Arianna Masciolini


2023

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Towards automatically extracting morphosyntactical error patterns from L1-L2 parallel dependency treebanks
Arianna Masciolini | Elena Volodina | Dana Dannlls
Proceedings of the 18th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2023)

L1-L2 parallel dependency treebanks are UD-annotated corpora of learner sentences paired with correction hypotheses. Automatic morphosyntactical annotation has the potential to remove the need for explicit manual error tagging and improve interoperability, but makes it more challenging to locate grammatical errors in the resulting datasets. We therefore propose a novel method for automatically extracting morphosyntactical error patterns and perform a preliminary bilingual evaluation of its first implementation through a similar example retrieval task. The resulting pipeline is also available as a prototype CALL application.

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A query engine for L1-L2 parallel dependency treebanks
Arianna Masciolini
Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)

L1-L2 parallel dependency treebanks are learner corpora with interoperability as their main design goal. They consist of sentences produced by learners of a second language (L2) paired with native-like (L1) correction hypotheses. Rather than explicitly labelled for errors, these are annotated following the Universal Dependencies standard. This implies relying on tree queries for error retrieval. Work in this direction is, however, limited. We present a query engine for L1-L2 treebanks and evaluate it on two corpora, one manually validated and one automatically parsed.

2021

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Grammar-Based Concept Alignment for Domain-Specific Machine Translation
Arianna Masciolini | Aarne Ranta
Proceedings of the Seventh International Workshop on Controlled Natural Language (CNL 2020/21)