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| "date_generated": "2023-01-19T08:06:26.045721Z" | |
| }, | |
| "title": "ALT Submission for OSACT Shared Task on Offensive Language Detection", | |
| "authors": [ | |
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| "first": "Hamdy", | |
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| "abstract": "In this paper, we describe our efforts at OSACT Shared Task on Offensive Language Detection. The shared task consists of two subtasks: offensive language detection (Subtask A) and hate speech detection (Subtask B). For offensive language detection, a system combination of Support Vector Machines (SVMs) and Deep Neural Networks (DNNs) achieved the best results on development set, which ranked 1st in the official results for Subtask A with F1-score of 90.51% on the test set. For hate speech detection, DNNs were less effective and a system combination of multiple SVMs with different parameters achieved the best results on development set, which ranked 4th in official results for Subtask B with F1-macro score of 80.63% on the test set.", | |
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| "abstract": [ | |
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| "text": "In this paper, we describe our efforts at OSACT Shared Task on Offensive Language Detection. The shared task consists of two subtasks: offensive language detection (Subtask A) and hate speech detection (Subtask B). For offensive language detection, a system combination of Support Vector Machines (SVMs) and Deep Neural Networks (DNNs) achieved the best results on development set, which ranked 1st in the official results for Subtask A with F1-score of 90.51% on the test set. For hate speech detection, DNNs were less effective and a system combination of multiple SVMs with different parameters achieved the best results on development set, which ranked 4th in official results for Subtask B with F1-macro score of 80.63% on the test set.", | |
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| "section": "Abstract", | |
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| "text": "Detecting offensive language or hate speech on social media has gained a lot of interest recently. Use of offensive language and hate speech on social media can be an indication of hate crimes, toxic environment or level of antagonism against individuals or particular groups. Detecting offensive language and hate speech can also help in filtering out inappropriate content for users. Although there is a lot of research on detecting offensive language and hate speech (Agrawal and Awekar, 2018; Djuric et al., 2015; Davidson et al., 2017) , work on Arabic offensive language detection is still in its early stages with very few notable works (Mubarak and Darwish, 2019; Mubarak et al., 2017; Albadi et al., 2018; Alakrot et al., 2018) . Mubarak and Darwish (2019) report that only 1-2% of the tweets are offensive. The highly skewed distribution of data makes it extremely difficult to build useful datasets and effective systems. OS-ACT4 shared task (Mubarak et al., 2020) presents the problem of detecting offensive language and hate speech in Arabic tweets to the community. The shared task consists of 2 subtasks: offensive language detection (subtask A), and hate speech detection (subtask B). This paper describes the systems submitted for OSACT4 shared task on Offensive Language Detection by the team ALT. First, we experimented with classical machine learning classifiers such as Support Vector Machines (SVMs) that are trained on character and word-level features. Then, we experimented with Deep Neural Networks (DNNs) and Bidirectional Encoder Representations from Transformers (BERT). SVMs were seen to outperform the DNNs and BERT. Since we expect the different kinds of classifier to make different kinds of errors, we take the most promising and diverse individual classifiers and perform voting to decide the final output. Majority voting on SVMs, DNNs and BERT yielded better results than individual systems for subtask A on the development set. The best results on development set for subtask B were obtained by combining the output of different SVMs and considering an instance to be hate speech if any of the classifiers voted it to be hate speech.", | |
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| "text": "(Agrawal and Awekar, 2018;", | |
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| "text": "Djuric et al., 2015;", | |
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| "text": "Davidson et al., 2017)", | |
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| "text": "(Mubarak and Darwish, 2019;", | |
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| "text": "Albadi et al., 2018;", | |
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| "text": "Mubarak and Darwish (2019)", | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
| }, | |
| { | |
| "text": "In section 2, we describe the dataset and the tasks, in section 3, we describe our approach and compare results for subtask A, in section 4, we describe our approach and compare results for subtask B and in section 5, we provide conclusion of our work.", | |
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| "section": "Introduction", | |
| "sec_num": "1." | |
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| { | |
| "text": "In this section, we describe the dataset provided to the participants and the two subtasks.", | |
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| "section": "Dataset and Task Description", | |
| "sec_num": "2." | |
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| { | |
| "text": "The dataset for OSACT Shared task consists of 10,000 Arabic tweets that are tagged for offensiveness and hate speech. The 10,000 tweets are split into training, development and testing sets as shown in Table 1 .", | |
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| "text": "Table 1", | |
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| "section": "Dataset", | |
| "sec_num": "2.1." | |
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| "text": "Dev Test 7,000 1,000 2,000 The organizers note that the data is highly skewed. Only 19% of the tweets are tagged as offensive and 5% of the tweets are tagged as hate speech. Table 2 shows examples from the training set. If a tweet has offensive language (insults or threats) targeting a group of people based on their origin (nationality, race, or ethnicity), their ideology (religion, political affiliation, etc.), gender or any other common characteristics, this is considered as hate speech, so all hate speech tweets are offensive according to this definition.", | |
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| "text": "Table 2", | |
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| "section": "Train", | |
| "sec_num": null | |
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| "text": "The participants were required to produce labels indicating if a tweet is offensive (subtask A) or hate speech (subtask B). Each tweet took one of these labels for subtask A: \"OFF\" (offensive) or \"NOT OFF\" (not offensive), and \"HS\" (Hate Speech) or \"NOT HS\" (not Hate Speech) for subtask B. The full train and development data were made ", | |
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| "section": "Task Description", | |
| "sec_num": "2.2." | |
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| "text": "In this section, we discuss our approach in Subtask A. First, we describe the preprocessing step. Then, we describe the different models we experimented on and compare results of different models. Lastly, we perform an error analysis to understand the limitations of our models and the dataset.", | |
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| "section": "Subtask A: Offensive Language Detection", | |
| "sec_num": "3." | |
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| "text": "Preprocessing the tweets is an important step as the data from social media can be quite noisy as they contain a lot of emojis, text in mixed languages, excessive use of punctuation etc. It is important to note that some of our models (described in the next subsection) use pretrained word embeddings as feature. In order to reduce noise and be able to find more words in the embeddings, we perform the following steps for preprocessing the tweets.", | |
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| "section": "Preprocessing Tweets", | |
| "sec_num": "3.1." | |
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| { | |
| "text": "Step 1: Remove all words that contain non-Arabic characters.", | |
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| "section": "Preprocessing Tweets", | |
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| "text": "Step 2: Remove all diacritics.", | |
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| "section": "Preprocessing Tweets", | |
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| "text": "Step 3: Remove all punctuation.", | |
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| "section": "Preprocessing Tweets", | |
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| "text": "Step 4: Replace repeated characters with only one.", | |
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| "section": "Preprocessing Tweets", | |
| "sec_num": "3.1." | |
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| "text": "In our initial experiments, we noticed that the settings listed above produces the best results. Therefore, we keep the same preprocessing settings for all experiments. ", | |
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| "section": "Preprocessing Tweets", | |
| "sec_num": "3.1." | |
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| "text": "As the features for SVMs, we transform the tweets into bagof-n-grams vector weighted with logarithmic term frequencies (tf) multiplied with inverse document frequencies (idf). We created both character and word n-grams this way. We experimented with different ranges of character and word n-grams. We also experimented on using Mazajak embeddings (Abu Farha and Magdy, 2019) as features to SVM. Mazajak embeddings were trained on Twitter data, which matches the domain of data in our task. Therefore, we expect it to be more useful compared to other embeddings that are trained on different domain of data (BERT-Multilingual, for example). From table 3, we can see that the best results were obtained when character [1, 5] gram and word [1 \u2212 3] gram features were combined with pretrained Mazajak word embeddings.", | |
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| "section": "SVMs", | |
| "sec_num": "3.2.1." | |
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| "text": "FastText is an efficient deep-learning based system for learning embeddings and performing text classification (Joulin et al., 2016) . Since the task of offensive language detection is a text classification problem, we experimented with FastText, but as we can see from Table 3 , FastText was outperformed by other systems.", | |
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| "text": "For the FFNN architecture, we use four hidden layers with a different number of units (1000, 500, 500, 100) and a sigmoid activation function in each layer, followed by an softmax output layer. To train the network, we use a batch size of 256, with maximum epochs of 50 and early stopping using a 10% of the training set with similar distribution of the labels. The models are then optimized using rmsprop optimization function. The parameter were initialized with small random numbers, sampled from a uniform distribution. Since other systems outperform FFNN, we have not tuned the architecture for different parameters such as number of hidden units or the learning rates, among others.", | |
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| "section": "FFNN", | |
| "sec_num": "3.2.3." | |
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| "text": "We use two sets of features for this model. First, we have pretrained word embedding (Mazajak embeddings) features for each word. Second, we use CNN as characterlevel feature extractor. First layer of the network is used to project the input string to character embeddings, which is then passed through a convolutional layer and max-overtime pooling is applied to obtain fixed length representation of words. Character-level representations have been shown to capture morphology of words (Kim et al., 2015 ). If we were to use only word level features obtained from pretrained word embeddings, we would lose out information when a word does not appear in the vocabulary of the pre- ", | |
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| "ref_id": "BIBREF8" | |
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| "sec_num": "3.2.4." | |
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| "text": "To have a better understanding of where our system is failing will help us improve our system in future and understand the limitation of the data. We examine 100 samples from the development data and attempt to identify where and why our best system (No. 11 from table 3) is failing. Examining the specific tweets provides us with some interesting insights. NOT HS ? Table 8 : Errors analysis of subtask B the table contains reference to Game of Thrones. This is offensive only if context of Game of Thrones is taken into account. We cannot expect the classifier to be correct on such instances. The second entry provides us with an example of error that we can tackle in the future. We see that the hashtag # is offensive toward immigrants. But it's out of vocabulary for our systems. In the future, we can attempt to parse the hashtags into its constituent words and see if it improves the performance. The third entry in the table is quite interesting. The sentence uses an offensive word, , which means \"stealing\", but the sentence itself is not offensive. It's likely our system picked up the offensive word but failed to take into account the context in which the word was used. In future, we can particularly target resolving issues of ambiguous use of words. Table 6 shows the confusion matrix of our best system. We can see that the classifier is much more likely to misclassify offensive instance as not offensive compared to misclassifying not offensive instances as offensive. This is to be expected as the data is highly skewed with only 19% of the instances being offensive.", | |
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| "section": "Error analysis", | |
| "sec_num": "3.3." | |
| }, | |
| { | |
| "text": "For subtask B, The preprocessing is the same as section 3. In this section, we describe the different models we experimented on for subtask B, present the results for the different models and discuss errors made by the models.", | |
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| "section": "Subtask B: Hate Speech Detection", | |
| "sec_num": "4." | |
| }, | |
| { | |
| "text": "Since same tweets are used for both the subtasks, for subtask B, we focused only on those systems that were promising in subtask A. These systems include SVMs, CNN-BiLSTM and M-BERT. The accuracy, precision, recall and F1 score are reported in Table 7 . The precision, recall and F1 scores are macro averaged.", | |
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| "sec_num": "4.1." | |
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| { | |
| "text": "As the features for SVMs, we use the same features as subtask A. In addition, we also experiment with bagged SVM (5 estimators). The SVMs outperformed CNN-BiLSTM and M-BERT in this subtask as well.", | |
| "cite_spans": [], | |
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| "section": "SVMs", | |
| "sec_num": "4.1.1." | |
| }, | |
| { | |
| "text": "We keep the same structure and settings of the CNN-BiLSTM used in subtask A.", | |
| "cite_spans": [], | |
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| "section": "CNN-BiLSTM", | |
| "sec_num": "4.1.2." | |
| }, | |
| { | |
| "text": "Once again, we follow the same methodology as subtask A. The only difference is that the M-BERT is now fine-tuned on hate speech detection.", | |
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| "section": "M-BERT", | |
| "sec_num": "4.1.3." | |
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| { | |
| "text": "For subtask B, we opt for a slightly different ensemble method. The best ensemble on the development set was obtained by only considering the three SVMs (Nos. 1,2,3 from table 7). We also change the voting mechanism such that it's no longer majority voting. We consider an instance to be hate speech if any of the three SVMs vote it to be hate speech. This voting scheme was outperforming majority voting scheme on the development set. The official result on this ensemble is shown in ", | |
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| "section": "Ensemble Method", | |
| "sec_num": "4.1.4." | |
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| { | |
| "text": "We attempt to perform error analysis similar to subtask A by collecting 100 samples from development set and examining them. Unfortunately, we could not identify any particular reasons for the system to fail. We speculate this to be because of the data for subtask B being extremely skewed (with only 5% of the data being hate speech). Table 8 contains examples of errors made by the best system (no. 6 from table 7). Table 10 shows the confusion matrix of our best system on subtask 2. As expected, because of the extreme imbalance of the data, we can see that the classifier is prone to misclassifying hate speech instances as non hate speech instances.", | |
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| "text": "To conclude, we experimented heavily with classical machine learning and deep learning approaches to detect if a tweet is offensive or contains hate speech. We achieve state of the art results for offensive language detection by combination of SVMs, CNN-BiLSTM and Multilingual BERT. We achieve competitive results on hate speech detection with a system combination of SVMs. Our error analysis indicates certain types of errors for offensive language detection that can be addressed in future. In future, we aim to explore augmentation of hate speech data to build better systems.", | |
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| "TABREF0": { | |
| "num": null, | |
| "html": null, | |
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| "html": null, | |
| "content": "<table><tr><td>: Examples from the dataset</td></tr></table>", | |
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| }, | |
| "TABREF5": { | |
| "num": null, | |
| "html": null, | |
| "content": "<table><tr><td>Tweet True</td><td>Majority Reason</td></tr><tr><td>Label</td><td>Label</td></tr></table>", | |
| "type_str": "table", | |
| "text": "Comparison of different systems submitted to subtask A" | |
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| "TABREF6": { | |
| "num": null, | |
| "html": null, | |
| "content": "<table><tr><td>: Error analysis of subtask A</td></tr></table>", | |
| "type_str": "table", | |
| "text": "Wikipedia text, is quite different from the Twitter data used in the shared task. Articles on Wikipedia are typically written in a formal way and follow the structure and rules of grammar. Text on social media platforms such as Twitter, on the other hand, can be very informal and chaotic." | |
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| "TABREF7": { | |
| "num": null, | |
| "html": null, | |
| "content": "<table><tr><td/><td colspan=\"2\">: Official results on subtask A test set</td></tr><tr><td/><td>NOT OFF</td><td>OFF</td></tr><tr><td colspan=\"2\">NOT OFF 789</td><td>32</td></tr><tr><td>OFF</td><td>25</td><td>154</td></tr></table>", | |
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| "text": "" | |
| }, | |
| "TABREF8": { | |
| "num": null, | |
| "html": null, | |
| "content": "<table/>", | |
| "type_str": "table", | |
| "text": "Confusion matrix on subtask A dev set" | |
| }, | |
| "TABREF9": { | |
| "num": null, | |
| "html": null, | |
| "content": "<table><tr><td colspan=\"2\">No. Model</td><td>Features</td><td>Acc.</td><td>Prec.</td><td>Recall</td><td>F1</td></tr><tr><td>1</td><td>SVM</td><td>Mazajak SG-250 + char[1-5] + word[1-3]</td><td>96.2</td><td>72</td><td>78.2</td><td>74.7</td></tr><tr><td>2</td><td/><td>Char [2-6] Gram</td><td>97.1</td><td>72.5</td><td>88.8</td><td>78.2</td></tr><tr><td>3</td><td>Bagged SVM</td><td>Mazajak SG-250 + char[1-5] + word[1-3]</td><td>96.4</td><td>68.9</td><td>81.3</td><td>73.4</td></tr><tr><td>4</td><td>CNN-BiLSTM</td><td colspan=\"2\">Mazajak SG-100 + CNN Feature Extractor 95.9</td><td>76.1</td><td>67.5</td><td>70.9</td></tr><tr><td>5</td><td>M-BERT</td><td/><td>95.7</td><td>72.4</td><td>74.9</td><td>73</td></tr><tr><td>11</td><td>Ensemble(1+2+3)</td><td/><td>96.6</td><td>80</td><td>78.7</td><td>79.3</td></tr><tr><td/><td colspan=\"4\">Table 7: Comparison of different systems submitted to subtask B</td><td/><td/></tr><tr><td/><td/><td colspan=\"2\">Tweet True</td><td/><td colspan=\"2\">Majority Reason</td></tr><tr><td/><td/><td/><td>Label</td><td/><td>Label</td><td/></tr><tr><td/><td/><td/><td>HS</td><td/><td>NOT</td><td>?</td></tr><tr><td/><td/><td/><td>HS</td><td/><td>NOT</td><td>?</td></tr></table>", | |
| "type_str": "table", | |
| "text": "" | |
| }, | |
| "TABREF10": { | |
| "num": null, | |
| "html": null, | |
| "content": "<table><tr><td>Official</td><td>Acc.</td><td>Prec.</td><td>Recall</td><td>F1</td></tr><tr><td>Rank</td><td/><td/><td/><td/></tr><tr><td>4th</td><td>96.6%</td><td>83.8%</td><td>78.1%</td><td>80.6%</td></tr></table>", | |
| "type_str": "table", | |
| "text": "" | |
| }, | |
| "TABREF11": { | |
| "num": null, | |
| "html": null, | |
| "content": "<table><tr><td/><td>NOT HS</td><td>HS</td></tr><tr><td colspan=\"2\">NOT HS 940</td><td>16</td></tr><tr><td>HS</td><td>18</td><td>26</td></tr></table>", | |
| "type_str": "table", | |
| "text": "Official results on subtask B test set" | |
| }, | |
| "TABREF12": { | |
| "num": null, | |
| "html": null, | |
| "content": "<table/>", | |
| "type_str": "table", | |
| "text": "Confusion matrix on subtask B dev set" | |
| } | |
| } | |
| } | |
| } |