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In syntactic dependency trees, when arcs are drawn from syntactic heads to dependents, they rarely cross. Constraints on these crossing dependencies are critical for determining the syntactic properties of human language, because they define the position of natural language in formal language hierarchies. We study whether the apparent constraints on crossing syntactic dependencies in natural language might be explained by constraints on dependency lengths (the linear distance between heads and dependents). We compare real dependency trees from treebanks of 52 languages against baselines of random trees which are matched with the real trees in terms of their dependency lengths. We find that these baseline trees have many more crossing dependencies than real trees, indicating that a constraint on dependency lengths alone cannot explain the empirical rarity of crossing dependencies. However, we find evidence that a combined constraint on dependency length and the rate of crossing dependencies might be able to explain two of the most-studied formal restrictions on dependency trees: gap degree and well-nestedness.
In 2019 the Journal of Memory and Language instituted an open data and code policy; this policy requires that, as a rule, code and data be released at the latest upon publication. How effective is this policy? We compared 59 papers published before, and 59 papers published after, the policy took effect. After the policy was in place, the rate of data sharing increased by more than 50%. We further looked at whether papers published under the open data policy were reproducible, in the sense that the published results should be possible to regenerate given the data, and given the code, when code was provided. For 8 out of the 59 papers, data sets were inaccessible. The reproducibility rate ranged from 34% to 56%, depending on the reproducibility criteria. The strongest predictor of whether an attempt to reproduce would be successful is the presence of the analysis code: it increases the probability of reproducing reported results by almost 40%. We propose two simple steps that can increase the reproducibility of published papers: share the analysis code, and attempt to reproduce one's own analysis using only the shared materials.
Syntactic priming is known to facilitate comprehension of the target sentence if the syntactic structure of the target sentence aligns with the structure of the prime (Branigan et al., 2005; Tooley and Traxler, 2010). Such a processing facilitation is understood to be constrained due to factors such as lexical overlap between the prime and the target, frequency of the prime structure, etc. Syntactic priming in SOV languages is also understood to be influenced by similar constraints (Arai, 2012). Sentence comprehension in SOV languages is known to be incremental and predictive. Such a top-down parsing process involves establishing various syntactic relations based on the linguistic cues of a sentence and the role of preverbal case-markers in achieving this is known to be critical. Given the evidence of syntactic priming during comprehension in these languages, this aspect of the comprehension process and its effect on syntactic priming becomes important. In this work, we show that syntactic priming during comprehension is affected by the probability of using the prime structure while parsing the target sentence. If the prime structure has a low probability given the sentential cues (e.g., nominal case-markers) in the target sentence, then the chances of persisting with the prime structure in the target reduces. Our work demonstrates the role of structural complexity of the target with regard to syntactic priming during comprehension and highlights that syntactic priming is modulated by an overarching preference of the parser to avoid rare structures