Neuroevolution Strategies for Word Embedding Adaptation in Text Adventure Games

Word embeddings have gained popularity in many Natural Language Processing (NLP) tasks. They can encode general semantic relationship between words, and hence provide benefits in many downstream tasks when used as a knowledge base. However, they still suffer from morphological ambiguity as the trained vectors do not share representations at the subword level. In this report, we propose a new architecture that uses neuroevolution to fine-tune pre-trained word embeddings for the challenging task of playing text-based games. We fit this architecture into an existing game agent and evaluate its performance on six text-based games. Experimental results show that the proposed fine-tuning architecture may significantly mitigate the effect of morphological ambiguity to enable our game agent to reduce the total number of steps required to generate valid actions and perform well in these games.