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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Chen, W., Aist, G., Mostow, J.: Generating questions automatically from informational text. In: 2nd Workshop on Question Generation (2009)
Curto, S., Mendes, A.C., Coheur, L.: Exploring linguistically-rich patterns for question generation. In: UCNLG+Eval: Language Generation and Evaluation Workshop (2011)
Du, X., Shao, J., Cardie, C.: Learning to ask: neural question generation for reading comprehension. In: 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2017)
Fan, Z., Wei, Z., Wang, S., Liu, Y., Huang, X.: A reinforcement learning framework for natural question generation using bi-discriminators. In: 27th International Conference on Computational Linguistics (COLING) (2018)
Ferragina, P., Scaiella, U.: Fast and accurate annotation of short texts with Wikipedia pages. IEEE Softw. 29(1), 70–75 (2012)
Heilman, M., Smith, N.A.: Good question! statistical ranking for question generation. In: Human Language Technologies: 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics (2010)
Hosking, T., Riedel, S.: Evaluating rewards for question generation models. In: NAACL-HLT (2019)
Kalady, S., Elikkottil, A., Das, R.: Natural language question generation using syntax and keywords. In: 3rd Workshop on Question Generation (2010)
Kilgarriff, A.: Comparing corpora. Int. J. Corpus Linguist. 6, 97–133 (2001)
Kumar, V., Ramakrishnan, G., Li, Y.F.: Putting the horse before the cart: a generator-evaluator framework for question generation from text. In: 23rd Conference on Computational Natural Language Learning (2019)
Kurdi, G., Leo, J., Parsia, B., Sattler, U., Al-Emari, S.: A systematic review of automatic question generation for educational purposes. Int. J. Artif. Intell. Educ. 30(1), 121–204 (2019). https://doi.org/10.1007/s40593-019-00186-y
Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the ACL (2020)
Liu, B., et al.: Learning to generate questions by learning what not to generate. In: WWW (2019)
Liu, B., Wei, H., Niu, D., Chen, H., He, Y.: Asking questions the human way: scalable question-answer generation from text corpus. In: WWW (2020)
Lopez, L.E., Cruz, D.K., Cruz, J.C.B., Cheng, C.: Transformer-based end-to-end question generation (2021)
Mannem, P., Prasad, R., Joshi, A.: Question generation from paragraphs at UPenn: QGSTEC system description. In: 3rd Workshop on Question Generation, QG 2000 (2000)
Mishra, S.K., Goel, P., Sharma, A., Jagannatha, A., Jacobs, D., Daume, H.: Towards automatic generation of questions from long answers. arXiv:2004.05109 (2020)
Pawar, S., Palshikar, G.K., Bhattacharyya, P.: Relation extraction: a survey. arxiv:1712.05191 (2017)
Rajpurkar, P., Zhang, J., Lopyrev, K., Liang, P.: SQuAD: 100,000+ questions for machine comprehension of text. In: EMNLP (2016)
Rao, S., Daume, H.: Answer-based adversarial training for generating clarification questions. In: NAACL-HLT (2019)
Šajatović, A., Buljan, M., Šnajder, J., Dalbelo Bašić, B.: Evaluating automatic term extraction methods on individual documents. In: Joint Workshop on Multiword Expressions and WordNet, MWE-WN 2019 (2019)
Serban, I.V., et al.: Generating factoid questions with recurrent neural networks: the 30M factoid question-answer corpus. In: 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2016)
Stefano, F., Finocchi, I., Ponzetto, S.P., Paola, V.: Efficient pruning of large knowledge graphs. In: IJCAI (2018)
Wyse, B., Piwek, P.: Generating questions from OpenLearn study units. In: 2nd Workshop on Question Generation (2009)
Yao, K., Zhang, L., Luo, T., Tao, L., Wu, Y.: Teaching machines to ask questions. In: IJCAI (2018)
Zhao, Y., Ni, X., Ding, Y., Ke, Q.: Paragraph-level neural question generation with maxout pointer and gated self-attention networks. In: EMNLP (2018)
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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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