We present a method of quantitative and automatic screening of heteromorphic segments in corpora of aligned translations that streamlines the qualitative and manual search for informational translation shifts. The latter provide data on translation as a result that serve to characterize the textual and linguistic idiosyncrasies of the languages in contact and also provide information on the individual biases of the subject translating as well as the cultural biases inherent in the translation operation for the languages-cultures in contact. To show how the tracking method works, we apply it to a bilingual corpus of translations aligned with their source segment. The corpus contains a speech by American President John F. Kennedy (1961) and a speech by British Prime Minister Theresa May (2017) that have been translated into French. For automatic screening of heteromorphic segments, the LWA (lexical word accuracy) algorithm automatically analyzes all source and target segment pairs and compares the number of lexical words in the source and target segments to calculate the translated information accuracy ratio (TIA). Subsequently, the heteromorphic segments screened using the TIA ratio are analyzed manually to determine the origin or cause of the variation in the translation’s informational content and to define the shifts it causes, if any. The method has the advantage of rationalizing the search for translation shifts in negative and positive heteromorphic segments of translated texts in a parallel corpus since it allows targeting and screening of heteromorphic segment pairs that are most likely to contain informational translation shifts.