Long-distance syntactic dependencies drive the complexity of legal language

Although contracts and other legal documents have long been known to cause processing difficulty in laypeople, the source and nature of this difficulty has remained unclear. To better understand this mismatch, we conducted a corpus analysis (~10 million words) to investigate to what extent difficult-to-process features that are reportedly common in contracts--such as center embedding, low-frequency jargon, passive voice and non-standard capitalization--are in fact present in contracts relative to normal texts. We found that all of these features were strikingly more prevalent in contracts relative to standard-English texts. We also conducted an experimental study ($n=108$ subjects) to determine to what extent such features cause processing difficulties for laypeople of different reading levels. We found that contractual excerpts containing these features were recalled and comprehended at a lower rate than excerpts without these features, even for experienced readers, and that center-embedded clauses led to greater decreases in recall than other features. These findings confirm long-standing anecdotal accounts of the presence of difficult-to-process features in contracts, and show that these features inhibit comprehension and recall of legal content for readers of all levels. Our findings also suggest such difficulties may largely result from working memory costs imposed by complex syntactic features--such as center-embedded clauses--as opposed to a mere lack of understanding of specialized legal concepts, and that removing these features would be both tractable and beneficial for society at large.

Infinite use of finite means? Evaluating the generalization of center embedding learned from an artificial grammar

Human language is often assumed to make "infinite use of finite means" - that is, to generate an infinite number of possible utterances from a finite number of building blocks. From an acquisition perspective, this assumed property of language is interesting because learners must acquire their languages from a finite number of examples. To acquire an infinite language, learners must therefore generalize beyond the finite bounds of the linguistic data they have observed. In this work, we use an artificial language learning experiment to investigate whether people generalize in this way. We train participants on sequences from a simple grammar featuring center embedding, where the training sequences have at most two levels of embedding, and then evaluate whether participants accept sequences of a greater depth of embedding. We find that, when participants learn the pattern for sequences of the sizes they have observed, they also extrapolate it to sequences with a greater depth of embedding. These results support the hypothesis that the learning biases of humans favor languages with an infinite generative capacity.

Depth-Bounded Statistical PCFG Induction as a Model of Human Grammar Acquisition

This article describes a simple PCFG induction model with a fixed category domain that predicts a large majority of attested constituent boundaries, and predicts labels consistent with nearly half of attested constituent labels on a standard evaluation data set of child-directed speech. The article then explores the idea that the difference between simple grammars exhibited by child learners and fully recursive grammars exhibited by adult learners may be an effect of increasing working memory capacity, where the shallow grammars are constrained images of the recursive grammars. An implementation of these memory bounds as limits on center embedding in a depth-specific transform of a recursive grammar yields a significant improvement over an equivalent but unbounded baseline, suggesting that this arrangement may indeed confer a learning advantage.

Syntactic Comprehension of Relative Clauses and Center Embedding Using Pseudowords

Relative clause (RC) formation and center embedding (CE) are two primary syntactic operations fundamental for creating and understanding complex sentences. Ample evidence from previous cross-linguistic studies has revealed several similarities and differences between RC and CE. However, it is not easy to investigate the effect of pure syntactic constraints for RC and CE without the interference of semantic and pragmatic interactions. Here, we show how readers process CE and RC using a self-paced reading task in Korean. More interestingly, we adopted a novel self-paced pseudoword reading task to exploit syntactic operations of the RC and CE, eliminating the semantic and pragmatic interference in sentence comprehension. Our results showed that the main effects of RC and CE conform to previous studies. Furthermore, we found a facilitation effect of sentence comprehension when we combined an RC and CE in a complex sentence. Our study provides a valuable insight into how the purely syntactic processing of RC and CE assists comprehension of complex sentences.