Multimodal Embeddings from Language Models
This work addresses the need for better multimodal representations in natural language processing, particularly for emotion recognition, though it is incremental as it builds on existing language models.
The authors tackled the problem of enhancing word embeddings by integrating acoustic information into a pretrained bidirectional language model, resulting in multimodal embeddings that improved state-of-the-art performance in emotion recognition on the CMU-MOSEI dataset.
Word embeddings such as ELMo have recently been shown to model word semantics with greater efficacy through contextualized learning on large-scale language corpora, resulting in significant improvement in state of the art across many natural language tasks. In this work we integrate acoustic information into contextualized lexical embeddings through the addition of multimodal inputs to a pretrained bidirectional language model. The language model is trained on spoken language that includes text and audio modalities. The resulting representations from this model are multimodal and contain paralinguistic information which can modify word meanings and provide affective information. We show that these multimodal embeddings can be used to improve over previous state of the art multimodal models in emotion recognition on the CMU-MOSEI dataset.