Computer-Assisted Interpreting Tools: Status Quo and Future Trends

Abstract

Computer-assisted interpreting (CAI) tools have the potential to benefit the interpreting profession and to improve its ecosystem considerably. Academic interest in this field has intensified in recent years. However, there have been no thorough analyses of the definitions and classifications of CAI tools or of the empirical studies on the subject. This study overviews CAI tools holistically. It describes advances as well as gaps that remain to be filled. It also provides an in-depth examination of the status quo and suggests potential avenues for improvement. The article begins by distinguishing between CAI tools in the broad sense and CAI tools in the narrow sense. By bridging the conceptual gaps between the two, we propose a unified description and a categorisation that reflects the main features of CAI tools. This comprehensive review analyses 27 empirical studies and examines the manner in which CAI tools affect interpreters’ performance. Since the influencing factors that have been identified in previous experiments vary between interpreters-related (e.g. interpreters’ profiles) and settings-related (e.g. reference information display modes), the contribution of CAI tools to overall interpreter performance can be different. Product-driven, practice-driven, and process-driven studies are identified as future trends in studies of CAI tools.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 19 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.