Discourse Context Predictability Effects in Hindi Word Order

TL;DR

This study investigates how discourse predictability affects Hindi word order choices, revealing that information status and discourse cues significantly influence syntactic variation, especially in non-canonical structures.

Contribution

It introduces a novel classifier using discourse and cognitive features to predict Hindi word order, highlighting the role of discourse predictability in syntactic variation.

Findings

Discourse predictability influences Hindi word order choices.

Information status affects non-canonical object-fronted orders.

A classifier using discourse features accurately predicts sentence structures.

Abstract

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.