Semi-automatic Simultaneous Interpreting Quality Evaluation
This addresses the need for more objective quality evaluation in interpreting, though it is incremental as it builds on existing semantic frameworks.
The paper tackles the problem of objectively evaluating simultaneous interpreting quality by proposing a semantic-scoring method based on frame semantics and Frame Elements to compare meaning between source and target texts, with experiments showing a significant correlation coefficient with human judgment.
Increasing interpreting needs a more objective and automatic measurement. We hold a basic idea that 'translating means translating meaning' in that we can assessment interpretation quality by comparing the meaning of the interpreting output with the source input. That is, a translation unit of a 'chunk' named Frame which comes from frame semantics and its components named Frame Elements (FEs) which comes from Frame Net are proposed to explore their matching rate between target and source texts. A case study in this paper verifies the usability of semi-automatic graded semantic-scoring measurement for human simultaneous interpreting and shows how to use frame and FE matches to score. Experiments results show that the semantic-scoring metrics have a significantly correlation coefficient with human judgment.