Tracking the Evolution of Written Language Competence in L2 Spanish Learners
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
CItA: an L1 Italian Learners Corpus to Study the Development of Writing Competence
In this paper, we present the CItA corpus (Corpus Italiano di Apprendenti L1), a collection of essays written by Italian L1 learners collected during the first and second year of lower secondary school. The corpus was bu…
On the Nature of BERT: Correlating Fine-Tuning and Linguistic Competence
Several studies in the literature on the interpretation of Neural Language Models (NLM) focus on the linguistic generalization abilities of pre-trained models. However, little attention is paid to how the linguistic know…
Batch Clustering for Multilingual News Streaming
Nowadays, digital news articles are widely available, published by various editors and often written in different languages. This large volume of diverse and unorganized information makes human reading very difficult or …
ArticlesClusteringEvaluating Large Language Models with Tests of Spanish as a Foreign Language: Pass or Fail?
Large Language Models (LLMs) have been profusely evaluated on their ability to answer questions on many topics and their performance on different natural language understanding tasks. Those tests are usually conducted in…
Natural Language UnderstandingReading ComprehensionSeventeenth-Century Spanish American Notary Records for Fine-Tuning Spanish Large Language Models
Large language models have gained tremendous popularity in domains such as e-commerce, finance, healthcare, and education. Fine-tuning is a common approach to customize an LLM on a domain-specific dataset for a desired d…
Language ModelingLanguage ModellingMasked Language Modeling