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        <identifier><![CDATA[oai:it.cnr:prodotti:oai:it.cnr:prodotti:442040]]></identifier>
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          <dc:type><![CDATA[info:eu-repo/semantics/conferenceObject]]></dc:type>
          <dc:type><![CDATA[Contributo in atti di convegno]]></dc:type>
          <dc:title><![CDATA[Is Neural Language Model Perplexity Related to Readability?]]></dc:title>
          <dc:creator><![CDATA[Miaschi]]></dc:creator>
          <dc:creator><![CDATA[Alessio]]></dc:creator>
          <dc:creator><![CDATA[Alzetta]]></dc:creator>
          <dc:creator><![CDATA[Chiara]]></dc:creator>
          <dc:creator><![CDATA[Brunato]]></dc:creator>
          <dc:creator><![CDATA[Dominique]]></dc:creator>
          <dc:creator><![CDATA[Dell'Orletta]]></dc:creator>
          <dc:creator><![CDATA[Felice]]></dc:creator>
          <dc:creator><![CDATA[Venturi]]></dc:creator>
          <dc:creator><![CDATA[Giulia]]></dc:creator>
          <dc:language><![CDATA[eng]]></dc:language>
          <dc:description><![CDATA[This paper explores the relationship between Neural Language Model (NLM) perplexity and sentence readability. Starting from the evidence that NLMs implicitly acquire sophisticated linguistic knowledge from a huge amount of training data, our goal is to investigate whether perplexity is affected by linguistic features used to automatically assess sentence readability and if there is a correlation between the two metrics. Our findings suggest that this correlation is actually quite weak and the two metrics are affected by different linguistic phenomena.]]></dc:description>
          <dc:source><![CDATA[Seventh Italian Conference on Computational Linguistics, 01-03/03/2021]]></dc:source>
          <dc:source><![CDATA[info:cnr-pdr/source/autori:Miaschi, Alessio and Alzetta, Chiara and Brunato, Dominique and Dell'Orletta, Felice and Venturi, Giulia/congresso_nome:Seventh Italian Conference on Computational Linguistics/congresso_luogo:/congresso_data:01-03%2F03%2F2021/anno:2020/pagina_da:/pagina_a:/intervallo_pagine:]]></dc:source>
          <dc:date><![CDATA[2020]]></dc:date>
          <dc:identifier><![CDATA[http://www.cnr.it/prodotto/i/442040]]></dc:identifier>
          <dc:identifier><![CDATA[https://publications.cnr.it/doc/442040]]></dc:identifier>
          <dc:identifier><![CDATA[http://ceur-ws.org/Vol-2769/paper_57.pdf]]></dc:identifier>
          <dc:identifier><![CDATA[urn:isbn:979-12-80136-28-2]]></dc:identifier>
          <dc:relation><![CDATA[info:cnr-pdr/author/idpersonaleesterno:22947/BRUNATO/DOMINIQUE PIERINA]]></dc:relation>
          <dc:relation><![CDATA[info:cnr-pdr/author/idpersonaleesterno:34242/MIASCHI/ALESSIO]]></dc:relation>
          <dc:relation><![CDATA[info:cnr-pdr/author/idpersonaleesterno:34247/ALZETTA/CHIARA]]></dc:relation>
          <dc:relation><![CDATA[info:cnr-pdr/author/matricola:14329/DELL'ORLETTA/FELICE]]></dc:relation>
          <dc:relation><![CDATA[info:cnr-pdr/author/matricola:17692/VENTURI/GIULIA]]></dc:relation>
          <dc:subject><![CDATA[nlp]]></dc:subject>
          <dc:subject><![CDATA[neural language models]]></dc:subject>
          <dc:subject><![CDATA[readability]]></dc:subject>
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