U niversity of H ildesheim at S em E val-2022 task 5: Combining Deep Text and Image Models for Multimedia Misogyny Detection (2022)

Abstract: This paper describes the participation of the University of Hildesheim at the SemEval task 5. The task deals with Multimedia Automatic Misogyny Identification (MAMI). Hateful memes need to be detected within a data collection. For this task, we implemented six models for text and image analysis and tested the effectiveness of their combinations. A fusion system implements a multi-modal transformer to integrate the embeddings of these models. The best performing models included BERT for the text of the meme, manually derived associations for words in the memes and a Faster R-CNN network for the image. We evaluated the performance of our approach also with the data of the Facebook Hateful Memes challenge in order to analyze the generalisation capabilities of the approach.

## introduction hate in social media continues to be a societal problem. the identification of problematic content based on text has made progress, but the performance is still not satisfying @xcite . visual content and multi-modal construction on semantics is a reality in social media today @xcite . systems for realistic scenarios in social media platforms (e.g. @xcite require image processing @xcite . the multimedia automatic misogyny identification (mami) challenge (semeval-2022 task 5) is addressing this problem @xcite . mami provides a testbed for algorithms which are capable of processing text and image of memes in one system. the experiments described in this paper measure the effectiveness of different models and their combination into a fusion system. we implemented a basic text classifier based on bert and an image processing system based on the faster r-cnn network. in addition, the generalization capabilities between collections are tested. we conducted the experiments with the mami dataset as well as with the dataset provided by the facebook memes challenge @xcite . ## previous work the detection of hate speech can be considered part of natural language processing. current research is driven by benchmark data and deep learning algorithms have shown to provide best performance. data sets such as offensive language detection in spanish variants (meoffendes@iberlef 2021) @xcite and detection of toxicity in comments in spanish (detoxis) @xcite focus on general concepts of offensive content while other data sets are dedicated to more specific topics than general offensive content. the semeval 2019 task-5 @xcite focused on the detection of hate speech against immigrants and women in spanish and english messages extracted from twitter. besides the main binary task to detect hate speech, there was a fine grained task to further classify into aggressive attitude and the target harassed, to distinguish whether a message contains incitement against an individual rather than a group. the best performing system @xcite trained a svm model with a rbf kernel using google's universal sentence encoder @xcite as features. the shared task hasoc @xcite ) created a large multilingual dataset for hate speech identification. the first hasoc track focused on the identification of hate speech in indo-european languages (hindi, english and german). hasoc introduced a binary classification into problematic content and other content. while most data sets include english data, several recent shared tasks have created new collections for other languages such as greek @xcite and turkish @xcite . spe-cific forms of hate speech based on the targeted groups have also been analyzed automatically. for this work, the detection of misogyny is especially relevant. in the automatic misogyny identification (ami) task at evalita, a twitter collection of misogynous messages was assembled. overall, 10.000 tweets were available and classified into misogynous and non-misogynous tweets. they were also further analyzed into more fine-grained classes @xcite . the second edition of the automatic misogyny identification (ami) task in 2020 followed up on binary classification. it also included the prediction of aggressiveness as a binary concept for the misogynous tweets and provided a subtask for the analysis of bias in the models @xcite . multi-modal processing of text and image simultaneously has made great progress recently. there are approaches for late fusion which first analyze image and text and combine the representations. early fusion systems process both text and image in parallel in order to benefit from the dependencies. systems like im-agebert @xcite and uniter @xcite have achieved promising results. uniter relates text and image parts to one another and tries to capture their interaction. ## multimedia datasets the data for the semeval-2022 task 5 (multimedia automatic misogyny identification, mami) is described in the overview paper @xcite . the system presented here is aiming at a binary classification. it did not use the fine-grained classification on kinds of misogyny. in addition and in order to observe how well the multimodal system generalizes over similar datasets from similar tasks, we also processed the facebook hateful meme challenge (hm) dataset @xcite . this dataset provides examples for hateful memes in general and includes also other kinds of problematic content than misogyny. however, because the tasks are related a system might also work across these two datasets. ## system description our system includes six single models. they were all tested as classifiers and we explored several figure 2: examples for associations from the knowledge graph combinations in the experiments. the first system is processing the text sequence associated with the meme @xcite . we used the the model bert-base-uncased foot_0 . it creates a transformer based presentation of the text. the principle of bert is illustrated in figure 1 . ## experiments for combining the single classifiers, they are fed into a fusion system. for that processing step, a transformer is used. after a layer normalization @xcite , all embedding values are concatenated and fed into a transformer. a sigmoid function is used for the final prediction. first, the models were tested individually. then, combinations were tested. for finding the optimal fusion of the classifiers above, we applied sequential forward selection (sfs) and sequential backward elimination (sbe). for all experiments, the models were fully trained. learning rates were adapted for each underlying model so that models converging faster did not overfit. models with larger embeddings were assigned a higher dropout in the fusion system. for sfs, all systems were tested individually first and the best system is used as the first component of the combined system. afterwards, sfs iteratively adds further components. in each iteration, the current version of the combined system is extended by each individual system that is not part of the combined system yet. the combination that leads to the best improvement is taken as the new best combination. sfs converges, when no further improvement can be accomplished. sbe works similar to sfs but starts with a combination of all systems and iteratively detaches individual systems from the combined system. the submitted result was assigned the team name milan_kalkenings. ## results and discussion the main results refer to the training on the two datasets. furthermore, we used the two datasets for training and testing respectively. these crossdataset experiments are reported in the subsequent section. ## conclusion the experiments have shown that the identification of hateful memes is still a challenging problem. in our experiments, text features are the most beneficial ones for the system. the influence of associations in particular needs to be further analyzed. first analysis seems to suggest that the number of found associations has a correlation with the performance for the problematic class. the performance across the two datasets is not optimal. further datasets are needed to analyze the generalization across different collections. in future work, we intend to analyze the impact of each system for the fusion in more detail. we also plan to experiment whether training with a misogyny text dataset can be beneficial for a multimodal system.