Tehnički vjesnik, Vol. 28 No. 3, 2021.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20200402175619
Automatic Question Generation Using Semantic Role Labeling for Morphologically Rich Languages
Daniel Vasić*
orcid.org/0000-0002-7713-8396
; University of Mostar, Faculty of Science and Education, 88000 Mostar, Bosnia and Herzegovina
Branko Žitko
orcid.org/0000-0001-8946-0916
; University of Split, Faculty of Science, 10000 Split, Croatia
Hrvoje Ljubić
orcid.org/0000-0001-8995-5855
; University of Mostar, Faculty of Science and Education, 88000 Mostar, Bosnia and Herzegovina
Sažetak
In this paper, a novel approach to automatic question generation (AQG) using semantic role labeling (SRL) for morphologically rich languages is presented. A model for AQG is developed for our native speaking language, Croatian. Croatian language is a highly inflected language that belongs to Balto-Slavic family of languages. Globally this article can be divided into two stages. In the first stage we present a novel approach to SRL of texts written in Croatian language that uses Conditional Random Fields (CRF). SRL traditionally consists of predicate disambiguation, argument identification and argument classification. After these steps most approaches use beam search to find optimal sequence of arguments based on given predicate. We propose the architecture for predicate identification and argument classification in which finding the best sequence of arguments is handled by Viterbi decoding. We enrich SRL features with custom attributes that are custom made for this language. Our SRL system achieves F1 score of 78% in argument classification step on Croatian hr 500k corpus. In the second stage the proposed SRL model is used to develop AQG system for question generation from texts written in Croatian language. We proposed custom templates for AQG that were used to generate a total of 628 questions which were evaluated by experts scoring every question on a Likert scale. Expert evaluation of the system showed that our AQG achieved good results. The evaluation showed that 68% of the generated questions could be used for educational purposes. With these results the proposed AQG system could be used for possible implementation inside educational systems such as Intelligent Tutoring Systems.
Ključne riječi
automatic question generation; morphologically rich languages; natural language processing; semantic role labeling
Hrčak ID:
258118
URI
Datum izdavanja:
6.6.2021.
Posjeta: 1.691 *