Multilingual CALL Framework for Automatic Language Exercise Generation from Free Text

Abstract

This paper describes a web-based application to design and answer exercises for language learning. It is available in Basque, Spanish, English, and French. Based on open-source Natural Language Processing (NLP) technology such as word embedding models and word sense disambiguation, the system enables users to create automatically and in real time three types of exercises, namely, Fill-inthe-Gaps, Multiple Choice, and Shuffled Sentences questionnaires. These are generated from texts of the users' own choice, so they can train their language skills with content of their particular interest.

Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)

“…A range of systems integrate the exercises into the base text, yet visual context such as markup elements and images are removed. This is, for instance, the case in the Tutor Assistant's Task Generator (Toole and Heift, 2001), MIRTO (Antoniadis et al, 2004), the Grammar Exercise Generator (GEG) (Melero and Font, 2001), the Language Exercise App (LEA) (Perez and Cuadros, 2017) and COLLIE 7 (Bodnar and Lyster, 2021). Visual context is only preserved in those exercise generation tools implemented as web plugins.…”

Generating and authoring high-variability exercises from authentic texts

“…Closed activity types such as Multiple Choice (MC) are especially popular due to their ability to automatically score the exercises based on the very restricted space of possible learner answers (Tafazoli et al, 2019), yet supported exercise formats vary from one system to the other. A number of tools integrate a variety of different formats: MIRTO automatically generates Fillin-the-Blanks (FiB) as well as Mark-the-Words (MtW) exercises (Antoniadis et al, 2004); Arik-Iturri can generate MC, Error Detection, FiB and Word Formation exercises (Aldabe et al, 2006); an extension of the language aware search Engine FLAIR 1 (Heck and Meurers, 2022b) covers a wide range including FiB, MC, MtW, Memory, Jumbled Sentences and Drag and Drop exercises; Sakumon (Hoshino and Nakagawa, 2008) and Cloze-Fox (Jozef and Sevinc, 2010) support cloze exercises in FiB as well as MC format; WERTi (Meurers et al, 2010) and its multilingual extension View (Reynolds et al, 2014) in addition feature MtW exercises, the Language Exercise App Sentence Shuffling activities (Pérez and Cuadros, 2017), and Ferreira and Pereira Jr. (2018)'s Verb Tenses System True/False and Tense transposition exercises. While these systems can generate multiple exercises for a linguistic structure from the same source document, the actual number of exercises is usually quite limited.…”

“…By varying exercise parameters such as the number of distractors, hints in parentheses, or the span of the target construction, variability can be increased. Notable examples making use of such parameterizations constitute MIRTO which provides parameters for the choice of target constructions, parentheses of FiB exercises and support elements such as reference pages (Antoniadis et al, 2004); the assistant system Sakumon which requires users to manually select target items and distractors from automatically generated suggestions (Hoshino and Nakagawa, 2008); the Language Exercise App where target constructions, distractors and parentheses of FiB exercises are parameterizable (Pérez and Cuadros, 2017); and FLAIR's exercise generation functionality which, in addition to providing parameters for target constructions, distractors and parentheses, allows users to influence the specificity of the exercise instructions (Heck and Meurers, 2022b). However, these systems require users to specify each configuration individually so that generating large numbers of parameterized exercises involves considerable configuration effort as well as manual labour to review the generated exercises for correctness.…”

“…Many exercise generation tools provide support to post-edit the generated exercises, either within the tool (e.g., Toole and Heift, 2001;Hoshino and Nakagawa, 2008) or by providing an interface to general-purpose authoring interfaces such as Hot Potatoes 2 or the LMS Moodle 3 (e.g., Bick, 2000;Aldabe et al, 2006;Pérez and Cuadros, 2017). These interfaces are, however, designed to edit a single exercise at a time.…”

Manipulating the Difficulty of C-Tests

“…Previous work on automatically controlling and manipulating test difficulty has largely focused on multiple-choice tests by generating appropriate distractors (i.e., incorrect solutions). Wojatzki et al (2016) avoid ambiguity of their generated distractors, Hill and Simha (2016) fit them to the context, and Perez and Cuadros (2017) consider multiple languages. Further work by Zesch and Melamud (2014), Beinborn (2016), and Lee and Luo (2016) employ word difficulty, lexical substitution, and the learner's answer history to control distractor difficulty.…”