Pointing to Context from a Relevance Theory Perspective (CALL Conference)

Presentation Summary

Presented findings from a comparative study of human and machine interpreting at the XXIInd International Computer Assisted Language Learning (CALL) Conference in Tokyo, Japan.

Key Points

• Applied Relevance Theory to analyze context-awareness in simultaneous interpreting
• Compared outputs from human interpreters with Google Speech Translation and iFlytek
• Demonstrated significant differences in contextual adaptation between human and machine interpreting
• Discussed implications for AI-assisted language learning and interpreting education