Interactive Text Generation
This addresses the challenge of making text generation models more adaptable to user interactions, which is incremental as it builds on existing generation methods by adding interactivity.
The paper tackles the problem of training machine learning models for text generation in interactive settings by introducing a new Interactive Text Generation task that uses user simulators to provide edits, showing that interactively trained models outperform non-interactive counterparts even with the same input budget.
Users interact with text, image, code, or other editors on a daily basis. However, machine learning models are rarely trained in the settings that reflect the interactivity between users and their editor. This is understandable as training AI models with real users is not only slow and costly, but what these models learn may be specific to user interface design choices. Unfortunately, this means most of the research on text, code, and image generation has focused on non-interactive settings, whereby the model is expected to get everything right without accounting for any input from a user who may be willing to help. We introduce a new Interactive Text Generation task that allows training generation models interactively without the costs of involving real users, by using user simulators that provide edits that guide the model towards a given target text. We train our interactive models using Imitation Learning, and our experiments against competitive non-interactive generation models show that models trained interactively are superior to their non-interactive counterparts, even when all models are given the same budget of user inputs or edits.