PLM-AS: Pre-trained Language Models Augmented with Scanpaths for Sentiment Classification
Duo Yang, Nora Hollenstein
Abstract
Recent research demonstrated that deep neural networks could generate meaningful feature representations from both eye-tracking data and sentences without designing handcrafted features, which achieved competitive performance across cognitive NLP tasks, such as sentiment classification over gaze datasets, but the previous works mainly encode the text and gaze data separately without considering the interaction between these two modalities or applying large-scaled pre-trained models. To address these challenges, we introduce PLM-AS, a novel framework to take full advantage of textual and eye-tracking features by sequence modeling in a highly interactive way for multimodal fusion. It is also the first attempt to combine large-scaled pre-trained models with eye-tracking features in the cognitive reading task. We show that PLM-AS captures cognitive signals from eye-tracking data and shows improved performance on sentiment classification within and across three datasets of different domains.