A central part of knowing a language is the ability to combine basic linguistic units to form complex representations. While our neurobiological understanding of how words combine into larger structures has significantly advanced in recent years, the combinatory operations that build words themselves remain unknown. Are complex words such as tombstone and starlet built with the same mechanisms that construct phrases from words, such as grey stone or bright star? Here we addressed this with two magnetoencephalography (MEG) experiments, which simultaneously varied demands associated with phrasal composition, and the processing of morphological complexity in compound and suffixed nouns. Replicating previous findings, we show that portions of the left anterior temporal lobe (LATL) are engaged in the combination of modifiers and monomorphemic nouns in phrases (e.g., brown rabbit). As regards compounding, we show that semantically transparent compounds (e.g., tombstone) also engage left anterior temporal cortex, though the spatiotemporal details of this effect differed from phrasal composition. Further, when a phrase was constructed from a modifier and a transparent compound (e.g., granite tombstone), the typical LATL phrasal composition response appeared at a delayed latency, which follows if an initial within-word operation (tomb + stone) must take place before the combination of the compound with the preceding modifier (granite + tombstone). In contrast to compounding, suffixation (i.e., star + let) did not engage the LATL in any consistent way, suggesting a distinct processing route. Finally, our results suggest an intriguing generalization that morpho-orthographic complexity that does not recruit the LATL may block the engagement of the LATL in subsequent phrase building. In sum, our findings offer a detailed spatiotemporal characterization of the lowest level combinatory operations that ultimately feed the composition of full sentences.
The reliability of acceptability judgments made by individual linguists has often been called into question. Recent large-scale replication studies conducted in response to this criticism have shown that the majority of published English acceptability judgments are robust. We make two observations about these replication studies. First, we raise the concern that English acceptability judgments may be more reliable than judgments in other languages. Second, we argue that it is unnecessary to replicate judgments that illustrate uncontroversial descriptive facts; rather, candidates for replication can emerge during formal or informal peer review. We present two experiments motivated by these arguments. Published Hebrew and Japanese acceptability contrasts considered questionable by the authors of the present paper were rated for acceptability by a large sample of naive participants. Approximately half of the contrasts did not replicate. We suggest that the reliability of acceptability judgments, especially in languages other than English, can be improved using a simple open review system, and that formal experiments are only necessary in controversial cases.
In computational psycholinguistics, various language models have been evaluated against human reading behavior (e.g., eye movement) to build human-like computational models. However, most previous efforts have focused almost exclusively on English, despite the recent trend towards linguistic universal within the general community. In order to fill the gap, this paper investigates whether the established results in computational psycholinguistics can be generalized across languages. Specifically, we re-examine an established generalization -the lower perplexity a language model has, the more human-like the language model isin Japanese with typologically different structures from English. Our experiments demonstrate that this established generalization exhibits a surprising lack of universality; namely, lower perplexity is not always human-like. Moreover, this discrepancy between English and Japanese is further explored from the perspective of (non-)uniform information density. Overall, our results suggest that a crosslingual evaluation will be necessary to construct human-like computational models.
Eye-tracking data from reading represent an important resource for both linguistics and natural language processing. The ability to accurately model gaze features is crucial to advance our understanding of language processing. This paper describes the Shared Task on Eye-Tracking Data Prediction, jointly organized with the eleventh edition of the Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2021). The goal of the task is to predict 5 different token-level eyetracking metrics from the Zurich Cognitive Language Processing Corpus (ZuCo). Eyetracking data were recorded during natural reading of English sentences. In total, we received submissions from 13 registered teams, whose systems include boosting algorithms with handcrafted features, neural models leveraging transformer language models, or hybrid approaches. The winning system used a range of linguistic and psychometric features in a gradient boosting framework.
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