La perplejidad como herramienta para estimar la asignación de nivel de competencia en escritos de una lengua extranjera

  1. Mata, Gadea
  2. Rubio, Julio
  3. Agustín Llach, María del Pilar
  4. Heras, Jonathan
Revue:
Procesamiento del lenguaje natural

ISSN: 1135-5948

Année de publication: 2023

Número: 71

Pages: 29-38

Type: Article

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Résumé

The allocation of proficiency levels to utterances written by foreign language learners is a subjective task. Therefore, the development of methods to automatically evaluate written sentences can help both students and teachers. In this work, we have explored two different approaches to tackle this task by using the corpus CAES, which contains written utterances of learners of Spanish labelled with CEFR levels (up to C1). The first approach is a deep learning model called Deep-ELE which assigns proficiency levels to sentences. The second approach consists in studying the perplexity of sentences written by students of different levels, to later allocate levels to those sentences based on such an analysis. Both approaches have been evaluated, and results confirm that they can be used to successfully classify written sentences into proficiency levels. In particular, the Deep-ELE model reaches an accuracy of 81.3% and a weighted Cohen Kappa of 0.83. As a conclusion, this work is a step towards better understanding how natural language processing methods can help learners of a second language.

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