language detection in English, is introduced and a battery of experiments comparing the portability of the fine-tuned models across the datasets are discussed, suggesting that portability is affected by compatibility of the annotated phenomena.
Abstract: In this paper, we introduce HateBERT, a re-trained BERT model for abusive language detection in English. The model was trained on RAL-E, a large-scale dataset of Reddit comments in English from communities banned for being offensive, abusive, or hateful that we have collected and made available to the public. We present the results of a detailed comparison between a general pre-trained language model and the abuse-inclined version obtained by retraining with posts from the banned communities on three English datasets for offensive, abusive language and hate speech detection tasks. In all datasets, HateBERT outperforms the corresponding general BERT model. We also discuss a battery of experiments comparing the portability of the generic pre-trained language model and its corresponding abusive language-inclined counterpart across the datasets, indicating that portability is affected by compatibility of the annotated phenomena.
106 citations
Cites background from "Transfer Learning for Hate Speech D..."
...Previous work (Karan and Šnajder, 2018; Benk, 2019; Pamungkas and Patti, 2019; Rizoiu et al., 2019) has addressed this task by conflating portability with generalizability, forcing datasets with different phenomena into homogenous annotations by collapsing labels into (binary) macro-categories....
[...]
...Previous work (Karan and Šnajder, 2018; Benk, 2019; Pamungkas and Patti, 2019; Rizoiu et al., 2019) has addressed this task by conflating portability with generalizability, forcing datasets with different phenomena into homogenous annotations by collapsing labels into (binary) macro-categories....
[...]
TL;DR: The authors proposed a transfer learning approach for hate speech detection based on an existing pre-trained language model called BERT (Bidirectional Encoder Representations from Transformers) and evaluated the proposed model on two publicly available datasets that have been annotated for racism, sexism, hate or offensive content on Twitter.
Abstract: Disparate biases associated with datasets and trained classifiers in hateful and abusive content identification tasks have raised many concerns recently. Although the problem of biased datasets on abusive language detection has been addressed more frequently, biases arising from trained classifiers have not yet been a matter of concern. In this paper, we first introduce a transfer learning approach for hate speech detection based on an existing pre-trained language model called BERT (Bidirectional Encoder Representations from Transformers) and evaluate the proposed model on two publicly available datasets that have been annotated for racism, sexism, hate or offensive content on Twitter. Next, we introduce a bias alleviation mechanism to mitigate the effect of bias in training set during the fine-tuning of our pre-trained BERT-based model for hate speech detection. Toward that end, we use an existing regularization method to reweight input samples, thereby decreasing the effects of high correlated training set' s n-grams with class labels, and then fine-tune our pre-trained BERT-based model with the new re-weighted samples. To evaluate our bias alleviation mechanism, we employed a cross-domain approach in which we use the trained classifiers on the aforementioned datasets to predict the labels of two new datasets from Twitter, AAE-aligned and White-aligned groups, which indicate tweets written in African-American English (AAE) and Standard American English (SAE), respectively. The results show the existence of systematic racial bias in trained classifiers, as they tend to assign tweets written in AAE from AAE-aligned group to negative classes such as racism, sexism, hate, and offensive more often than tweets written in SAE from White-aligned group. However, the racial bias in our classifiers reduces significantly after our bias alleviation mechanism is incorporated. This work could institute the first step towards debiasing hate speech and abusive language detection systems.
65 citations
Posted Content•
TL;DR: This work proposes a simple, yet highly-effective Deep k-NN defense against both feature collision and convex polytope clean- label attacks on the CIFAR-10 dataset and shows that current clean-label poisoning attack strategies can be annulled, and serves as a strong yet simple-to-implement baseline defense to test future clean- Label poisoning attacks.
Abstract: Targeted clean-label data poisoning is a type of adversarial attack on machine learning systems in which an adversary injects a few correctly-labeled, minimally-perturbed samples into the training data, causing a model to misclassify a particular test sample during inference. Although defenses have been proposed for general poisoning attacks, no reliable defense for clean-label attacks has been demonstrated, despite the attacks' effectiveness and realistic applications. In this work, we propose a simple, yet highly-effective Deep k-NN defense against both feature collision and convex polytope clean-label attacks on the CIFAR-10 dataset. We demonstrate that our proposed strategy is able to detect over 99% of poisoned examples in both attacks and remove them without compromising model performance. Additionally, through ablation studies, we discover simple guidelines for selecting the value of k as well as for implementing the Deep k-NN defense on real-world datasets with class imbalance. Our proposed defense shows that current clean-label poisoning attack strategies can be annulled, and serves as a strong yet simple-to-implement baseline defense to test future clean-label poisoning attacks. Our code is available at this https URL
61 citations
Cites background from "Transfer Learning for Hate Speech D..."
...DUCTION Machine-learning-based systems are increasingly deployed in settings with high societal impact, such as biometric applications (Sun et al., 2014) and hate speech detection on social networks (Rizoiu et al., 2019), as well as settings with high cost of failure, such as autonomous driving (Chen et al., 2017a) and malware detection (Pascanu et al., 2015). In such settings, robustness to not just noise but also a...
[...]
01 Dec 2020
TL;DR: XHate-999 is presented, a multi-domain and multilingual evaluation data set for abusive language detection and it is shown that domain- and language-adaption, via intermediate masked language modeling on abusive corpora in the target language, can lead to substantially improved abusivelanguage detection in thetarget language in the zero-shot transfer setups.
Abstract: We present XHate-999, a multi-domain and multilingual evaluation data set for abusive language detection. By aligning test instances across six typologically diverse languages, XHate-999 for the first time allows for disentanglement of the domain transfer and language transfer effects in abusive language detection. We conduct a series of domain- and language-transfer experiments with state-of-the-art monolingual and multilingual transformer models, setting strong baseline results and profiling XHate-999 as a comprehensive evaluation resource for abusive language detection. Finally, we show that domain- and language-adaption, via intermediate masked language modeling on abusive corpora in the target language, can lead to substantially improved abusive language detection in the target language in the zero-shot transfer setups.
48 citations
Cites background from "Transfer Learning for Hate Speech D..."
...Some cross-domain approaches rely on term analysis, e.g., Wiegand et al. (2018a) start from a manually constructed sample of abusive terms and augment it automatically to aid domain adaptation, while Rizoiu et al. (2019) aim to construct task-agnostic representations of abusive language....
[...]
References
More filters
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Abstract: Learning to store information over extended time intervals by recurrent backpropagation takes a very long time, mostly because of insufficient, decaying error backflow. We briefly review Hochreiter's (1991) analysis of this problem, then address it by introducing a novel, efficient, gradient based method called long short-term memory (LSTM). Truncating the gradient where this does not do harm, LSTM can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units. Multiplicative gate units learn to open and close access to the constant error flow. LSTM is local in space and time; its computational complexity per time step and weight is O. 1. Our experiments with artificial data involve local, distributed, real-valued, and noisy pattern representations. In comparisons with real-time recurrent learning, back propagation through time, recurrent cascade correlation, Elman nets, and neural sequence chunking, LSTM leads to many more successful runs, and learns much faster. LSTM also solves complex, artificial long-time-lag tasks that have never been solved by previous recurrent network algorithms.
72,897 citations
"Transfer Learning for Hate Speech D..." refers methods in this paper
...2018), which we adapt to the current learning tasks using a bidirectional Long Short-Term Memory (bi-LSTM) (Hochreiter and Schmidhuber 1997) unit....
[...]
...We use state of the art pre-trained word embeddings such as Embeddings from Language Models (ELMo) (Peters et al. 2018), which we adapt to the current learning tasks using a bidirectional Long Short-Term Memory (bi-LSTM) (Hochreiter and Schmidhuber 1997) unit....
[...]
Proceedings Article•
12 Jun 2017TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.
Abstract: The dominant sequence transduction models are based on complex recurrent orconvolutional neural networks in an encoder and decoder configuration. The best performing such models also connect the encoder and decoder through an attentionm echanisms. We propose a novel, simple network architecture based solely onan attention mechanism, dispensing with recurrence and convolutions entirely.Experiments on two machine translation tasks show these models to be superiorin quality while being more parallelizable and requiring significantly less timeto train. Our single model with 165 million parameters, achieves 27.5 BLEU onEnglish-to-German translation, improving over the existing best ensemble result by over 1 BLEU. On English-to-French translation, we outperform the previoussingle state-of-the-art with model by 0.7 BLEU, achieving a BLEU score of 41.1.
52,856 citations
01 Oct 2014
TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Abstract: Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition.
30,558 citations
"Transfer Learning for Hate Speech D..." refers methods in this paper
...Here, we use the Global Vectors (GloVe) embedding (Pennington, Socher, and Manning 2014)) with a 200-dimensional embedding pre-trained on a data set of two billion tweets ELMo constructs a lookup table between the words observed in the training set, and their pre-trained representations....
[...]
Journal Article•
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
Abstract: We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic Neighbor Embedding (Hinton and Roweis, 2002) that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map. t-SNE is better than existing techniques at creating a single map that reveals structure at many different scales. This is particularly important for high-dimensional data that lie on several different, but related, low-dimensional manifolds, such as images of objects from multiple classes seen from multiple viewpoints. For visualizing the structure of very large datasets, we show how t-SNE can use random walks on neighborhood graphs to allow the implicit structure of all of the data to influence the way in which a subset of the data is displayed. We illustrate the performance of t-SNE on a wide variety of datasets and compare it with many other non-parametric visualization techniques, including Sammon mapping, Isomap, and Locally Linear Embedding. The visualizations produced by t-SNE are significantly better than those produced by the other techniques on almost all of the datasets.
30,124 citations
01 Jan 2014
TL;DR: In this paper, the encoder and decoder of the RNN Encoder-Decoder model are jointly trained to maximize the conditional probability of a target sequence given a source sequence.
Abstract: In this paper, we propose a novel neural network model called RNN Encoder‐ Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder‐Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.
19,998 citations