X2T: TRAINING AN X-TO-TEXT TYPING INTERFACE WITH ONLINE LEARNING FROM USER FEEDBACK

Abstract

We aim to help users communicate their intent to machines using flexible, adaptive interfaces that translate arbitrary user input into desired actions. In this work, we focus on assistive typing applications in which a user cannot operate a keyboard, but can instead supply other inputs, such as webcam images that capture eye gaze or neural activity measured by a brain implant. Standard methods train a model on a fixed dataset of user inputs, then deploy a static interface that does not learn from its mistakes; in part, because extracting an error signal from user behavior can be challenging. We investigate a simple idea that would enable such interfaces to improve over time, with minimal additional effort from the user: online learning from user feedback on the accuracy of the interface's actions. In the typing domain, we leverage backspaces as feedback that the interface did not perform the desired action. We propose an algorithm called x-to-text (X2T) that trains a predictive model of this feedback signal, and uses this model to fine-tune any existing, default interface for translating user input into actions that select words or characters. We evaluate X2T through a small-scale online user study with 12 participants who type sentences by gazing at their desired words, a large-scale observational study on handwriting samples from 60 users, and a pilot study with one participant using an electrocorticography-based brain-computer interface. The results show that X2T learns to outperform a non-adaptive default interface, stimulates user co-adaptation to the interface, personalizes the interface to individual users, and can leverage offline data collected from the default interface to improve its initial performance and accelerate online learning.

1. INTRODUCTION

Recent advances in user interfaces have enabled people with sensorimotor impairments to more effectively communicate their intent to machines. For example, Ward et al. (2000) enable users to type characters using an eye gaze tracker instead of a keyboard, and Willett et al. (2020) enable a paralyzed human patient to type using a brain implant that records neural activity. The main challenge in building such interfaces is translating high-dimensional, continuous user input into desired actions. Standard methods typically calibrate the interface on predefined training tasks for which expert demonstrations are available, then deploy the trained interface. Unfortunately, this does not enable the interface to improve with use or adapt to distributional shift in the user inputs. In this paper, we focus on the problem of assistive typing: helping a user select words or characters without access to a keyboard, using eye gaze inputs (Ward et al., 2000) ; handwriting inputs (see Figure 7 in the appendix), which can be easier to provide than direct keystrokes (Willett et al., 2020) ; or inputs from an electrocorticography-based brain implant (Leuthardt et al., 2004; Silversmith et al., 2020) . To enable any existing, default interface to continually adapt to the user, we train a model using online learning from user feedback. The key insight is that the user provides feedback on the interface's actions via backspaces, which indicate that the interface did not perform the desired

