How direct is the link between words and images?

Abstract

Current word embedding models despite their success, still suffer from their lack of grounding in the real world. In this line of research, Gunther et al. 2022 proposed a behavioral experiment to investigate the relationship between words and images. In their setup, participants were presented with a target noun and a pair of images, one chosen by their model and another chosen randomly. Participants were asked to select the image that best matched the target noun. In most cases, participants preferred the image selected by the model. Gunther et al., therefore, concluded the possibility of a direct link between words and embodied experience. We took their experiment as a point of departure and addressed the following questions. 1. Apart from utilizing visually embodied simulation of given images, what other strategies might subjects have used to solve this task? To what extent does this setup rely on visual information from images? Can it be solved using purely textual representations? 2. Do current visually grounded embeddings explain subjects’ selection behavior better than textual embeddings? 3. Does visual grounding improve the semantic representations of both concrete and abstract words? To address these questions, we designed novel experiments by using pre-trained textual and visually grounded word embeddings. Our experiments reveal that subjects’ selection behavior is explained to a large extent based on purely text-based embeddings and word-based similarities, suggesting a minor involvement of active embodied experiences. Visually grounded embeddings offered modest advantages over textual embeddings only in certain cases. These findings indicate that the experiment by Gunther et al. may not be well suited for tapping into the perceptual experience of participants, and therefore the extent to which it measures visually grounded knowledge is unclear.

Hassan Shahmohammadi

PhD candidate at University of Tuebingen & IMPRS-IS

My research interests include Multi-Modal learning using deep learning bridging NLP and CV