Like a Baby: Visually Situated Neural Language Acquisition
Alexander G. Ororbia, Ankur Mali, Matthew Kelly, David Reitter · 2019
We examine the benefits of visual context in training neural language models to perform next-word prediction.A multi-modal neural architecture is introduced that outperform its equivalent trained on language alone with a 2% decrease in perplexity, even when no visual context is available at test.Fine-tuning the embeddings of a pre-trained state-of-theart bidirectional language model (BERT) in the language modeling framework yields a 3.5% improvement.The advantage for training with visual context when testing without is robust across different languages (English, German and Spanish) and different models (GRU, LSTM, ∆-RNN, as well as those that use BERT embeddings).Thus, language models perform better when they learn like a baby, i.e, in a multi-modal environment.This finding is compatible with the theory of situated cognition: language is inseparable from its physical context.