Hylog: A Hybrid Approach to Logging Text Production in Non-alphabetic Scripts

TL;DR

Hylog tackles the challenge of logging text production in non-alphabetic scripts by marrying ecological text logging with precise keystroke recording to capture IME-driven transformations. The system includes modular Word and Chrome plug-ins, a Dynamic Snapshot Window logging strategy, and a hybridizer that produces a synchronized dual trace, enabling fine-grained analyses of IKIs across letters, pinyin syllables, Chinese characters, and words, including IME confirmation events. A proof-of-concept with two translators demonstrates that Hylog yields richer, more accurate timing metrics than alphabetic-focused loggers, revealing cognitive bottlenecks at the IME level and motor regularities at the sub-lexical level. By open-sourcing the tool and outlining extensible patterns for other IMEs and scripts, Hylog offers a scalable path for inclusive, multilingual studies of digital text production and typing behavior.

Abstract

Research keyloggers are essential for cognitive studies of text production, yet most fail to capture the on-screen transformations performed by Input Method Editors (IMEs) for non-alphabetic scripts. To address this methodological gap, we present Hylog, a novel hybrid logging system that combines analytical keylogging with ecological text logging for a more complete and finer-grained analysis. Our modular, open-source system uses plug-ins for standard applications (Microsoft Word, Google Chrome) to capture both keyboard output and rendered text, which a hybridizer module then synchronizes into a dual trace. To validate the system's technical feasibility and demonstrate its analytical capabilities, we conducted a proof-of-concept study where two volunteers translated a text into simplified Chinese. Hylog successfully captured keypresses and temporal intervals between Latin letters, Chinese characters, and IME confirmations -- some measurements invisible to traditional keyloggers. The resulting data enable the formulation of new, testable hypotheses about the cognitive restrictions and affordances at different linguistic layers in IME-mediated typing. Our plug-in architecture enables extension to other IME systems and fosters more inclusive multilingual text-production research.

Figures (8)

• Figure 1: Example of Inputlog's record of English typing
• Figure 2: Sample log collected with Translog II
• Figure 3: Workflow of Hylog, a hybrid input logger
• Figure 4: The architecture of the hybridizer module
• Figure 5: Coherence check of an excerpt from a logging session in Chinese.