  Previous work has shown that isolated non-canonical sentences with
Object-before-Subject (OSV) order are initially harder to process than their
canonical counterparts with Subject-before-Object (SOV) order. Although this
difficulty diminishes with appropriate discourse context, the underlying
cognitive factors responsible for alleviating processing challenges in OSV
sentences remain a question. In this work, we test the hypothesis that
dependency length minimization is a significant predictor of non-canonical
(OSV) syntactic choices, especially when controlling for information status
such as givenness and surprisal measures. We extract sentences from the
Hindi-Urdu Treebank corpus (HUTB) that contain clearly-defined subjects and
objects, systematically permute the preverbal constituents of those sentences,
and deploy a classifier to distinguish between original corpus sentences and
artificially generated alternatives. The classifier leverages various
discourse-based and cognitive features, including dependency length, surprisal,
and information status, to inform its predictions. Our results suggest that,
although there exists a preference for minimizing dependency length in
non-canonical corpus sentences amidst the generated variants, this factor does
not significantly contribute in identifying corpus sentences above and beyond
surprisal and givenness measures. Notably, discourse predictability emerges as
the primary determinant of constituent-order preferences. These findings are
further supported by human evaluations involving 44 native Hindi speakers.
Overall, this work sheds light on the role of expectation adaptation in
word-ordering decisions. We conclude by situating our results within the
theories of discourse production and information locality.
