Abstract
Question generation (QG) from technical text has multiple important applications such as creation of question-banks for examinations, interviews as well as in intelligent tutoring systems. However, much of the existing work for QG has focused on open-domain and not specifically on technical domain. We propose to generate technical questions using semantic templates. We also focus on ensuring that a large fraction of the generated questions are long-form, i.e., they require longer answers spanning multiple sentences. This is in contrast with existing work which has predominantly focused on generating factoid questions which have a few words or phrases as answers. Using the technical topics selected from undergraduate and graduate-level courses in Computer Science, we show that the proposed approach is able to generate questions with high acceptance rate. Further, we also show that the proposed template-based approach can be effectively leveraged using the distant supervision paradigm to finetune and significantly improve the existing sequence-to-sequence deep learning models for generating long-form, technical questions.
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Pal, S., Singh, A., Datta, S., Patil, S., Bhattacharya, I., Palshikar, G. (2021). Semantic Templates for Generating Long-Form Technical Questions. In: EkÅ¡tein, K., Pártl, F., KonopÃk, M. (eds) Text, Speech, and Dialogue. TSD 2021. Lecture Notes in Computer Science(), vol 12848. Springer, Cham. https://doi.org/10.1007/978-3-030-83527-9_20
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