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This paper presents the results of a comparison of three metrics for measuring cross-linguistic variations in information volume between parallel segments of a bilingual corpus. The performance of each metric is compared with the results of a human annotation of multiword expressions (MWEs) in each segment. The first metric measures characters in source and target segments and compares the variation, if any, with the expected character count ratio based on averages for the entire source and target texts. The second metric follows the same method except that it measures graphical word count (function and content words combined) in target and source segments. The third metric involves an analysis obtained via the content word precision (CWP) algorithm coded in Python. The purpose of the comparison is to determine which metric is closer to the human annotation and is, therefore, a better indicator of a large spectrum of MWEs.

A Comparison of Three Metrics for Detecting Cross- Linguistic Variations in Information Volume and Multiword Expressions Between Parallel Bitexts