Arianna Masciolini


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Synthetic-Error Augmented Parsing of Swedish as a Second Language: Experiments with Word Order
Arianna Masciolini | Emilie Francis | Maria Irena Szawerna
Proceedings of the Joint Workshop on Multiword Expressions and Universal Dependencies (MWE-UD) @ LREC-COLING 2024

Ungrammatical text poses significant challenges for off-the-shelf dependency parsers. In this paper, we explore the effectiveness of using synthetic data to improve performance on essays written by learners of Swedish as a second language. Due to their relevance and ease of annotation, we restrict our initial experiments to word order errors. To do that, we build a corrupted version of the standard Swedish Universal Dependencies (UD) treebank Talbanken, mimicking the error patterns and frequency distributions observed in the Swedish Learner Language (SweLL) corpus. We then use the MaChAmp (Massive Choice, Ample tasks) toolkit to train an array of BERT-based dependency parsers, fine-tuning on different combinations of original and corrupted data. We evaluate the resulting models not only on their respective test sets but also, most importantly, on a smaller collection of sentence-correction pairs derived from SweLL. Results show small but significant performance improvements on the target domain, with minimal decline on normative data.

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Bootstrapping the Annotation of UD Learner Treebanks
Arianna Masciolini
Proceedings of the 17th Workshop on Building and Using Comparable Corpora (BUCC) @ LREC-COLING 2024


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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.


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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)