UPB at S em E val-2022 Task 5: Enhancing UNITER with Image Sentiment and Graph Convolutional Networks for Multimedia Automatic Misogyny Identification (2022)

Abstract: In recent times, the detection of hate-speech, offensive, or abusive language in online media has become an important topic in NLP research due to the exponential growth of social media and the propagation of such messages, as well as their impact. Misogyny detection, even though it plays an important part in hate-speech detection, has not received the same attention. In this paper, we describe our classification systems submitted to the SemEval-2022 Task 5: MAMI - Multimedia Automatic Misogyny Identification. The shared task aimed to identify misogynous content in a multi-modal setting by analysing meme images together with their textual captions. To this end, we propose two models based on the pre-trained UNITER model, one enhanced with an image sentiment classifier, whereas the second leverages a Vocabulary Graph Convolutional Network (VGCN). Additionally, we explore an ensemble using the aforementioned models. Our best model reaches an F1-score of 71.4% in Sub-task A and 67.3% for Sub-task B positioning our team in the upper third of the leaderboard. We release the code and experiments for our models on GitHub.

## introduction the web and social network platforms, in particular, have become a significant part of our modern social lives. sharing information, opinions, news, and jokes through these platforms with friends and family are now daily routines. one of the most prevalent forms of jokes in social networks are memes. internet memes are small cultural units that are transformed, mixed, and shared using online platforms, often spreading in a viral manner @xcite . a prolific part are image-based memes, often available as templates that are accompanied by humorous or witty text. unfortunately, a considerable proportion of the memes shared by internet users are offensive or even hateful messages foot_1 . detecting hate and offensive speech is a significant task for any online platform. not only companies have this legal obligation in most countries, but also such language establishes a toxic environment that is detrimental to any online community on the long run. hate speech can take multiple forms, but it is most frequently encountered as a disparaging message on the basis of a characteristic as race, gender, religion, and other criteria; misogyny is one such frequent form specific to meme culture @xcite . detecting hate speech is often a hard problem, even in an unimodal setting since the message often relies on the context, addresses current events, and incorporates cultural knowledge that cannot be easily incorporated into an automated model. the multi-modality of internet memes increases the difficulty of the task since many memes can have a seemingly benign text that, contextualized with the associated image, becomes offensive or hateful. multi-modal hate speech detection had less attention in the research literature than traditional text-only methods. in the past two years, several datasets and challenges have addressed this by proposing detection tasks on meme-based data @xcite @xcite . misogyny detection, as a subgroup of hate speech detection tasks, has also been more frequently encountered in research in recent years. the series of automatic misogyny identification tasks proposed at ibereval 2018, evalita 2018, evalita 2020, and trac-2020 @xcite @xcite focused on the classification of tweets in english, spanish, italian, bangla, and hindi languages. in these tasks, researchers identified misogynous tweets and classified them as aggressive/nonaggressive or active/passive. semeval-2022 task 5: mami -multimedia automatic misogyny identification @xcite is a multi-modal classification task, aiming to detect misogynous memes by leveraging both image and text information. the task includes two sub-tasks: a binary identification of misogynous/non-misogynous memes (sub-task a), and a multi-label classification distinguishing between the types of misogyny, namely: stereotype, shaming, objectification, and violence (sub-task b). in this paper, we present our contribution to this task by proposing two architectures based on the pre-trained multimodal uniter (universal image-text representation) model @xcite , as well as an ensemble from these two models. uniter is an early fusion model pre-trained on large text-image datasets. uniter leverages visual and location features extracted with faster r-cnn @xcite , together with wordpiece encodings @xcite derived from text tokens using a transformer-based model @xcite . uniter learns a generalizing representation for the text-image context by bringing the visual and text representations in a common embedding space. we use uniter at the core of our two architectures: the first one enhances the pre-trained model with image sentiment features using a vgg-19 model @xcite , while the second leverages graph convolutions on a co-occurrence graph (kipf and welling, 2016) built from an external dataset. ## background multi-modal tasks were traditionally associated with visual question answering @xcite , image captioning @xcite , audiovisual speech recognition @xcite , or cross-modal retrieval @xcite . with success of competitions like the hateful memes challenge @xcite , more research focused on multi-modal offensive classification. pre-trained transformer models such as vilbert @xcite , visualbert @xcite , lxmert @xcite , oscar @xcite , and others, dominated the competition leaderboard, either as stand-alone models or in large ensembles. while considering uniter, @xcite used an ensemble that placed them in the top 5 teams. ## method we explored two ways of enhancing uniter, first by adding a unimodal late fusion with a visual sentiment classifier, and second by using a multimodal early fusion with a modified vocabulary graph convolutional network (vgcn) @xcite . ## results while considering the two proposed data preprocessing techniques, removing the websites from the textual information proved to decrease the performance of the models. even though removing the source from the texts would provide a better generalization in a production setting, this information turned out to be a clue on misogyny content in this dataset. compensating for the unbalanced classes with oversampling from the minority classes proved to be less impactful than weighting the loss of the positive classes. also, we noticed a strong tendency to overfit during our experiments. a common used mitigation technique is to augment the training data with small variations -e.g., data augmentation using similar words in the embedding space @xcite , as well as simpler replacement, swap, and deletion methods @xcite . none of the applied augmentation methods improved performance. we thus hypothesize that although these augmentations create similar meanings, innuendos get lost. for example, the phrase "her: excuse me, i'm trying to put a load in the tables 2 and 3 are the best performances on the official competition test set for the models we trained. all modes were trained using the hyperparameters specified in the previous section. even though the performance on the binary classification from sub-task a was comparable with the best single model (uniter-vgcn), it was slightly lower. as seen in figure 3 , our best system -the ensemble model -has the tendency to over-predict misogyny. some erroneous predictions were driven by their aggressive language, for instance "if you're dating my daughter and your stupid enough to do this i'm going to kill you!" depicting also that a domestic abuse victim was understandably detected as misogynous type "violence". records 15566 and 15311 depict a face close up of a famous woman and were incorrectly labeled as misogynous, even if the text is replaced with a neutral or even positive one like "woman" or "best". this can be explained by the off-balance in the training data where the visual object "woman" is detected 2,262 times in the misogynous entries, and only 639 times in nonmisogynous memes; similarly, "eyebrows" are four times more likely to appear in the misogynous class. however, memes like "when you know it's a trap but you can't wait to take the bait" that depict a woman body are not detected as misogyny since these memes require additional background knowledge in order to understand the real intent. ## conclusion in this paper, we describe the architectures used in our submission at semeval-2022, task 5: mami -multimedia automatic misogyny identification. our proposed models took the pre-trained uniter-base and uniter-large models and enhanced them with image sentiment features or, by using a gcn, with additional domain information from an external dataset. our best model achieved an f1-score of 71.4 for sub-task a and 67.3 in sub-task b, seen in in terms of future work, we plan to continue our research into the automatic detection of abusive and hateful online content, and extend the experiments onto other pre-trained multi-modal models, as well as to attempt to improve their performance through task-adaptive pre-training @xcite . even though internet memes provide an interesting combination of textual message and image, they represent only one medium that can spread toxic messages. studying other modalities like video or audio would help widen the understanding on how to detect and limit the spread of these undesired messages.