TY - JOUR
T1 - Computer-Assisted Interpreting Tools
T2 - Status Quo and Future Trends
AU - Guo, Meng
AU - Han, Lili
AU - Anacleto, Marta Teixeira
N1 - Publisher Copyright:
© 2023 ACADEMY PUBLICATION.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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.
AB - 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.
KW - automatic speech recognition
KW - computer-assisted interpreting tools
KW - empirical studies
KW - interpreting
KW - state of the art
UR - http://www.scopus.com/inward/record.url?scp=85145829600&partnerID=8YFLogxK
U2 - 10.17507/tpls.1301.11
DO - 10.17507/tpls.1301.11
M3 - Article
AN - SCOPUS:85145829600
SN - 1799-2591
VL - 13
SP - 89
EP - 99
JO - Theory and Practice in Language Studies
JF - Theory and Practice in Language Studies
IS - 1
ER -