Assessing Relative Sentence Complexity using an Incremental CCG Parser

Bharat Ram Ambati, Siva Reddy, Mark Steedman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Given a pair of sentences, we present computational models to assess if one sentence is simpler to read than the other. While existing models explored the usage of phrase structure features using a non-incremental parser, experimental evidence suggests that the human language processor works incrementally.
We empirically evaluate if syntactic features from an incremental CCG
parser are more useful than features from a non-incremental phrase structure parser. Our evaluation on Simple and Standard Wikipedia sentence pairs suggests that incremental CCG features are indeed more useful than phrase structure features achieving 0.44 points gain in performance. Incremental
CCG parser also gives significant improvements in speed (12 times faster) in comparison to the phrase structure parser. Furthermore, with the addition of psycholinguistic features, we achieve the strongest result to date reported
on this task. Our code and data can be downloaded from https://github.
com/bharatambati/sent-compl.
Original languageEnglish
Title of host publicationProceedings of NAACL-HLT 2016
PublisherAssociation for Computational Linguistics
Pages1051-1057
Number of pages7
ISBN (Print)978-1-941643-91-4
Publication statusPublished - 2016
Event15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - San Diego, United States
Duration: 12 Jun 201617 Jun 2016
http://naacl.org/naacl-hlt-2016/
http://naacl.org/naacl-hlt-2016/

Conference

Conference15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Abbreviated titleNAACL HLT 2016
Country/TerritoryUnited States
CitySan Diego
Period12/06/1617/06/16
Internet address

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