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
Brunato D;Dell'orletta F
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.| Campo DC | Valore | Lingua |
|---|---|---|
| dc.authority.people | Iavarone B | it |
| dc.authority.people | Brunato D | it |
| dc.authority.people | Dell'orletta F | it |
| dc.collection.id.s | 71c7200a-7c5f-4e83-8d57-d3d2ba88f40d | * |
| dc.collection.name | 04.01 Contributo in Atti di convegno | * |
| dc.contributor.appartenenza | Istituto di linguistica computazionale "Antonio Zampolli" - ILC | * |
| dc.contributor.appartenenza.mi | 918 | * |
| dc.date.accessioned | 2024/02/20 13:13:45 | - |
| dc.date.available | 2024/02/20 13:13:45 | - |
| dc.date.issued | 2021 | - |
| dc.description.abstracteng | 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. | - |
| dc.description.affiliations | Scuola Normale Superiore, Scuola Normale Superiore, Italy; Istituto Di Linguistica Computazionale Antonio Zampolli, Pisa, Istituto di Linguistica Computazionale "Antonio Zampolli", Pisa, , Italy | - |
| dc.description.allpeople | Iavarone, B; Brunato, D; Dell'Orletta, F | - |
| dc.description.allpeopleoriginal | Iavarone B ; Brunato D ; Dell'orletta F | - |
| dc.description.fulltext | none | en |
| dc.description.numberofauthors | 3 | - |
| dc.identifier.doi | 10.18653/v1/2021.cmcl-1.23 | - |
| dc.identifier.isbn | 978-1-954085-35-0 | - |
| dc.identifier.scopus | 2-s2.0-85123172973 | - |
| dc.identifier.uri | https://hdl.handle.net/20.500.14243/440176 | - |
| dc.identifier.url | http://www.scopus.com/record/display.url?eid=2-s2.0-85123172973&origin=inward | - |
| dc.language.iso | eng | - |
| dc.relation.conferencedate | 10/06/2021 | - |
| dc.relation.conferencename | Proceedings of Workshop on Cognitive Modeling and Computational Linguistics (CMCL 2021) | - |
| dc.relation.firstpage | 186 | - |
| dc.relation.lastpage | 199 | - |
| dc.subject.keywords | linguistic complexity | - |
| dc.subject.keywords | crowdsourcing | - |
| dc.subject.keywords | human perception | - |
| dc.subject.singlekeyword | linguistic complexity | * |
| dc.subject.singlekeyword | crowdsourcing | * |
| dc.subject.singlekeyword | human perception | * |
| dc.title | Sentence Complexity in Context | en |
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| dc.type.full | 04 Contributo in convegno::04.01 Contributo in Atti di convegno | it |
| dc.type.miur | 273 | - |
| dc.type.referee | Sì, ma tipo non specificato | - |
| dc.ugov.descaux1 | 464975 | - |
| iris.orcid.lastModifiedDate | 2024/04/05 00:24:57 | * |
| iris.orcid.lastModifiedMillisecond | 1712269497991 | * |
| iris.scopus.extIssued | 2021 | - |
| iris.scopus.extTitle | Sentence Complexity in Context | - |
| iris.sitodocente.maxattempts | 2 | - |
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| scopus.category | 1203 | * |
| scopus.category | 3616 | * |
| scopus.contributor.affiliation | Istituto Di Linguistica Computazionale Antonio Zampolli | - |
| scopus.contributor.affiliation | Istituto Di Linguistica Computazionale Antonio Zampolli | - |
| scopus.contributor.affiliation | Istituto Di Linguistica Computazionale Antonio Zampolli | - |
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| scopus.contributor.country | Italy | - |
| scopus.contributor.country | Italy | - |
| scopus.contributor.country | Italy | - |
| scopus.contributor.dptid | - | |
| scopus.contributor.dptid | - | |
| scopus.contributor.dptid | - | |
| scopus.contributor.name | Benedetta | - |
| scopus.contributor.name | Dominique | - |
| scopus.contributor.name | Felice | - |
| scopus.contributor.subaffiliation | - | |
| scopus.contributor.subaffiliation | - | |
| scopus.contributor.subaffiliation | - | |
| scopus.contributor.surname | Iavarone | - |
| scopus.contributor.surname | Brunato | - |
| scopus.contributor.surname | Dell'orletta | - |
| scopus.date.issued | 2021 | * |
| scopus.description.abstracteng | 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. | * |
| scopus.description.allpeopleoriginal | Iavarone B.; Brunato D.; Dell'orletta F. | * |
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| scopus.identifier.doi | 10.18653/v1/2021.cmcl-1.23 | * |
| scopus.identifier.isbn | 9781954085350 | * |
| scopus.identifier.pui | 636990649 | * |
| scopus.identifier.scopus | 2-s2.0-85123172973 | * |
| scopus.journal.sourceid | 21101073272 | * |
| scopus.language.iso | eng | * |
| scopus.publisher.name | Association for Computational Linguistics (ACL) | * |
| scopus.relation.conferencedate | 2021 | * |
| scopus.relation.conferencename | 11th Workshop on Cognitive Modeling and Computational Linguistics, CMCL 2021 | * |
| scopus.relation.firstpage | 186 | * |
| scopus.relation.lastpage | 199 | * |
| scopus.title | Sentence Complexity in Context | * |
| scopus.titleeng | Sentence Complexity in Context | * |
| Appare nelle tipologie: | 04.01 Contributo in Atti di convegno | |
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