Text to Dialog: Using Semantic Similarity to Extend Narrative Immersion in Virtual Worlds

Our objective is to create an expressive language interface that allows human participants to have agency in narrative-driven virtual worlds. Text to Dialog (TTD) gives narrative designers an opportunity to paint audience participants into a story universe utilizing semantic similarity. To do this, we apply the Universal Sentence Encoder by using embedding vectors that specifically target transfer learning to story-dialog related NLP tasks. We conclude that building expressive tools like TTD could enable new artistic experiences through (1) Semantic Dialect Matching, where human-generated textual statements are semantically matched with a pre-scripted list of dialog (from an avatar's dialect, voice, or way of speaking), and (2) Semantic Dialog Selection, where natural language can maneuver decision points through semantic matching. We reference two case-studies to demonstrate each use-case.

• Machine Intelligence

• Natural Language Processing