| { | |
| "paper_id": "2021", | |
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| "date_generated": "2023-01-19T13:13:57.749923Z" | |
| }, | |
| "title": "IIITT at CASE 2021 Task 1: Leveraging Pretrained Language Models for Multilingual Protest Detection", | |
| "authors": [ | |
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| "first": "Pawan", | |
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| "institution": "Indian Institute of Information Technology Tiruchirappalli", | |
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| "institution": "Indian Institute of Information Technology Tiruchirappalli", | |
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| "first": "Adeep", | |
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| "institution": "Indian Institute of Information Technology Tiruchirappalli", | |
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| { | |
| "first": "Bharathi", | |
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| "institution": "National University of Ireland", | |
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| "abstract": "In a world abounding in constant protests resulting from events like a global pandemic, climate change, religious or political conflicts, there has always been a need to detect events/protests before getting amplified by news media or social media. This paper demonstrates our work on the sentence classification subtask of multilingual protest detection in CASE@ACL-IJCNLP 2021. We approached this task by employing various multilingual pre-trained transformer models to classify if any sentence contains information about an event that has transpired or not. Furthermore, we performed soft voting over the models, achieving the best results among the models, accomplishing a macro F1-Score of 0.8291, 0.7578, and 0.7951 in English, Spanish, and Portuguese, respectively. The source codes for our systems are published 1 .", | |
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| { | |
| "text": "In a world abounding in constant protests resulting from events like a global pandemic, climate change, religious or political conflicts, there has always been a need to detect events/protests before getting amplified by news media or social media. This paper demonstrates our work on the sentence classification subtask of multilingual protest detection in CASE@ACL-IJCNLP 2021. We approached this task by employing various multilingual pre-trained transformer models to classify if any sentence contains information about an event that has transpired or not. Furthermore, we performed soft voting over the models, achieving the best results among the models, accomplishing a macro F1-Score of 0.8291, 0.7578, and 0.7951 in English, Spanish, and Portuguese, respectively. The source codes for our systems are published 1 .", | |
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| "text": "The recent surge in social media users has led many people to express their opinions on various global issues. These opinions travel far and wide within a matter of seconds (Hossny et al., 2018) . This can influence many people and may engage public movements (Won et al., 2017a) . Therefore, there is a definite need to detect these protests and analyse them to know the significant areas of disinterest.", | |
| "cite_spans": [ | |
| { | |
| "start": 173, | |
| "end": 194, | |
| "text": "(Hossny et al., 2018)", | |
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| "start": 260, | |
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| "text": "(Won et al., 2017a)", | |
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| "section": "Introduction", | |
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| "text": "Being a free and easy to use platform, social media has become a part of our day to day life. It incorporates people of different ages, gender, location, religions, background, and so on. The enormous number of rich and diversified users results in an enormous amount of information being generated, which is helpful in many ways (Kapoor et al., 2018) . Some of this even contains private information about the users, which others could misuse. Cases were also found where certain users were being targeted and harassed by people using this platform, a common scenario in cyberbullying (Abaido, 2020) .", | |
| "cite_spans": [ | |
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| "text": "Social media plays a crucial role in amplifying these protests and movements (Won et al., 2017b) . It enables political groups and protesters to organise protest movements and share information. It acts as a platform for the people who are underrepresented by giving a voice to them. It also offers new opportunities for people to engage in activism, political resistance, and protest outside the political groups and civic institutions. Thus, it has a social impact on everyone (Pulido et al., 2018) . It is to be noted that social media, similar to news media, plays a vital role in its social and political events worldwide (Holt et al., 2013) . For the above reasons, we can state that social media plays a crucial role in most worldwide events.", | |
| "cite_spans": [ | |
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| "section": "Introduction", | |
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| "text": "The English language is widely regarded as the first Lingua Franca. Statistically, it is one of the most widely spoken languages globally, having official status in over 53 countries (Crystal, 2008) . Over 400 million people speak English as their primary language and widely spoken in the United States and the United Kingdom. BlackLivesMatter (Dave et al., 2020), EarthDay (Rome, 2010) are some of the major protests that have occurred in these countries. Espa\u00f1ol commonly referred to as Spanish, is spoken by over 360 million people worldwide, with most of its speakers residing in Mexico, Argentina, Spain. 15-M Movement (Casero-Ripoll\u00e9s and Feenstra, 2012) and YoSoy132 (Garc\u00eda and Trer\u00e9, 2014) Portuguese Not-event E uma nova experi\u00eancia mobilizat\u00f3ria.", | |
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| "end": 387, | |
| "text": "(Rome, 2010)", | |
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| "end": 661, | |
| "text": "(Casero-Ripoll\u00e9s and Feenstra, 2012)", | |
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| "text": "Portuguese Event E decidiram ir\u00e0s aulas e passar o dia de saia.", | |
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| "text": "Portuguese Not-event cial media, youth, exaggeration of certain events. (Basile and Caselli, 2020) . Any early detection of mass protest detection through social media platforms such as Facebook, Twitter, and Instagram to help minimizing the aftermath of the protests (Wilson, 2017) . This has motivated Natural Language Processing (NLP) researchers to develop NLP systems to generalize on data coming from diverse sources to leverage the NLP systems to more realistic environments (B\u00fcy\u00fck\u00f6z et al., 2020) . Hence, there is a need to develop NLP systems that could be generalized to any protest/events (Peng et al., 2013) , which has motivated us to participate in the shared task for multilingual protest detection (H\u00fcrriyetoglu et al., 2019a ) The objective of the task is to identify if any sentence talks about any mentions of protests or events in three languages, namely, English, Spanish, and Portuguese. Hence, we treat this as a sequence classification task. The rest of the paper is organized as follows, Section 2 presents previous work on protest detection and analysis. Section 3 entails a comprehensive analysis of the dataset used for our cause. Next, section 4 gives a detailed description of the models used for the multilingual event detection. Finally, section 5 analyses the results obtained, and Section 6 concludes our work while discussing the potential directions for future work.", | |
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| "text": "The need to detect events that could lead to protests is of prime interest to sociologists and governments (Danilova et al., 2016) . There are several active ongoing projects for socio-political event systems such as KEDS (Kansas Event Data System) (Schrodt and Hall, 2006) , CAMEO (Conflict and Mediation Event Observation) (Gerner et al., 2002) , and several other databases for protest de-tection systems (Danilova, 2015) . These methods have focused on news data as they have traditionally been the most reliant source of events. Protest detection has been one of the major issues in the context of social and political (Ettinger et al., 2017) . Papanikolaou and Papageorgiou (2020) presented a computational social science methodology to analyse protests in Greece. constructed a corpus of protest events comprising various language sources from various countries. Several systems were submitted to the CLEF ProtestNews Track that consisted of three shared tasks, primarily aimed at identifying and extracting event information spanning to multiple countries (H\u00fcrriyetoglu et al., 2019b .", | |
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| "section": "Related Work", | |
| "sec_num": "2" | |
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| { | |
| "text": "This dataset comprises 26,208 sentences in three languages, namely English, Spanish, and Portuguese. The dataset consists of two classes:", | |
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| "sec_num": "3" | |
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| { | |
| "text": "\u2022 Event: The sentence indicates an event of the past.", | |
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| "text": "\u2022 Not-event: The sentence does not talk about any event.", | |
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| "text": "The volume of sequences indicating Not-event is higher in contrast to that of the Event label. Therefore, the dataset distribution is quite imbalanced. We can also notice that the number of English samples exceeds that of Spanish and Portuguese ones. Refer to ", | |
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| "text": "We used pretrained transformer-based models for identifying if a sentence talks about an event or not. The models that were used are BERT (Devlin et al., 2019) , RoBERTa (Liu et al., 2019) and DistilBERT (Sanh et al., 2019) . Even though there are 3 different languages, we used a single model for all three due to memory constraints and reduced training time. We fine-tuned these models for sequence classification. Soft Voting is done on all these models to produce the respective final outputs for the languages. In soft voting, each classifier predicts that a specific data point belongs to the particular target class. A weighted sum of the predictions is done based on the importance of the classifier (all models have equal weights). The overall prediction is chosen as the target with the greatest sum of the weighted probability, thus winning the vote (Beyeler, 2017; Hande et al., 2021) .", | |
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| "text": "Bidirectional Encoder Representations from Transformers (BERT) (Devlin et al., 2019) is a pretrained language model which was created with the objective that fine-tuning a pretrained model yields better performance. BERT's pretraining phase includes two tasks. Firstly, Masked Language Modeling (MLM) is where certain words are randomly masked in a sequence. About 15% of the words in a sequence is masked. The model then attempts to predict the masked words. Secondly, Next Sentence Prediction (NSP), where the model has an additional loss function, NSP loss, indicates if the second sequence follows the first one. Around 50% of the inputs are a pair, and they randomly chose the other 50. Here, we use a bert-base-multilingual-cased (Pires et al., ", | |
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| "text": "DistilBERT (Sanh et al., 2019) is the distilled version of BERT. DistilBERT employs a triple loss language modelling, where it integrates cosine distance loss with knowledge distillation. DistilBERT has 40% fewer parameters than BERT but still promises 97% of the latter's performance. It is also 60% faster than BERT. In this system, we used a cased multilingual DistilBERT model as they are three different languages. For our cause, we finetune distilbert-base-multilingual-cased, which is distilled from the mBERT checkpoint. The model has 6 layers, 768 dimensions, and 12 Attention heads, totalizing about 134 million parameters.", | |
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| "text": "Robustly Optimized BERT (RoBERTa) (Liu et al., 2019) follows the same architecture of BERT while differing in the pretraining strategy. It is pretrained with MLM as its objective where the model tries to predict the masked words. RoBERTa model is trained on the vast English Wikipedia and CC-News datasets. The NSP is not employed as a pretraining strategy, and the tokens are dynamically masked, making the model slightly different to BERT. During tokenization, RoBERTa follows byte-pair encoding (BPE) (Gall\u00e9, 2019) as opposed to WordPiece employed in BERT. We use robertabase, a pretrained language model consisting of 12 layers, 768 hidden, 12 attention heads, and 125 million parameters.", | |
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| "text": "For our system, we fine-tune the pretrained models discussed in Section 4.1, 4.2, and 4.3. We combine the three datasets as the number of samples for Spanish and Portuguese are quite low. After combining the models, we split the validation set accordingly, maintaining the split's ratio and tabulating the results on the concatenated dataset in Table3. The embeddings are extracted from these models to be fed as input to the LSTM layer, (Hochreiter and Schmidhuber, 1997) as shown in Figure1. The resulting output is fed into a global average pooling layer (Lin et al., 2014) and then passed into fully connected layers, followed by a sigmoid activation function to obtain the resulting probability score for the input sentences. The same parameters are used for all three models. A dropout layer (Srivastava et al., 2014) is also added in between the fully connected layers for regularization.", | |
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| "sec_num": "4.4" | |
| }, | |
| { | |
| "text": "Refer Table 4 for the parameters used in the model. ", | |
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| "text": "All pretrained language models are fine-tuned in Google Colab 2 for ten epochs. We use the Tensorflow implementation of the models 3 on the Huggingface transformers library 4 . We compare the macro F1-Scores of our fine-tuned models on the validation set, which were created by splitting the given dataset. The remaining split is the training data. The validation set contains samples from all three languages. It has 4,387 Not-event sequences and 963 Event sequences making a combined total of 5,350. The results are shown in Table3. We fine-tuned BERT, DistilBERT, and RoBERTa models on the training set. We have combined the Language Macro F1-Score English 0.8291 Spanish 0.7578 Portuguese 0.7951 Table 5 : Macro F1-Scores on the Test Set three language corpora into a single corpus comprising of all the three languages together. The main intention towards using a multilingual model is that the representations learnt during one language's pretraining would help the other. We can observe that DistilBERT achieved a better F1-Score among the models mentioned in the previous sections. RoBERTa gave the lowest score among these. The reason could be that the RoBERTa model was not multilingual, unlike the other two; however, it still managed to get a score very close to the BERT model. It is imperative that performing soft voting on all three models has managed to increase the score. One of the reasons for the poor performance of the models is the imbalance in the distribution of the classes. In the dataset, there are 21,794 Not-event sentences and only 4,954 Event ones. The models performed very well in the majority class and poorly in the minority class. Having more Event samples could have certainly helped the model in distinguishing better among the classes. Based on the performance of soft voting on the validation set, we have used the same for the test set. The results for the test set are shown in Table5. The reason for relatively low scores of Spanish and Portuguese could be due to the inadequate support of the training set (2,741 and 1,182) instead of English (22, 825) . We also believe that our approach of combining datasets could have influenced the performance of the low support datasets.", | |
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| "text": "English (22,", | |
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| "text": "Table 5", | |
| "ref_id": null | |
| } | |
| ], | |
| "eq_spans": [], | |
| "section": "Results and Analysis", | |
| "sec_num": "5" | |
| }, | |
| { | |
| "text": "The need to develop automated systems to detect any event is an active protest has constantly been increasing because of the escalation of social media users and several platforms to support them. In this paper, we have explored several multilingual language models to classify if a given sentence talks about an event that has happened (Event) or not (Not-event) in three languages. Our work primarily focuses on fine-tuning language models and feeding them to an architecture we created. We also observe that the problem of class imbalance has had a significant impact on the performance of the models. The soft voting approach has achieved macro F1-Scores of 0.8291, 0.7578, and 0.7951 for English, Spanish, and Portuguese, respectively. For future work, we intend to explore class weighting techniques and semi-supervised approaches to improve our performance.", | |
| "cite_spans": [], | |
| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "Conclusion", | |
| "sec_num": "6" | |
| }, | |
| { | |
| "text": "https://colab.research.google.com/ 3 https://huggingface.co/transformers/pretrained models.html 4 https://huggingface.co/", | |
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| "ref_spans": [], | |
| "eq_spans": [], | |
| "section": "", | |
| "sec_num": null | |
| } | |
| ], | |
| "back_matter": [], | |
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| "FIGREF0": { | |
| "text": "3: Precision (P), recall (R), and F1-Score of the models on the validation set; M(P), M(R), and M(F1) are the Macro averages of precision, recall, and F1-Score respectively", | |
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| "TABREF1": { | |
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| "text": "Examples of the dataset indicating events of the past and not-events.", | |
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| "TABREF2": { | |
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| "text": "System Architecture based on BERT(Devlin et al., 2019)", | |
| "content": "<table><tr><td/><td/><td/><td/><td/><td>Dense</td></tr><tr><td/><td/><td/><td/><td>Event</td><td>Not-event</td><td>Layer(1 Neuron)</td></tr><tr><td/><td/><td/><td/><td>Dropout</td></tr><tr><td/><td/><td/><td/><td>.....</td></tr><tr><td/><td>Dense</td><td/><td/><td/></tr><tr><td/><td>Layer(128</td><td/><td/><td/></tr><tr><td/><td>Neurons)</td><td/><td/><td/></tr><tr><td/><td/><td/><td/><td colspan=\"2\">Global Average Pooling</td></tr><tr><td/><td/><td/><td/><td/><td>.....</td></tr><tr><td/><td>C</td><td/><td>T1</td><td>T2</td><td>TN</td></tr><tr><td/><td/><td/><td/><td>Pretrained</td></tr><tr><td/><td/><td/><td/><td>Language</td></tr><tr><td/><td/><td/><td/><td>Model</td><td>.....</td></tr><tr><td/><td colspan=\"2\">E[CLS]</td><td>E1</td><td>E2</td><td>.....</td><td>EN</td></tr><tr><td/><td>[CLS]</td><td/><td>Tok 1</td><td>Tok 2</td><td>.....</td><td>Tok N</td></tr><tr><td colspan=\"5\">Figure 1: Language English Spanish Portuguese</td></tr><tr><td>Not-event</td><td>18,602</td><td>2,291</td><td/><td>901</td></tr><tr><td>Event</td><td>4,223</td><td>450</td><td/><td>281</td></tr><tr><td>Total</td><td>22,285</td><td>2,741</td><td/><td>1,182</td></tr></table>", | |
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| "text": ".917 0.922 0.641 0.675 0.658 0.873 0.784 0.796 0.790 DistilmBERT 0.924 0.947 0.936 0.729 0.646 0.685 0.893 0.827 0.797 0.810 RoBERTa 0.910 0.938 0.924 0.670 0.578 0.621 0.873 0.790 0.758 0.772 SoftVoting 0.937 0.939 0.938 0.720 0.713 0.717 0.899 0.829 0.826 0.827", | |
| "content": "<table><tr><td/><td colspan=\"2\">Event</td><td/><td/><td>Not-event</td><td/><td>Overall</td></tr><tr><td>Model</td><td>P</td><td>R</td><td>F1</td><td>P</td><td>R</td><td>F1</td><td>Acc M(P) M(R) M(F1)</td></tr><tr><td>mBERT</td><td>0.928 0</td><td/><td/><td/><td/><td/><td/></tr><tr><td/><td/><td/><td/><td/><td/><td/><td>2019)</td></tr><tr><td/><td/><td/><td/><td/><td colspan=\"3\">trained on top of 104 languages in the largest</td></tr><tr><td/><td/><td/><td/><td/><td colspan=\"3\">Wikipedia corpus. This model has 12 layers, 12</td></tr><tr><td/><td/><td/><td/><td/><td colspan=\"3\">Attention heads with over 179 million parameters.</td></tr></table>", | |
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