Question: Do they use a unified model to handle the annotated data in all three languages and five tasks?
Evidence: With the expanding amount of text data generated on different social media platforms, current filters are insufficient to prevent the spread of hate speech. Most internet users involved in a study conducted by the Pew Research Center report having been subjected to offensive name calling online or witnessed someone being physically threatened or harassed online. Additionally, Amnesty International within Element AI have lately reported that many women politicians and journalists are assaulted every 30 seconds on Twitter. This is despite the Twitter policy condemning the promotion of violence against people on the basis of race, ethnicity, national origin, sexual orientation, gender identity, religious affiliation, age, disability, or serious disease. Hate speech may not represent the general opinion, yet it promotes the dehumanization of people who are typically from minority groups BIBREF0, BIBREF1 and can incite hate crime BIBREF2.
Moreover, although people of various linguistic backgrounds are exposed to hate speech BIBREF3, BIBREF2, English is still at the center of existing work on toxic language analysis. Recently, some research studies have been conducted on languages such as German BIBREF4, Arabic BIBREF5, and Italian BIBREF6. However, such studies usually use monolingual corpora and do not contrast, or examine the correlations between online hate speech in different languages. On the other hand, tasks involving more than one language such as the hatEval task, which covers English and Spanish, include only separate classification tasks, namely (a) women and immigrants as target groups, (b) individual or generic hate and, (c) aggressive or non-aggressive hate speech.
Treating hate speech classification as a binary task may not be enough to inspect the motivation and the behavior of the users promoting it and, how people would react to it. For instance, the hateful tweets presented in Figure FIGREF5 show toxicity directed towards different targets, with or without using slurs, and generating several types of reactions. We believe that, in order to balance between truth and subjectivity, there are at least five important aspects in hate speech analysis. Hence, our annotations indicate (a) whether the text is direct or indirect; (b) if it is offensive, disrespectful, hateful, fearful out of ignorance, abusive, or normal; (c) the attribute based on which it discriminates against an individual or a group of people; (d) the name of this group; and (e) how the annotators feel about its content within a range of negative to neutral sentiments. To the best of our knowledge there are no other hate speech datasets that attempt to capture fear out of ignorance in hateful tweets or examine how people react to hate speech. We claim that our multi-aspect annotation schema would provide a valuable insight into several linguistic and cultural differences and bias in hate speech.
We use Amazon Mechanical Turk to label around 13,000 potentially derogatory tweets in English, French, and Arabic based on the above mentioned aspects and, regard each aspect as a prediction task. Since in natural language processing, there is a peculiar interest in multitask learning, where different tasks can be used to help each other BIBREF7, BIBREF8, BIBREF9, we use a unified model to handle the annotated data in all three languages and five tasks. We adopt BIBREF8 as a learning algorithm adapted to loosely related tasks such as our five annotated aspects and, use the Babylon cross-lingual embeddings BIBREF10 to align the three languages. We compare the multilingual multitask learning settings with monolingual multitask, multilingual single-task, and monolingual single-task learning settings respectively. Then, we report the performance results of the different settings and discuss how each task affects the remaining ones. We release our dataset and code to the community to extend research work on multilingual hate speech detection and classification.
Since in natural language processing, there is a peculiar interest in multitask learning, where different tasks can be used to help each other, we use a unified model to handle the annotated data in all three languages and five tasks.
Answer: Yes.