SlowGuess/ACL_2020_OCR huggingface dataset:
The STL experiments are conducted on the abuse detection datasets independently. As mentioned in the STL section, we experiment with four different model configurations to select the best STL baseline.
Table 1a presents the evaluation results of the STL models trained and tested on the OffensEval dataset, and Table 1b on the Waseem and Hovy dataset. The best results are highlighted in bold and are in line with the validation set results. We select the best performing STL model configuration on each dataset and use it as part of the corresponding MTL architecture in the MTL experiments below.
Model	P	R	F1
STLBiLSTM+attn	77.40	73.27	74.40
MTLHard	77.21	73.30	74.51
MTLEncoder	77.47	73.82	74.97
MTLGatedEncoder	77.46	75.27	76.03†
(a) Twitter - OffensEval results.
Model	P	R	F1
STLmaxpool+MLP	79.39	78.20	78.33
MTLHard	79.34	77.61	77.90
MTLEncoder	80.77	78.18	79.02
MTLGatedEncoder	80.12	79.60	79.55†
(b) Twitter - Waseem and Hovy results.
Table 2: STL vs. MTL with emotion detection as the auxiliary task.  $\dagger$  indicates statistically significant improvement over STL.
Dataset	Method	P	R	F1
OE	MTL	77.46	75.27†	76.03†
Transfer	76.81	73.71	74.67
W&H	MTL	80.12	79.60†	79.55
Transfer	81.28	77.72	79.07
Table 3: MTL vs. transfer learning performance.  $OE$  refers to the OffensEval dataset and  $W\& H$  to the Waseem&Hovy dataset.  $\dagger$  indicates statistically significant improvements.
5.3 MTL experiments
In this section, we examine the effectiveness of the MTL models for the abuse detection task and explore the impact of using emotion detection as an auxiliary task. We also compare the performance of our MTL models with that of a transfer learning approach.
Emotion detection as an auxiliary task In this experiment, we test whether incorporating emotion detection as an auxiliary task improves the performance of abuse detection. Tables 2a and 2b show the results on OffensEval and Waseem and Hovy datasets ( $\dagger$  indicates statistically significant results over the corresponding STL model). Learning emotion and abuse detection jointly proved beneficial, with MTL models achieving statistically significant improvement in F1 using the Gated Double Encoder Model  $\mathrm{MTL}_{\text{GatedEncoder}}$  ( $p < 0.05$ , using a paired t-test). This suggests that affective features from the shared encoder benefit the abuse detection task.
MTL vs. transfer learning Transfer learning is an alternative to MTL that also allows us to transfer knowledge from one task to another. This experiment aims to compare the effectiveness of MTL against transfer learning. We selected the MTL model with the best performance in abuse detection and compared it against an identical model, but trained in a transfer learning setting. In this setup, we first train the model on the emotion detection task until convergence and then proceed by fine-tuning it for the abuse detection task. Table 3 presents the comparison between MTL and transfer learning, for which we use the same architecture and hyperparameter configuration as MTL. We observe that MTL outperforms transfer learning and provides statistically significant  $(p < 0.05)$  results on both OffensEval and Waseem and Hovy datasets.
Auxiliary task Our results show that emotion detection significantly improves abuse detection on both OffensEval and Waseem and Hovy datasets. Table 4 presents examples of improvements in both datasets achieved by the MTL $_{GatedEncoder}$  model, over the STL model. In the examples, the highlighted words are emotion evocative words, which are also found in the SemEval2018 Emotion dataset. As the emotion detection task encourages the model to learn to predict the emotion labels for the examples that contain these words, the word representations and encoder weights that are learned by the model encompass some affective knowledge. Ultimately, this allows the MTL model to determine the affective nature of the example, which may help it to classify abuse more accurately. It is also interesting to observe that a controversial person or topic may strongly influence the classification of the sample containing it. For example, sentences referring to certain politicians may be classified as Offensive, regardless of the context. An example instance of this can be found in Table 4. $^4$  The MTL model, however, classifies it correctly, which may be attributed to the excessive use of “!” marks. The latter is one of the most frequently used symbols in the SemEval2018 Emotion dataset, and it can encompass many emotions such as surprise, fear, etc., therefore, not being indicative of a particular type of emotion. Such knowledge can be learned within the shared features of the MTL model.
4We mask the name using the _POLITICIAN_ tag.
Sample	STL	MTL	Gold Label	Predicted Emotion
Shut up Katie and Nikki... That is all :) #HASHTAG	neither	sexism	sexism	disgust
MTN_ That's the disadvantage of following a religion of uneducated morons, so that you have to rely on Kufir for everything.	neither	racism	racism	anger, disgust
MTN_ Earthly tyrants want to be feared because for them fear is control and obedience. The writer of the Quran was unsophisticated.	neither	racism	racism	fear, optimism
MTN_ And does this surprise any of us _POLITICIAN_ SUPPORTERS!!! Not at all... We have heard him accused of everything that can be imagined!!! We still stand BE-HIND _POLITICIAN_!!!	Offensive	NotOffensive	NotOffensive	None
MTN_ I m pretty sure you are not too bad yourself...thanks for a lil bit of sweetness on this brutal world	Offensive	NotOffensive	NotOffensive	joy, optimism
Table 4: STL vs. MTL: samples from Twitter - Waseem and Hovy and Twitter - OffensEval datasets, where superior performance of MTL is observed. The 'predicted emotion' column contains the emotion labels predicted on the abuse detection data. The name of the politician in the fourth row is masked using the _POLITICIAN_ tag.
MTL vs. transfer learning This experiment demonstrates that MTL achieves higher performance than transfer learning in a similar experimental setting. The higher performance may be indicative of a more stable way of transferring knowledge, which leads to better generalization. In the MTL framework, since the shared parameters are updated alternately, each task learns some knowledge that may be mutually beneficial to both related tasks, which leads to a shared representation that encompasses the knowledge of both tasks and hence is more generalized. In contrast, in the case of transfer learning, the primary task fine-tunes the knowledge from the auxiliary task (i.e., in the form of pre-trained parameters) for its task objective and may be forgetting auxiliary task knowledge.
In this paper, we proposed a new approach to abuse detection, which takes advantage of the affective features to gain auxiliary knowledge through an MTL framework. Our experiments demonstrate that MTL with emotion detection is beneficial for the abuse detection task in the Twitter domain. The mutually beneficial relationship that exists between
these two tasks opens new research avenues for improvement of abuse detection systems in other domains as well, where emotion would equally play a role. Overall, our results also suggest the superiority of MTL over STL for abuse detection. With this new approach, one can build more complex models introducing new auxiliary tasks for abuse detection. For instance, we expect that abuse detection may also benefit from joint learning with complex semantic tasks, such as figurative language processing and inference.
Guy Aglionby, Chris Davis, Pushkar Mishra, Andrew Caines, Helen Yannakoudakis, Marek Rei, Ekaterina Shutova, and Paula Buttery. 2019. CAMsterdam at SemEval-2019 task 6: Neural and graph-based feature extraction for the identification of offensive tweets. In Proceedings of the 13th International Workshop on Semantic Evaluation, pages 556-563, Minneapolis, Minnesota, USA. Association for Computational Linguistics.
Md Shad Akhtar, Deepanway Ghosal, Asif Ekbal, Pushpak Bhattacharyya, and Sadao Kurohashi. 2018. A multi-task ensemble framework for emotion, sen
timent and intensity prediction. arXiv preprint arXiv:1808.01216.
Rich Caruana. 1997. Multitask learning. Machine learning, 28(1):41-75.
Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning, pages 160-167. ACM.
Maral Dadvar, Dolf Trieschnigg, Roeland Ordelman, and Franciska de Jong. 2013. Improving cyberbullying detection with user context. In European Conference on Information Retrieval, pages 693-696. Springer.
Taner Danisman and Adil Alpkocak. 2008. Feeler: Emotion classification of text using vector space model. In AISB 2008 Convention Communication, Interaction and Social Intelligence, volume 1, page 53.
Karthik Dinakar, Roi Reichart, and Henry Lieberman. 2011. Modeling the detection of textual cyberbullying. In fifth international AAAI conference on webblogs and social media.
Paul Ekman. 1992. An argument for basic emotions. Cognition & emotion, 6(3-4):169-200.
Antigoni Maria Founta, Despoina Chatzakou, Nicolas Kourtellis, Jeremy Blackburn, Athena Vakali, and Ilias Leontiadis. 2019. A unified deep learning architecture for abuse detection. In Proceedings of the 10th ACM Conference on Web Science, pages 105-114. ACM.
Njagi Dennis Gitari, Zhang Zuping, Hanyurwimfura Damien, and Jun Long. 2015. A lexicon-based approach for hate speech detection. International Journal of Multimedia and Ubiquitous Engineering, 10(4):215-230.
Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics, pages 249-256.
Geoffrey E Hinton. 1987. Learning translation invariant recognition in a massively parallel networks. In International Conference on Parallel Architectures and Languages Europe, pages 1-13. Springer.
Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735-1780.
Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Edward A Mabry. 1974. Dimensions of profanity. *Psychological Reports*, 35(1):387-391.
Albert Mehrabian. 1996. Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament. Current Psychology, 14(4):261-292.
Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, and Ekaterina Shutova. 2018a. Author profiling for abuse detection. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1088-1098, Santa Fe, New Mexico, USA. Association for Computational Linguistics.
Pushkar Mishra, Marco Del Tredici, Helen Yannakoudakis, and Ekaterina Shutova. 2019a. Abusive Language Detection with Graph Convolutional Networks. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2145-2150, Minneapolis, Minnesota. Association for Computational Linguistics.
Pushkar Mishra, Helen Yannakoudakis, and Ekaterina Shutova. 2018b. Neural character-based composition models for abuse detection. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), pages 1-10, Brussels, Belgium. Association for Computational Linguistics.
Pushkar Mishra, Helen Yannakoudakis, and Ekaterina Shutova. 2019b. Tackling online abuse: A survey of automated abuse detection methods. *Arxiv: abs/1908.06024*.
Saif Mohammad, Felipe Bravo-Marquez, Mohammad Salameh, and Svetlana Kiritchenko. 2018. Semeval-2018 task 1: Affect in tweets. In Proceedings of The 12th International Workshop on Semantic Evaluation, pages 1-17.
Emily R. Munro. 2011. The protection of children online: a brief scoping review to identify vulnerable groups.
Chikashi Nobata, Joel Tetreault, Achint Thomas, Yashar Mehdad, and Yi Chang. 2016. Abusive language detection in online user content. In WWW, pages 145-153.
Sinno Jialin Pan and Qiang Yang. 2009. A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10):1345-1359.
Ji Ho Park and Pascale Fung. 2017. One-step and two-step classification for abusive language detection on twitter. arXiv preprint arXiv:1706.01206.
George TW Patrick. 1901. The psychology of profanity. Psychological Review, 8(2):113.
John Pavlopoulos, Prodromos Malakasiotis, and Ion Androutsopoulos. 2017. Deep learning for user comment moderation. In ALW, pages 25-35.
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532-1543.
Isidoros Perikos and Ioannis Hatzilygeroudis. 2016. Recognizing emotions in text using ensemble of classifiers. Engineering Applications of Artificial Intelligence, 51:191-201.
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word representations. arXiv preprint arXiv:1802.05365.
Maarten Sap, Dallas Card, Saadia Gabriel, Yejin Choi, and Noah A Smith. 2019. The risk of racial bias in hate speech detection. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1668-1678.
Sara Owsley Sood, Judd Antin, and Elizabeth Churchill. 2012. Using crowdsourcing to improve profanity detection. In 2012 AAAI Spring Symposium Series.
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15(1):1929-1958.
Cindy Wang. 2018. Interpreting neural network hate speech classifiers. In Proceedings of the 2nd Workshop on Abusive Language Online (ALW2), pages 86-92.
Zeerak Waseem and Dirk Hovy. 2016. Hateful symbols or hateful people? predictive features for hate speech detection on twitter. In Proceedings of the NAACL Student Research Workshop, pages 88-93, San Diego, California. Association for Computational Linguistics.
Wenpeng Yin, Katharina Kann, Mo Yu, and Hinrich Schütze. 2017. Comparative study of cnn and rn for natural language processing. arXiv preprint arXiv:1702.01923.
Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar. 2019a. Predicting the type and target of offensive posts in social media. arXiv preprint arXiv:1902.09666.
Marcos Zampieri, Shervin Malmasi, Preslav Nakov, Sara Rosenthal, Noura Farra, and Ritesh Kumar. 2019b. Semeval-2019 task 6: Identifying and categorizing offensive language in social media (offenseval). arXiv preprint arXiv:1903.08983.
Yu Zhang and Qiang Yang. 2017. A survey on multitask learning. arXiv preprint arXiv:1707.08114.
Yuxiang Zhang, Jiamei Fu, Dongyu She, Ying Zhang, Senzhang Wang, and Jufeng Yang. 2018. Text emotion distribution learning via multi-task convolutional neural network. In IJCAI, pages 4595-4601.
4279