@inproceedings{pal-etal-2022-weakly,
title = "Weakly Supervised Context-based Interview Question Generation",
author = "Pal, Samiran and
Khan, Kaamraan and
Singh, Avinash Kumar and
Ghosh, Subhasish and
Nayak, Tapas and
Palshikar, Girish and
Bhattacharya, Indrajit",
editor = "Bosselut, Antoine and
Chandu, Khyathi and
Dhole, Kaustubh and
Gangal, Varun and
Gehrmann, Sebastian and
Jernite, Yacine and
Novikova, Jekaterina and
Perez-Beltrachini, Laura",
booktitle = "Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.gem-1.4",
doi = "10.18653/v1/2022.gem-1.4",
pages = "43--53",
abstract = "We explore the task of automated generation of technical interview questions from a given textbook. Such questions are different from those for reading comprehension studied in question generation literature. We curate a context based interview questions data set for Machine Learning and Deep Learning from two popular textbooks. We first explore the possibility of using a large generative language model (GPT-3) for this task in a zero shot setting. We then evaluate the performance of smaller generative models such as BART fine-tuned on weakly supervised data obtained using GPT-3 and hand-crafted templates. We deploy an automatic question importance assignment technique to figure out suitability of a question in a technical interview. It improves the evaluation results in many dimensions. We dissect the performance of these models for this task and also scrutinize the suitability of questions generated by them for use in technical interviews.",
}
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%0 Conference Proceedings
%T Weakly Supervised Context-based Interview Question Generation
%A Pal, Samiran
%A Khan, Kaamraan
%A Singh, Avinash Kumar
%A Ghosh, Subhasish
%A Nayak, Tapas
%A Palshikar, Girish
%A Bhattacharya, Indrajit
%Y Bosselut, Antoine
%Y Chandu, Khyathi
%Y Dhole, Kaustubh
%Y Gangal, Varun
%Y Gehrmann, Sebastian
%Y Jernite, Yacine
%Y Novikova, Jekaterina
%Y Perez-Beltrachini, Laura
%S Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F pal-etal-2022-weakly
%X We explore the task of automated generation of technical interview questions from a given textbook. Such questions are different from those for reading comprehension studied in question generation literature. We curate a context based interview questions data set for Machine Learning and Deep Learning from two popular textbooks. We first explore the possibility of using a large generative language model (GPT-3) for this task in a zero shot setting. We then evaluate the performance of smaller generative models such as BART fine-tuned on weakly supervised data obtained using GPT-3 and hand-crafted templates. We deploy an automatic question importance assignment technique to figure out suitability of a question in a technical interview. It improves the evaluation results in many dimensions. We dissect the performance of these models for this task and also scrutinize the suitability of questions generated by them for use in technical interviews.
%R 10.18653/v1/2022.gem-1.4
%U https://aclanthology.org/2022.gem-1.4
%U https://doi.org/10.18653/v1/2022.gem-1.4
%P 43-53
Markdown (Informal)
[Weakly Supervised Context-based Interview Question Generation](https://aclanthology.org/2022.gem-1.4) (Pal et al., GEM 2022)
ACL
- Samiran Pal, Kaamraan Khan, Avinash Kumar Singh, Subhasish Ghosh, Tapas Nayak, Girish Palshikar, and Indrajit Bhattacharya. 2022. Weakly Supervised Context-based Interview Question Generation. In Proceedings of the 2nd Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), pages 43–53, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.