Discourse Context Predictability Effects in Hindi Word Order

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 10390 - 10406 December 7-11, 2022 ©2022 Association for Computational Linguistics Discourse Context Predictability Effects in Hindi Word Order Sidharth Ranjan IIT Delhi sidharth.ranjan03@gmail.com Marten van Schijndel Cornell University mv443@cornell.edu Sumeet Agarwal IIT Delhi sumeet@iitd.ac.in Rajakrishnan Rajkumar IISER Bhopal rajak@iiserb.ac.in Abstract We test the hypothesis that discourse pre- dictability 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 or- der of the following sentences. Specifically, we extract sentences from the Hindi-Urdu Tree- bank corpus (HUTB), permute the preverbal constituents of those sentences, and build a classifier to predict which sentences actually occurred in the corpus against artificially gener- ated 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. 1 Introduction Grammars of natural languages have evolved over time to factor in cognitive pressures related to pro- duction (Hawkins, 1994, 2000) and comprehen- sion (Hawkins, 2004, 2014), learnability (Chris- tiansen and Chater, 2008) and communicative effi- ciency (Jaeger and Tily, 2011; Gibson et al., 2019). In this work, we test the hypothesis that maximiza- tion of discourse predictability (quantified using lexical repetition surprisal and adaptive LSTM sur- prisal) is a significant predictor of Hindi syntactic choice, when controlling for information status, de- pendency length, and surprisal measures estimated from n-gram, LSTM and incremental constituency parsing models. Our hypothesis is inspired by a solid body of evi- dence from studies based on dependency treebanks of typologically diverse languages which show that grammars of languages tend to order words by minimizing dependency length (Liu, 2008; Futrell et al., 2015) and maximizing their trigram pre- dictability (Gildea and Jaeger, 2015). Parallel to this line of work on sentence-level word order, an- other strand of work has focused on discourse-level estimates of entropy starting from the Constant En- tropy Rate hypothesis (CER; Genzel and Charniak, 2002). To overcome the major difficulty of esti- mating sentence probabilities conditioned on the previous discourse context, Qian and Jaeger (2012) approximated discourse-level entropy using lexi- cal cues from the previous context. In contrast, we leverage modern computational psycholinguis- tic neural techniques to obtain word and sentence- level estimates of inter-sentential discourse pre- dictability and study the impact of these measures on Hindi word order choices. We conclude that discourse-level priming influences Hindi word or- der decisions and interpret our findings in the light of the factors outlined by Reitter et al. (2011). Hindi (Indo-Aryan language; Indo-European language family) has a rich case-marking …

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