Dual Mechanism Priming Effects in H indi Word Order (2022)

Abstract: Word order choices during sentence production can be primed by preceding sentences. In this work, we test the DUAL MECHANISM hypothesis that priming is driven by multiple different sources. Using a Hindi corpus of text productions, we model lexical priming with an n-gram cache model, and we capture more abstract syntactic priming with an adaptive neural language model. We permute the preverbal constituents of corpus sentences and then use a logistic regression model to predict which sentences actually occurred in the corpus against artificially generated meaning-equivalent variants. Our results indicate that lexical priming and lexically-independent syntactic priming affect complementary sets of verb classes. By showing that different priming influences are separable from one another, our results support the hypothesis that multiple different cognitive mechanisms underlie priming.

## data our data set consists of 1996 reference sentences containing well-defined subject and object constituents corresponding to the projective dependency trees in hutb corpus @xcite . the sentences in hutb corpus belong to newswire domain and contains written text in naturally occurring context i.e, every reference sentence in our dataset was taken from a newspaper article, thus situated in the context of preceding sentences. for each reference sentence in our data set, we created counterfactual grammatical variants expressing the same truth-conditional meaning 1 by permuting the preverbal constituents whose heads were linked to the root node in the dependency tree. 2 inspired by grammar rules proposed in the nlg literature (rajkumar and white, 2014), ungrammatical variants were automatically filtered out by detecting dependency relation sequences not attested in the original hutb corpus. after filtering, we had 72833 variant sentences for our classification task. 1 a limitation of this definition: it does not capture the fact that, in contrast to marked orders, which necessitate context for a full interpretation, sov canonical orders are neutral with respect to the preceding discourse @xcite ). ## classification task in order to mitigate the data imbalance between the two groups (1996 references vs. 72833 variants), we follow @xcite by formulating our task as a pair-wise ranking problem. the goal of the basic binary classifier model is shown in equation 1, where the model learns a feature weight (w) such that the dot product of the variant feature vector (ϕ(variant)) with w is less than the dot product of w with the reference feature vector (ϕ(ref erence)). the same goal can be written as equation 2 which ensures that w's dot product with the difference between the feature vectors is positive. this transformation alleviates issues from having dramatically unbalanced class distributions. we first arranged the references and variants into ordered pairs (e.g., a reference with two variants would be paired as (ref erence, variant 1 ) and (variant 2 , ref erence)), and then subtracted the feature vectors of the first member of the pair from the feature vectors of its second member. we then assigned binary labels to each pair, with referencevariant pairs coded as "1", and variant-reference pairs coded as "0", thus re-balancing our previously severely imbalanced classification task. additionally, the feature values of sentences with varying lengths get centered using this technique. refer to @xcite and @xcite for a more detailed illustration. using features extracted from the transformed dataset, we trained a logistic regression model to predict each reference sentence (see equation 3 ). all the experiments were done with the generalized linear model (glm) package in r. here choice is encoded by the binary dependent variable as discussed above (1: reference preference and 0: variant preference). ## experiments and results we tested the hypothesis that surprisal enhanced with inter-sentential discourse information (adaptive lstm surprisal) predicts constituent ordering in hindi over other baseline cognitive controls, including information status, dependency length, lexical repetition, and non-adaptive surprisal. for our adaptation experiments, we used an adaptive learning rate of 2 as it minimized the perplexity of the validation data set (see ## discussion written text is a consequence of language production and is often edited to facilitate comprehension for the readers. according to @xcite language production model, speakers evaluate their own utterances by comprehending their own speech and make necessary adjustments to an utterance via a self-monitoring loop. therefore, we interpret our results in the light of the dual mechanism account @xcite described earlier in the introduction. this account makes claims pertaining to both production and comprehension and tooley and @xcite demonstrates the parity of syntactic persistence across both phenomena. our results indicate that the dual mechanism account can be extended to postulate a viable model of priming effects in hindi word order. constituent ordering choices demonstrate both lexically independent syntactic priming as well as lexically dependent effects. we discuss how these two effects are induced by distinct underlying mechanisms (as stated at the outset), viz., implicit learning @xcite , and residual activation @xcite respectively. previous work suggests that lexical overlap between prime and target sentences enhances syntactic priming @xcite . we also show that certain verb classes are more susceptible to priming than others. specifically, give verbs selecting double objects are most prone to priming, a case demonstrated in english as well @xcite , thus providing crosslinguistic support for the finding. hindi conjunct verbs in prime sentences trigger subsequent target sentences with conjunct verbs, and preverbal word order patterns for hindi conjunct verbs are influenced by the repetition of lexical cues mentioned in the previous sentence. these two findings lend credence to the idea in the literature that lexical boost effects are attested for heads (conjunct verbs in this case) as well as other non-head lexical items @xcite . the explanation for such effects stems from the residual activation theory @xcite where activated lemmas (linguistic category and combinatory nodes) in the prime utterance retain their activation for a short time. the residue of such activation is transferred to the target lemma. @xcite proffer an alternative explanation for lexical boost via spreading activation mechanism posited by the act-r framework of cognition. however, we observe syntactic priming independent of lexical effects over and above lexical repetition in double object constructions. our verbspecific priming analyses indicate that prime sentences need not share the same main verb as the target sentence; instead, successive sentences may have a similar argument structure (subcategorization frame), which enforces a tendency to repeat canonical structures. @xcite show that such effects are best explained by the implicit learning account @xcite , where language users unconsciously acquire abstract routines over a period of time. in stark contrast to short-lived residual activation accounting for lexical boost effects, @xcite showed that lexically independent syntactic priming effects persisted even when 10 intervening structures occurred between prime and target utterances. the relationship between prediction (quantified using our surprisal measures) and learning is made explicit in the p-chain framework of dell and kittredge (2013) connecting production and comprehension. according to p-chain assumptions, prediction error leads to implicit learning, which in turn helps the prediction system to adapt to less common structures (like double object constructions), which are known to induce higher priming strengths compared to commonplace structures @xcite @xcite . while our results demonstrate priming at the level of verb classes, @xcite showed that the combinatory properties of the verb need not be the sole driver of priming in hindi. in their self-paced reading experiments involving identical critical verbs in both prime and target sentences, they observed faster reading times only in the target condition where nominals were marked by a locative case marker (in contrast to accusative and ergative conditions). language-specific properties like case markers and the relationship between hindi production and comprehension processes needs to be investigated more thoroughly by extending our preliminary human evaluation (via a simple forced choice task) using more finegrained measures like reading aloud and silent reading times as proposed by @xcite . overall, in line with the assumptions of the dual mechanism account, our main findings suggest that hindi word order choices are influenced by both lexically independent syntactic priming effects as well as lexically dependent priming effects. future inquiries need to explore controlled experiments to corroborate the psychological reality of our current results. root hua main uajala k4 yah k1 sukravar k7t daak k3 prapt pof । rsym amar pof__cn ko lwg__psp ko lwg__psp se lwg__psp (a) dependency tree label dependency relation invariant syntactic relations k1 subject/agent k2 object/patient k3 instrument k4 object/recipient k7t location in time complex predicate relation pof parts of conjunct verb pof_cn parts of compound noun local word group (lwg) lwg_psp postposition lwg_vaux auxilliary verb symbols rsym symbol relation (b) dependency relations figure 1: example hutb dependency tree and relation labels appendix a variant generation (5) context sentence amar ujala-ki amar ujala-gen bhumika role nispaksh unbiased rehti remain hai be.prs.sg amar ujala's role remains unbiased. (6) a. amar ujala-ko amar ujala-acc yah it sukravar-ko friday-on daak-se post-inst prapt receive hua be.pst.sg [given-@xmath0. b. yah amar ujala-ko sukravar-ko daak-se prapt hua [given-@xmath1. sukravar-ko yah amar ujala-ko daak-se prapt hua [new-@xmath2 this work uses sentences from the hindi-urdu treebank (hutb) corpus of dependency trees @xcite containing well-defined subject and object constituents. figure 1 displays the dependency tree (and a glossary of relation labels) for reference sentence 6a. the grammatical variants were created using an algorithm that took as input the dependency tree corresponding to each hutb reference sentence. the re-ordering algorithm permuted the preverbal dependents of the root verb and linearized the resulting tree to obtain variant sentences. for example, corresponding to the reference sentence 6a and its root verb "hai" (see figure 1a ), the preverbal constituents 7 with parents as "ujala", "yah", "suravar", "daak", and "prapt" were permuted to generate the artificial variants (6b and 6c). the ungrammatical variants were automatically filtered out using dependency relation sequences (denoting grammar rules) attested in the gold standard corpus of hutb trees. in the dependency tree 1a, "k4-k1", "k7t-k1", "k3-k7t", and "pof-k3" are dependency relation sequences. in cases where the total number of variants exceeded 100 (a random cutoff), foot_5 we chose 99 non-reference variants randomly along with the reference sentence.