Unsupervised Multimodal Language Representations using Convolutional Autoencoders

Abstract

Multimodal Language Analysis is a demanding area of research, since it is associated with two requirements: combining different modalities and capturing temporal information. During the last years, several works have been proposed in the area, mostly centered around supervised learning in downstream tasks. In this paper we propose extracting unsupervised Multimodal Language representations that are universal and can be applied to different tasks. Towards this end, we map the word-level aligned multimodal sequences to 2-D matrices and then use Convolutional Autoencoders to learn embeddings by combining multiple datasets. Extensive experimentation on Sentiment Analysis (MOSEI) and Emotion Recognition (IEMOCAP) indicate that the learned representations can achieve near-state-of-the-art performance with just the use of a Logistic Regression algorithm for downstream classification. It is also shown that our method is extremely lightweight and can be easily generalized to other tasks and unseen data with small performance drop and almost the same number of parameters. The proposed multimodal representation models are open-sourced and will help grow the applicability of Multimodal Language.

Face-to-Face Co-Located Human-Human Social Interaction Analysis using Nonverbal Cues: A Survey

“…multimodal emotion recognition [63,101], and sentiment analysis [101]. Notice that the last two domains both use nonverbal signals to make decisions and the effectiveness of unsupervised pre-training in [63,101,151] was on par with or even better than the several fully-supervised SOTA.…”

“…multimodal emotion recognition [63,101], and sentiment analysis [101]. Notice that the last two domains both use nonverbal signals to make decisions and the effectiveness of unsupervised pre-training in [63,101,151] was on par with or even better than the several fully-supervised SOTA. Unfortunately, unsupervised pre-training has not been yet integrated into the face-to-face co-located human-human social interaction analysis.…”