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| "paper_id": "2021", |
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| "date_generated": "2023-01-19T16:20:27.146905Z" |
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| "title": "Simon @ DravidianLangTech-EACL2021: Meme Classification for Tamil with BERT", |
| "authors": [ |
| { |
| "first": "Qinyu", |
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| "last": "Que", |
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| "institution": "Yunnan University", |
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| "settlement": "Yunnan", |
| "country": "P.R. China" |
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| "abstract": "In this paper, we introduce the system for the task of meme classification for Tamil, submitted by our team. In today's society, social media has become an important platform for people to communicate. We use social media to share information about ourselves and express our views on things. It has gradually developed a unique form of emotional expression on social media-meme. The meme is an expression that is often ironic. This also gives the meme a unique sense of humor. But it's not just positive content on social media. There's also a lot of offensive content. Meme's unique expression makes it often used by some users to post offensive content. Therefore, it is very urgent to detect the offensive content of the meme. Our team uses the natural language processing method to classify the offensive content of the meme. Our team combines the BERT model with the CNN to improve the model's ability to collect statement information. Finally, the F1-score of our team in the official test set is 0.49, and our method ranks 5th.", |
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| "text": "In this paper, we introduce the system for the task of meme classification for Tamil, submitted by our team. In today's society, social media has become an important platform for people to communicate. We use social media to share information about ourselves and express our views on things. It has gradually developed a unique form of emotional expression on social media-meme. The meme is an expression that is often ironic. This also gives the meme a unique sense of humor. But it's not just positive content on social media. There's also a lot of offensive content. Meme's unique expression makes it often used by some users to post offensive content. Therefore, it is very urgent to detect the offensive content of the meme. Our team uses the natural language processing method to classify the offensive content of the meme. Our team combines the BERT model with the CNN to improve the model's ability to collect statement information. Finally, the F1-score of our team in the official test set is 0.49, and our method ranks 5th.", |
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| "text": "Everything has two sides, and information technology is no exception. On the one hand, the rapid development of information technology makes it easy for people to communicate and discuss on social platforms Mahesan, 2019, 2020a,b) . On the other hand, the rapid development of information technology has also led to the proliferation of offensive content on the Internet Chakravarthi and Muralidaran, 2021; Chakravarthi et al., 2020a; Mandl et al., 2020) . Meme makes people like it because of its unique sense of humor. This also allows meme to spread rapidly on social media. But these memes don't just contain positive content. Some memes also contain offensive content. With the rapid growth of offensive content, hateful content, and offensive content on the Internet, the detection of offensive content on Meme has become more and more important. The meme is special. Different people have different perceptions of its content. Some memes are only accessible to a certain group of people, and others don't get the true meaning of these memes. This also makes people unclear about the offensive content in Meme, and also increases the difficulty of detecting the offensive content in Meme. The meme usually consists of pictures and text. Our team uses a natural language processing method to detect offensive content in the meme. We do the classification for Memes in Tamil language.", |
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| "text": "Chakravarthi and Muralidaran, 2021;", |
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| "text": "Tamil is a Dravidian language spoken by the Tamil people of South Asia as their first language (Chakravarthi et al., 2018) . For over 2600 years, Tamil literature has been recorded. Tamil has the oldest non-Sanskritic Indian literature in any Indian language. Tamil was the first Indian classical language to be listed, and it is one of the world's oldest classical languages. There are 12 vowels, 18 consonants, and one special character, the aytam, in the present Tamil script. The vowels and consonants merge to form 216 compound characters, for a total of 247 (12 + 18 + 1 + (12 x 18) characters (Chakravarthi et al., 2020b). In Tamil, suffixes are used to denote noun class, number, and case, as well as verb tense and other grammatical categories. In comparison to Sanskrit, which is the standard for most Aryan languages, Tamil's standard metalinguistic terminology and scholarly vocabulary is Tamil. Tamil is under-resourced languages (Chakravarthi, 2020).", |
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| "text": "Many researchers have made efforts to detect offensive language. As people communicate more and Figure 1 : The description of the model used for this task more online, the detection of hateful content and offensive language has become necessary. Content published in traditional media such as newspapers and television is heavily censored. But people are often free to express their opinions on the new social media platforms. Troll memes disrupt the user's communication experience on these platforms. Nascimento et al. (Reddy and Vasu, 2002) believe that people can communicate without face to face in social networks. So people don't know each other's point of view and background. People abuse anonymous nature of the internet by using offensive language. This greatly accelerates the spread of offensive language. Nascimento et al. classified the Brazilian grape language text into hate languages. They used data sets from the Brazilian 55chan Imageboard. They used three classifiers for classification, with the best F1-score being 0.955. The meme is a regional language, and the expression in communication is that only some people can know the specific meaning of a certain meme. This characteristic of meme makes its offensive language have a specific target group. There has been an increase in Islamophobic comments on social media platforms. It ostracized Muslims in the community. B. Vidgen' team built a model that could distinguish between Islamophobia (Ross et al., 2017) . This model can divide content into strongly Islamophobic content, weakly Islamophobic content, and non-Islamophobic content. The targeted groups are not only Muslims, but also immigrant groups and Jewish groups (Vidgen and Yasseri, 2020; Bilewicz et al., 2013; Finkelstein Label Train set Test set troll 1282 395 Not troll 1018 272 Djuric et al., 2015) . In recent years, developments in the field of detecting offensive content have contributed to many problems (Bohra et al., 2018; Jurgens et al., 2019; Caselli et al., 2020; Fortuna et al., 2019) .", |
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| "text": "3 Methodology and Corpus", |
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| "text": "Competition organizers provides dataset with text and image as features (Suryawanshi et al., 2020; Suryawanshi and Chakravarthi, 2021) . Because our team uses natural language processing to identify offensive content. We used the text portion of the dataset. The text portion of the dataset contains two types of tags, one troll, and the other Not troll. The tag troll represents a meme that has provocative or offensive content. The Not troll tag indicates that the meme has no offensive or provocative content. The detailed contents of the dataset are shown in Table 1 .", |
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| "text": "We reviewed the results of recent competitions and found that participants who used pre-trained language models scored highly on many of the tasks. For example, in the Hate Speech and Offensive Content Identification in Indo-European", |
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| "text": "Hyper-parameter Value dropout 0.5 learning rate 1e-5 epoch 10 per gpu train batch size 32 gradient accumulation steps 8 Languages(HASOC 1 ) competition, many contestants using the BERT model rank high. And in the competition, many players adopted the finetuned BERT (Devlin et al., 2019) model method to complete the task. In our model, we use XLM-Roberta (Conneau et al., 2020) as our preprocessing model. The XLM-Roberta model is a multilingual processing model. It can handle text in 100 languages. Compared with the BERT model, the XLM-Roberta model greatly increases the amount of data to be trained. XLM-Roberta has no prediction for the next sentence. Our team fine-tuned the superstructure of the XLM-Roberta model to enhance the semantic collection capability of XLM-Roberta. The specific model is shown in Figure 1 . In the first step, we get the pooler output of XLM-Roberta (P O). For classification tasks, the output of XLM-Roberta(pooler output) is obtained by its last layer hidden state of the first token of the sequence (CLS token) further processed by a linear layer and a Tanh activation function. Second, we combine the output of the last three hidden layers of XLM-Roberta with CNN (Simonyan and Zisserman, 2014) . Finally, we concatenate the CNN output with the pooler output of XLM-Roberta and put it into the classifier.", |
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| "text": "Our team uses the stratified 5-fold cross-validation to divide the training sets provided by the competition organizer into new training sets and validation sets. Our pre-training model is from XLM Roberta -Base 2 . We put the hyper-parameters used in the experiment in Table 2 . Finally, Our method rankings 5th in this task, and the F1-score is 0.49.", |
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| "section": "Experiment and Results", |
| "sec_num": "4" |
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| "text": "This paper describes the model and results used by the Simon team in the meme classification task.", |
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| "text": "This model is a fine-tuned XLM-Roberta model, which improves the ability of the model to obtain sentence information. The datasets used for this competition are slightly unbalanced. This also affects the final result of the model. Our team will continue to improve the model to achieve better results. The code is available online. 3", |
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| "text": "https://competitions.codalab.org/competitions/26027 2 https://huggingface.co/xlm-roberta-base", |
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| "text": "https://https://github.com/queqinyu/EACL", |
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