Couples Behavior Modeling and Annotation Using Low-Resource LSTM Language Models

Observational studies on couple interactions are often based on manual annotations of a set of behavior codes. Such annotations are expensive, time-consuming, and often suffer from low inter-annotator agreement. In previous studies it has been shown that the lexical channels contain sufficient information for capturing behavior and predicting the interaction labels, and various automated processes using language models have been proposed. However, current methods are restricted to a small context window due to the difficulty of training language models with limited data as well as the lack of frame-level labels. In this paper we investigate the application of recurrent neural networks for capturing behavior trajectories through larger context windows. We solve the issue of data sparsity and improve robustness by introducing out-of-doniain knowledge through pretrained word representations. Finally, we show that our system can accurately estimate true rating values of couples interactions using a fusion of the frame-level behavior trajectories. The ratings predicted by our proposed system achieve inter-annotator agreements comparable to those of trained human annotators. Importantly, our system promises robust handling of out of domain data, exploitation of longer context, on-line feedback with continuous labels and easy fusion with other modalities.