Alessio Miaschi


2020

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Tracking the Evolution of Written Language Competence in L2 Spanish Learners
Alessio Miaschi | Sam Davidson | Dominique Brunato | Felice Dell’Orletta | Kenji Sagae | Claudia Helena Sanchez-Gutierrez | Giulia Venturi
Proceedings of the Fifteenth Workshop on Innovative Use of NLP for Building Educational Applications

In this paper we present an NLP-based approach for tracking the evolution of written language competence in L2 Spanish learners using a wide range of linguistic features automatically extracted from students’ written productions. Beyond reporting classification results for different scenarios, we explore the connection between the most predictive features and the teaching curriculum, finding that our set of linguistic features often reflect the explicit instructions that students receive during each course.

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Linguistic Profiling of a Neural Language Model
Alessio Miaschi | Dominique Brunato | Felice Dell’Orletta | Giulia Venturi
Proceedings of the 28th International Conference on Computational Linguistics

In this paper we investigate the linguistic knowledge learned by a Neural Language Model (NLM) before and after a fine-tuning process and how this knowledge affects its predictions during several classification problems. We use a wide set of probing tasks, each of which corresponds to a distinct sentence-level feature extracted from different levels of linguistic annotation. We show that BERT is able to encode a wide range of linguistic characteristics, but it tends to lose this information when trained on specific downstream tasks. We also find that BERT’s capacity to encode different kind of linguistic properties has a positive influence on its predictions: the more it stores readable linguistic information of a sentence, the higher will be its capacity of predicting the expected label assigned to that sentence.

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Contextual and Non-Contextual Word Embeddings: an in-depth Linguistic Investigation
Alessio Miaschi | Felice Dell’Orletta
Proceedings of the 5th Workshop on Representation Learning for NLP

In this paper we present a comparison between the linguistic knowledge encoded in the internal representations of a contextual Language Model (BERT) and a contextual-independent one (Word2vec). We use a wide set of probing tasks, each of which corresponds to a distinct sentence-level feature extracted from different levels of linguistic annotation. We show that, although BERT is capable of understanding the full context of each word in an input sequence, the implicit knowledge encoded in its aggregated sentence representations is still comparable to that of a contextual-independent model. We also find that BERT is able to encode sentence-level properties even within single-word embeddings, obtaining comparable or even superior results than those obtained with sentence representations.

2019

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Linguistically-Driven Strategy for Concept Prerequisites Learning on Italian
Alessio Miaschi | Chiara Alzetta | Franco Alberto Cardillo | Felice Dell’Orletta
Proceedings of the Fourteenth Workshop on Innovative Use of NLP for Building Educational Applications

We present a new concept prerequisite learning method for Learning Object (LO) ordering that exploits only linguistic features extracted from textual educational resources. The method was tested in a cross- and in- domain scenario both for Italian and English. Additionally, we performed experiments based on a incremental training strategy to study the impact of the training set size on the classifier performances. The paper also introduces ITA-PREREQ, to the best of our knowledge the first Italian dataset annotated with prerequisite relations between pairs of educational concepts, and describe the automatic strategy devised to build it.