Improving Source-text Comprehension and Internalization with Argument Flow Analysis

Higher-level discourse structuring features are commonly acknowledged; however, few translators possess the expertise to analyze grounding features like Robert Longacre's mainline and support in source languages. Clear Bible has developed digital tools that allow knowledge experts to annotate source language texts for features like these, which can then be made available to translation teams by displaying the source language analysis on an aligned translation.

The larger the unit of text that is annotated (e.g., a clause versus a phrase or a word), the better the representation, even if the aligned translation has not adequately translated the feature. When translators can identify the mainline of a discourse and connect supporting material to the correct mainline proposition, it improves their ability to internalize the overall flow of the text, which ultimately improves comprehension.

This is especially crucial in OV languages, where inductive ordering of an argument is more restricted than in VO languages such as Greek and Hebrew. Identifying transitions in the discourse makes it easier for teams to select appropriate strategies to naturally represent them in the target language, thus improving accuracy.
After a brief overview of the concepts, the focus will be on equipping attendees to apply the data to a selected text. The argument flow analysis methodology employs Stephen Levinsohn's discourse analysis model and Walter Kintsch's construction-integration model.

Join us to learn about this innovative approach to improving discourse structuring in translation.