Expectation-driven facilitation (Hale, 2001; Levy, 2008) and locality-driven retrieval difficulty (Gibson, 1998, 2000; Lewis & Vasishth, 2005) are widely recognized to be two critical factors in incremental sentence processing; there is accumulating evidence that both can influence processing difficulty. However, it is unclear whether and how expectations and memory interact. We first confirm a key prediction of the expectation account: a Hindi self-paced reading study shows that when an expectation for an upcoming part of speech is dashed, building a rarer structure consumes more processing time than building a less rare structure. This is a strong validation of the expectation-based account. In a second study, we show that when expectation is strong, i.e., when a particular verb is predicted, strong facilitation effects are seen when the appearance of the verb is delayed; however, when expectation is weak, i.e., when only the part of speech “verb” is predicted but a particular verb is not predicted, the facilitation disappears and a tendency towards a locality effect is seen. The interaction seen between expectation strength and distance shows that strong expectations cancel locality effects, and that weak expectations allow locality effects to emerge.

Much previous work has suggested that word order preferences across languages can be explained by the dependency distance minimization constraint (Ferrer-i Cancho, 2008Hawkins, 1994). Consistent with this claim, corpus studies have shown that the average distance between a head (e.g., verb) and its dependent (e.g., noun) tends to be short cross-linguistically show that the comprehension system can adapt to the typological properties of a language, for example, verb-final order, leading to more complex structures, for example, having longer linear distance between a head and its dependent. In this paper, we conduct a corpus study for a group of 38 languages, which were either Subject-Verb-Object (SVO) or Subject-Object-Verb (SOV), in order to investigate the role of word order typology in determining syntactic complexity. We present results aggregated across all dependency types, as well as for specific verbal (objects, indirect objects, and adjuncts) and nonverbal (nominal, adjectival, and adverbial) dependencies. The results suggest that dependency distance in a language is determined by the default word order of a language, and crucially, the direction of a dependency (whether the head precedes the dependent or follows it; e.g., whether the noun precedes the verb or follows it). Particularly we show that in SOV languages (e.g., Hindi, Korean) as well as SVO languages (e.g., English, Spanish), longer linear distance (measured as number of words) between head and dependent arises in structures when they mirror the default word order of the language. In addition to showing results on linear distance, we also investigate the influence of word order typology on hierarchical distance (HD; measured as number of heads between head and dependent). The results for HD are similar to that of linear distance. At the same time, in comparison to linear distance, the influence of adaptability on HD seems less strong. In particular, the results Correspondence should be sent to Himanshu Yadav is now affiliated with University of Potsdam, Germany. Vishakha Shukla is now affiliated with Shroff Charity Eye Hospital, Delhi, India. show that most languages tend to avoid greater structural depth. Together, these results show evidence for "limited adaptability" to the default word order preferences in a language. Our results support a large body of work in the processing literature that highlights the importance of linguistic exposure and its interaction with working memory constraints in determining sentence complexity. Our results also point to the possible role of other factors such as the morphological richness of a language and a multifactor account of sentence complexity remains a promising area for future investigation.

Delaying the appearance of a verb in a noun-verb dependency tends to increase processing difficulty at the verb; one explanation for this locality effect is decay and/or interference of the noun in working memory. Surprisal, an expectation-based account, predicts that delaying the appearance of a verb either renders it no more predictable or more predictable, leading respectively to a prediction of no effect of distance or a facilitation. Recently, Husain et al. (2014) suggested that when the exact identity of the upcoming verb is predictable (strong predictability), increasing argument-verb distance leads to facilitation effects, which is consistent with surprisal; but when the exact identity of the upcoming verb is not predictable (weak predictability), locality effects are seen. We investigated Husain et al.'s proposal using Persian complex predicates (CPs), which consist of a non-verbal element—a noun in the current study—and a verb. In CPs, once the noun has been read, the exact identity of the verb is highly predictable (strong predictability); this was confirmed using a sentence completion study. In two self-paced reading (SPR) and two eye-tracking (ET) experiments, we delayed the appearance of the verb by interposing a relative clause (Experiments 1 and 3) or a long PP (Experiments 2 and 4). We also included a simple Noun-Verb predicate configuration with the same distance manipulation; here, the exact identity of the verb was not predictable (weak predictability). Thus, the design crossed Predictability Strength and Distance. We found that, consistent with surprisal, the verb in the strong predictability conditions was read faster than in the weak predictability conditions. Furthermore, greater verb-argument distance led to slower reading times; strong predictability did not neutralize or attenuate the locality effects. As regards the effect of distance on dependency resolution difficulty, these four experiments present evidence in favor of working memory accounts of argument-verb dependency resolution, and against the surprisal-based expectation account of Levy (2008). However, another expectation-based measure, entropy, which was computed using the offline sentence completion data, predicts reading times in Experiment 1 but not in the other experiments. Because participants tend to produce more ungrammatical continuations in the long-distance condition in Experiment 1, we suggest that forgetting due to memory overload leads to greater entropy at the verb.

This is the first attempt at characterizing reading difficulty in Hindi using naturally occurring sentences. We created the Potsdam-Allahabad Hindi Eyetracking Corpus by recording eye-movement data from 30 participants at the University of Allahabad, India. The target stimuli were 153 sentences selected from the beta version of the Hindi-Urdu treebank. We find that word- or low-level predictors (syllable length, unigram and bigram frequency) affect first-pass reading times, regression path duration, total reading time, and outgoing saccade length. An increase in syllable length results in longer fixations, and an increase in word unigram and bigram frequency leads to shorter fixations. Longer syllable length and higher frequency lead to longer outgoing saccades. We also find that two predictors of sentence comprehension difficulty, integration and storage cost, have an effect on reading difficulty. Integration cost (Gibson, 2000) was approximated by calculating the distance (in words) between a dependent and head; and storage cost (Gibson, 2000), which measures difficulty of maintaining predictions, was estimated by counting the number of predicted heads at each point in the sentence. We find that integration cost mainly affects outgoing saccade length, and storage cost affects total reading times and outgoing saccade length. Thus, word-level predictors have an effect in both early and late measures of reading time, while predictors of sentence comprehension difficulty tend to affect later measures. This is, to our knowledge, the first demonstration using eye-tracking that both integration and storage cost influence reading difficulty.

The paper describes the overall design of a new two stage constraint based hybrid approach to dependency parsing. We define the two stages and show how different grammatical construct are parsed at appropriate stages. This division leads to selective identification and resolution of specific dependency relations at the two stages. Furthermore, we show how the use of hard constraints and soft constraints helps us build an efficient and robust hybrid parser. Finally, we evaluate the implemented parser on Hindi and compare the results with that of two data driven dependency parsers.