Multimodal Conversation Modelling for Topic Derailment Detection (2022)

Abstract: Conversations on social media tend to go off-topic and turn into different and sometimes toxic exchanges. Previous work focuses on analysing textual dialogues that have derailed into toxic content, but the range of derailment types is much broader, including spam or bot content, tangential comments, etc. In addition, existing work disregards conversations that involve visual information (i.e. images or videos), which are prevalent on most platforms. In this paper, we take a broader view of conversation derailment and propose a new challenge: detecting derailment based on the “change of conversation topic”, where the topic is defined by an initial post containing both a text and an image. For that, we (i) create the first Multimodal Conversation Derailment (MCD) dataset, and (ii) introduce a new multimodal conversational architecture (MMConv) that utilises visual and conversational contexts to classify comments for derailment. Experiments show that MMConv substantially outperforms previous text-based approaches to detect conversation derailment, as well as general multimodal classifiers. MMConv is also more robust to textual noise, since it relies on richer contextual information.

## introduction online conversations on social media can easily go off-topic @xcite and result in divergent and even harmful exchanges, ranging from off-topic discussions to spam information and personal attacks. work in this field has focused on detecting or forecasting conversations that derail into toxic content @xcite @xcite . however, such work is limited in two ways: 1) derailment is not limited to toxic comments or personal attacks; it can include i love our pediatrician's shirt today if i was a pediatrician i would purposely tweak anti-vaxxers. whatever i could do to get them to come into the light so i could fire them as patients. this is exactly one of the reasons why she wears it. i complimented the shirt, asked if i could take a photo, and then she told me about how many people take issue with the shirt. this office immediately fires patients if they find out they're not up to date on vaccines. is that actually the correct term? "fire patients"? i know it but i just want to know if that's actually what you would say. ## model in this section, we introduce the architecture of our proposed mmconv model, which consists of a pre-trained text encoder, a visual encoder, a context encoder, and a late fusion component. figure 4 gives an overview of the model architecture. pre-trained text encoder we use a pre-trained bert @xcite model as our text encoder, but note that any other pre-trained language model can be used. given an input text sequence ω = [ω 1 , ω 2 , ..., ω k ], bert encodes the input into a sequence of vectors @xmath0, e 2 , ..., e k ]. for each utterance u i and the target text x, we use the first hidden vector (i.e. the [cls] token representation) as the sentence representation s u @xmath1 visual encoder we use an image object detector as our visual encoder. given an input image i, the visual encoder detects local regions in the image as object proposals and extract m pooled visual features @xmath2, o 2 , ..., o m ] for each object region proposal. an mlp layer with gelu activation function @xcite is used to map the image features into the same space as the text, thus generating the visual hidden vectors @xmath3, v 2 , ..., v m ]: given that both title and image form the initial topic of a conversation, we concatenate the title token vectors e t with the visual hidden vectors v and fuse them using a self-attention layer @xcite to generate a multimodal title-image hidden vector h mm (the title [cls] position is used for the hidden vector): context encoder to better model the chronological order in the sequence of utterances, we use a 1-layer uni-directional gru network @xcite as our context encoder. given the sentence representations for each utterance [s u 1 , s u 2 , ..., s un ], the context encoder iteratively computes the context hidden state h u i up to the i-th utterance: late fusion finally, a self-attention layer is used to fuse the multimodal title-image hidden vector h mm , the utterances sequence hidden vector h un , and the target text representation s x , generating a vector h conv encoding the whole conversation. the three hidden vectors are averaged after the selfattention layer, then fed to an mlp classifier to make the final prediction: ## robustness analysis since the main application of dialogue derailment detection is automatic moderation on social platforms, where the user-generated texts can be noisy, it is important to evaluate the model's robustness to textual noise, e.g. typos, code-switching, etc. @xcite evaluate on test data injected with noise and show that multimodal models are more robust to textual noise than unimodal models. following their settings, we select 7 types of common textual noise (details in appendix e) from nl-augmenter @xcite and apply noise injection to the test set. we evaluate under two settings: noise injected to both title and target text, and a more realistic setting where noise is injected to all textual inputs, including title, previous utterances, and the target text. the models' f1 scores on different proportion of noise are shown in figure 5 . when noise is only injected to title and text, all models show robustness against a small proportion (less than 5%). however, when the noise percentage reaches 15%, all models exhibit larger performance drops while the performance of mmconv is more stable and clearly shows less performance drop over the others, which indicates that the benefits from using image and context are consistent and can help robustness. when noise is injected to all textual inputs, similar performance drops is observed for all conversational models. comparing the performance with and without noise injected to previous utterances, we notice that the difference within each model type is small, showing that the models are robust to the noise in previous utterances. note that the four conversational models use a vector to encode the whole sequence of utterances rather than each utterance separately, so they are less affected by noise in individual utterances. ## conclusions in this paper, we explore the task of detecting online conversation derailment. we take a broader view of derailment and focus on conversation derailment based on topic changes. we create a new multimodal conversation derailment (mcd) dataset, which introduces multimodality into conversation derailment detection. furthermore, we propose a novel hierarchical architecture -mm-conv -that uses visual and conversational contexts. our experiments show that the proposed model outperforms strong baselines in conversation derailment detection. finally, we perform a robustness analysis by evaluating models with noisy texts. we find that models using visual and conversational information have increased robustness, which can help build more accurate practical moderation applications. future work in this direction could involve extending the size of the mcd dataset to include more topics in texts/images, building pre-trained multimodal conversational models for the multimodal derailment detection task, and applying distillation to mmconv model to reduce model size.