Creating high-quality multiple-choice items requires careful attention to several factors, including ensuring that there is only one correct option, that options are independent of each other, that there is no overlap between options, and that each option is plausible. This attention is reflected in the explanations provided by human item-writers for each option. This study aimed to compare the creation of explanations of multiple-choice item options for reading comprehension by ChatGPT with those created by humans. We used two context-dependent multiple-choice item sets created based on EvidenceCentered Design. Results indicate that ChatGPT is capable of producing explanations with different type of information that are comparable to those created by humans. So that humans could benefit from additional information given to enhance their explanations. We conclude that ChatGPT ability to generate explanations for multiple-choice item options in reading comprehension tests is comparable to that of humans.

The RUFINO team proposed a non-supervised, conceptually-simple and low-cost approach for addressing the Multilingual and Cross-lingual Semantic Word Similarity challenge at SemEval 2017. The proposed systems were cross-lingual extensions of popular monolingual lexical similarity approaches such as PMI and word2vec. The extensions were possible by means of a small parallel list of concepts similar to the Swadesh’s list, which we obtained in a semi-automatic way. In spite of its simplicity, our approach showed to be effective obtaining statistically-significant and consistent results in all datasets proposed for the task. Besides, we provide some research directions for improving this novel and affordable approach.