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    <abstract>Coherence modeling is an important task in natural language processing (NLP) with potential impact on other NLP taskssuch as Natural Language Understanding or Automated Essay Scoring. But it can also offer interesting linguistic insightswith pedagogical implications. Early work on coherence modeling has focused on exploring definitions of the phenomenonand in recent years, neural models have entered also this field of research allowing to successfully distinguish coherent fromincoherent (synthetically created) texts or to identify the correct continuation for a given sample of texts as demonstratedfor Italian in the DisCoTex task of EVALITA 2023. In this article, we target coherence modeling for Italian language in astrongly domain-specific scenario, i.e. education. We use a corpus of student essays, collected to analyse student‘s textcoherence and data augmentation techniques to experiment with the effect of various linguistically informed features ofincoherent writing on current coherence modelling strategies used in NLP. Our results show the capabilities of encodermodels to capture features of (in)coherence in a domain-specific scenario discerning natural from artificially corrupted texts.Our code is available at the following url https://gitlab.inf.unibz.it/commul/itaca/automatic_eval</abstract>
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%0 Conference Proceedings
%T Towards an Automatic Evaluation of (In)coherence in Student Essays
%A Pellegrino, Filippo
%A Frey, Jennifer
%A Zanasi, Lorenzo
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F pellegrino-etal-2024-towards
%X Coherence modeling is an important task in natural language processing (NLP) with potential impact on other NLP taskssuch as Natural Language Understanding or Automated Essay Scoring. But it can also offer interesting linguistic insightswith pedagogical implications. Early work on coherence modeling has focused on exploring definitions of the phenomenonand in recent years, neural models have entered also this field of research allowing to successfully distinguish coherent fromincoherent (synthetically created) texts or to identify the correct continuation for a given sample of texts as demonstratedfor Italian in the DisCoTex task of EVALITA 2023. In this article, we target coherence modeling for Italian language in astrongly domain-specific scenario, i.e. education. We use a corpus of student essays, collected to analyse student‘s textcoherence and data augmentation techniques to experiment with the effect of various linguistically informed features ofincoherent writing on current coherence modelling strategies used in NLP. Our results show the capabilities of encodermodels to capture features of (in)coherence in a domain-specific scenario discerning natural from artificially corrupted texts.Our code is available at the following url https://gitlab.inf.unibz.it/commul/itaca/automatic_eval
%U https://aclanthology.org/2024.clicit-1.82/
%P 757-765

Markdown (Informal)

[Towards an Automatic Evaluation of (In)coherence in Student Essays](https://aclanthology.org/2024.clicit-1.82/) (Pellegrino et al., CLiC-it 2024)

• Towards an Automatic Evaluation of (In)coherence in Student Essays (Pellegrino et al., CLiC-it 2024)

ACL

• Filippo Pellegrino, Jennifer Frey, and Lorenzo Zanasi. 2024. Towards an Automatic Evaluation of (In)coherence in Student Essays. In Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024), pages 757–765, Pisa, Italy. CEUR Workshop Proceedings.