We study the influence of context on how humans evaluate the complexity of a sentence in English. We collect a new dataset of sentences, where each sentence is rated for perceived complexity within different contextual windows. We carry out an in-depth analysis to detect which linguistic features correlate more with complexity judgments and with the degree of agreement among annotators. We train several regression models, using either explicit linguistic features or contextualized word embeddings, to predict the mean complexity values assigned to sentences in the different contextual windows, as well as their standard deviation. Results show that models leveraging explicit features capturing morphosyntactic and syntactic phenomena perform always better, especially when they have access to features extracted from all contextual sentences.

Sentence Complexity in Context

Iavarone, Benedetta;
2021

Abstract

We study the influence of context on how humans evaluate the complexity of a sentence in English. We collect a new dataset of sentences, where each sentence is rated for perceived complexity within different contextual windows. We carry out an in-depth analysis to detect which linguistic features correlate more with complexity judgments and with the degree of agreement among annotators. We train several regression models, using either explicit linguistic features or contextualized word embeddings, to predict the mean complexity values assigned to sentences in the different contextual windows, as well as their standard deviation. Results show that models leveraging explicit features capturing morphosyntactic and syntactic phenomena perform always better, especially when they have access to features extracted from all contextual sentences.
2021
Settore INF/01 - Informatica
11th Workshop on Cognitive Modeling and Computational Linguistics, CMCL 2021
2021
CMCL 2021 - Workshop on Cognitive Modeling and Computational Linguistics, Proceedings
Association for Computational Linguistics (ACL)
9781954085350
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11384/134286
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