Multimodal Routing: Improving Local and Global Interpretability of Multimodal Language Analysis (2020)

Abstract: The human language can be expressed through multiple sources of information known as modalities, including tones of voice, facial gestures, and spoken language. Recent multimodal learning with strong performances on human-centric tasks such as sentiment analysis and emotion recognition are often black-box, with very limited interpretability. In this paper we propose, which dynamically adjusts weights between input modalities and output representations differently for each input sample. Multimodal routing can identify relative importance of both individual modalities and cross-modality factors. Moreover, the weight assignment by routing allows us to interpret modality-prediction relationships not only globally (i.e. general trends over the whole dataset), but also locally for each single input sample, meanwhile keeping competitive performance compared to state-of-the-art methods.

## introduction the human language contains multimodal cues, including textual (e.g., spoken or written words), visual (e.g., body gestures), and acoustic (e.g., voice tones) modalities. it acts as a medium for human communication and has been advanced in areas spanning affect recognition @xcite , media description @xcite , and multimedia information retrieval @xcite . modeling multimodal sources requires to understand the relative importance of not only each single modality (defined as unimodal explanatory features) but also the interactions (defined as bimodal or trimodal explanatory features) @xcite . recent work @xcite @xcite proposed methods to fuse information across modalities and yielded superior performance, but these models are often black-box with very limited interpretability. interpretability matters. it allows us to identify crucial explanatory features for predictions. such interpretability knowledge could be used to provide insights into multimodal learning, improve the model design, or debug a dataset. this inerpretability is useful at two levels: the global and the local level. the global interpretation reflects the general (averaged) trends of explanatory feature importance over the whole dataset. the local interpretation is arguably harder but can give a highresolution insight of feature importance specifically depending on each individual samples during training and inference. these two levels of interpretability should provide us an understanding of unimodal, bimodal and trimodal explanatory features. ## related work multimodal language learning is based on the fact that human integrates multiple sources such as acoustic, textual, and visual information to learn language @xcite @xcite . recent ad-vances in modeling multimodal language using deep neural networks are not interpretable @xcite . linear method like the generalized additive models (gams) @xcite do not offer local interpretability. even though we could use post hoc (interpret predictions given an arbitrary model) methods such as lime @xcite , shap (lundberg and lee, 2017), and l2x @xcite to interpret these black-box models, these interpretation methods are designed to detect the contributions only from unimodal features but not bimodal or trimodal explanatory features. it is shown that in human communication, modality interactions are more important than individual modalities @xcite . two recent methods, graph-mfn @xcite and multimodal factorized model (mfm) @xcite , attempted to interpret relationships between modality interactions and learning for human language. nonetheless, graph-mfn did not separate the contributions among unimodal and multimodal explanatory features, and mfm only provided the analysis on trimodal interaction feature. both of them cannot interpret how both single modality and modality interactions contribute to final prediction at the same time. our method is inspired and related to capsule networks @xcite , which performs routing between layers of capsules. each capsule is a group of neurons that encapsulates spatial information as well as the probability of an object being present. on the other hand, our method performs routing between multimodal features (i.e., unimodal, bimodal, and trimodal explanatory features) and concepts of the model's decision. ## method the proposed multimodal routing contains three stages shown in fig. 2 : encoding, routing, and prediction. the encoding stage encodes raw inputs (speech, text, and visual data) to unimodal, bimodal, and trimodal features. the routing stage contains a routing mechanism @xcite , which 1) updates some hidden representations and 2) adjusts local weights between each feature and each hidden representation by pairwise similarity. following previous work @xcite , we call the hidden representations "concepts", and each of them is associated to specific a prediction label (in our case sentiment or an emotion). finally, the prediction stage takes the inferred concepts to perform model prediction. ## experiments in this section, we first provide details of experiments we perform and comparison between our proposed model and state-of-the-art (sota) method, as well as baseline models. we include interpretability analysis in the next section. ## interpretation analysis in this section, we revisit our initial research question: how to locally identify the importance or contribution of unimodal features and the bimodal or trimodal interactions? we provide examples in this section on how multimodal routing can be used to see the variation of contributions. we first present the local interpretation and then the global interpretation. ## conclusion in this paper, we presented multimodal routing to identify the contributions from unimodal, bimodal and trimodal explanatory features to predictions in a locally manner. for each specific input, our method dynamically associates an explanatory feature with a prediction if the feature explains the prediction well. then, we interpret our approach by analyzing the routing coefficients, showing great variation of feature importance in different samples. we also conduct global interpretation over the whole datasets, and show that the acoustic features are crucial for predicting negative sentiment or emotions, and the acoustic-visual interactions are crucial for predicting emotion angry. these observations align with prior work in psychological research. the advantage of both local and global interpretation is achieved without much loss of performance compared to the sota methods. we believe that this work sheds light on the advantages of understanding human behaviors from a multimodal perspective, and makes a step towards introducing more interpretable multimodal language models.