Quantifying Structural and Non‐structural Expectations in Relative Clause Processing

Chen, Zhong; Hale, John

doi:10.1111/cogs.12927

Cited by 6 publications

(4 citation statements)

References 99 publications

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“…In the course of online RC processing, recognizing the embedded verb will facilitate the processing of the embedded clause, for the embedded verb is the key to assigning the argument roles to both the head noun and the noun in the embedded clause. The results are not in accord with previous works on L1 speakers (Hale, 2001;Levy, 2008;Chen and Hale, 2021), which showed slower reading only at the embedded NP but not at the embedded verb. This may be due to L2 learners' low proficiency in connecting the thematic role of the embedded NP and the following verb.…”

Section: Methodscontrasting

confidence: 99%

Exploring the effects of animacy and verb type on the processing asymmetry between SRC and ORC among Chinese EFL learners

Sun

Fan

2023

Humanit Soc Sci Commun

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This study investigates the production and comprehension of subject relative clause (SRC) and object relative clause (ORC) in English by Chinese EFL learners. Two experiments are reported. Using a sentence completion task to elicit the production of relative clauses (RCs), Experiment 1 examined the distributional patterns of SRC and ORC and showed that SRC was more frequently distributed than ORC. In addition, animacy and verb type had effects on the asymmetric distribution of SRC and ORC. Using a word-by-word moving-window self-paced reading paradigm, Experiment 2 further compared reading times (RTs) of SRC and ORC containing different animacy and verb type configurations. Reading difficulties in ORC were observed, and comprehension difficulties of certain configurations of animacy and verb type just mirrored their frequencies in the first experiment. Taken together, the processing asymmetry of SRC and ORC has been observed in both comprehension and production processes. Comprehension difficulties are believed to stem from the asymmetric distributions of sentence patterns involving different animacy and verb type configurations. These findings suggest that comprehension difficulties are correlated with the distributional patterns, which could provide strong support to the Production-Distribution-Comprehension account, the experience-based approach applicable in language acquisition.

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Section: Methodscontrasting

confidence: 99%

Exploring the effects of animacy and verb type on the processing asymmetry between SRC and ORC among Chinese EFL learners

Sun

Fan

2023

Humanit Soc Sci Commun

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“…Sag and Wasow (2011) distill this and related considerations into a short list of requirements on performance-plausible grammars. In subsequent years, Stabler's (1997) formalization emerged as a version of transformational grammar that meets Sag and Wasow's requirements (see, e.g., Chen & Hale, 2021;Graf, Monette, & Zhang, 2017;Hale, 2006;Hunter, Stanojević, & Stabler, 2019;Stanojević & Stabler, 2018). 3 More precisely, Sturt & Lombardo's challenge is addressed by combining revealing parsing operation with a predictive step.…”

Section: Discussionmentioning

confidence: 99%

Modeling Structure‐Building in the Brain With CCG Parsing and Large Language Models

et al. 2023

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To model behavioral and neural correlates of language comprehension in naturalistic environments, researchers have turned to broad‐coverage tools from natural‐language processing and machine learning. Where syntactic structure is explicitly modeled, prior work has relied predominantly on context‐free grammars (CFGs), yet such formalisms are not sufficiently expressive for human languages. Combinatory categorial grammars (CCGs) are sufficiently expressive directly compositional models of grammar with flexible constituency that affords incremental interpretation. In this work, we evaluate whether a more expressive CCG provides a better model than a CFG for human neural signals collected with functional magnetic resonance imaging (fMRI) while participants listen to an audiobook story. We further test between variants of CCG that differ in how they handle optional adjuncts. These evaluations are carried out against a baseline that includes estimates of next‐word predictability from a transformer neural network language model. Such a comparison reveals unique contributions of CCG structure‐building predominantly in the left posterior temporal lobe: CCG‐derived measures offer a superior fit to neural signals compared to those derived from a CFG. These effects are spatially distinct from bilateral superior temporal effects that are unique to predictability. Neural effects for structure‐building are thus separable from predictability during naturalistic listening, and those effects are best characterized by a grammar whose expressive power is motivated on independent linguistic grounds.

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“…In English, as in many other languages, ORCs are generally more difficult to process than SRCs (Lau and Tanaka, 2021). This difficulty is thought in part to reflect the relative unpredictability of ORCs (Chen and Hale, 2021;Hale, 2001;Staub, 2010;Vani et al, 2021). However, unlike the garden path constructions, the overall comprehension difficulty associated with ORCs has occasionally been argued to involve memory-related difficulties above and beyond the effects of predictability even by proponents of surprisal theory (Levy, 2013).…”

Section: The Syntactic Ambiguity Processing Benchmark: Dataset Constr...mentioning

confidence: 99%

Surprisal does not explain syntactic disambiguation difficulty: evidence from a large-scale benchmark

Huang¹,

Arehalli²,

Kugemoto³

et al. 2023

Preprint

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Prediction has been proposed as an overarching principle that explains human information processing in language and beyond. To what degree can processing difficulty in syntactically complex sentences - one of the major concerns of psycholinguistics - be explained by predictability, as estimated using computational language models? A precise, quantitative test of this question requires a much larger scale data collection effort than has been done in the past. We present the Syntactic Ambiguity Processing Benchmark, a dataset of self-paced reading times from 2000 participants, who read a diverse set of complex English sentences. This dataset makes it possible to measure processing difficulty associated with individual syntactic constructions, and even individual sentences, precisely enough to rigorously test the predictions of computational models of language comprehension. We find that the predictions of language models with two different architectures sharply diverge from the reading time data, dramatically underpredicting processing difficulty, failing to predict relative difficulty among different syntactic ambiguous constructions, and only partially explaining item-wise variability. These findings suggest that prediction is most likely insufficient on its own to explain human syntactic processing.

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Quantifying Structural and Non‐structural Expectations in Relative Clause Processing

Cited by 6 publications

References 99 publications

Exploring the effects of animacy and verb type on the processing asymmetry between SRC and ORC among Chinese EFL learners

Exploring the effects of animacy and verb type on the processing asymmetry between SRC and ORC among Chinese EFL learners

Modeling Structure‐Building in the Brain With CCG Parsing and Large Language Models

Surprisal does not explain syntactic disambiguation difficulty: evidence from a large-scale benchmark

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