We test the hypothesis that discourse predictability influences Hindi
syntactic choice. While prior work has shown that a number of factors (e.g.,
information status, dependency length, and syntactic surprisal) influence Hindi
word order preferences, the role of discourse predictability is underexplored
in the literature. Inspired by prior work on syntactic priming, we investigate
how the words and syntactic structures in a sentence influence the word order
of the following sentences. Specifically, we extract sentences from the
Hindi-Urdu Treebank corpus (HUTB), permute the preverbal constituents of those
sentences, and build a classifier to predict which sentences actually occurred
in the corpus against artificially generated distractors. The classifier uses a
number of discourse-based features and cognitive features to make its
predictions, including dependency length, surprisal, and information status. We
find that information status and LSTM-based discourse predictability influence
word order choices, especially for non-canonical object-fronted orders. We
conclude by situating our results within the broader syntactic priming
literature.

考察了语篇可预测性对印地语句法选择的影响，发现信息状态和 LSTM-based 语篇可预测性对非规范的宾语前置语序选择有影响。