Strong and Simple Baselines for Multimodal Utterance Embeddings
Table 1: Results for multimodal personality trait recognition on POM (left) and multimodal sentiment analysis on CMU-MOSI (right). EF-LSTM(⋆)and HCRF(⋆) denote the best result obtained from the LSTM and HCRF variants respectively. The top two results are highlighted in bold. Our proposed baseline model (MMB2), despite its simplicity, often ranks in the top two models and outperforms many large neural models such as C-MKL, DF, SAL-CNN, EF-LSTM, MV-LSTM, BC-LSTM, TFN, and MFN.
['Dataset', '[BOLD] POM Personality Trait Recognition, measured in  [ITALIC] r', '[BOLD] POM Personality Trait Recognition, measured in  [ITALIC] r', '[BOLD] POM Personality Trait Recognition, measured in  [ITALIC] r', '[BOLD] POM Personality Trait Recognition, measured in  [ITALIC] r', '[BOLD] POM Personality Trait Recognition, measured in  [ITALIC] r', '[BOLD] POM Personality Trait Recognition, measured in  [ITALIC] r', '[BOLD] POM Personality Trait Recognition, measured in  [ITALIC] r', '[BOLD] POM Personality Trait Recognition, measured in  [ITALIC] r', '[BOLD] POM Personality Trait Recognition, measured in  [ITALIC] r', '[BOLD] POM Personality Trait Recognition, measured in  [ITALIC] r', '[BOLD] POM Personality Trait Recognition, measured in  [ITALIC] r']
Our first set of experiments evaluates the performance of our baselines on two multimodal prediction tasks: multimodal sentiment analysis on CMU-MOSI and multimodal speaker traits recognition on POM. (TFN) Zadeh et al. We view this as an impressive achievement considering the simplicity of our model and the significantly fewer parameters that our model contains. As we will later show, our model’s strong performance comes with the additional benefit of being significantly faster than the existing models.