A Weak Supervision Approach for Predicting Difficulty of Technical Interview Questions

Arpita Kundu, Subhasish Ghosh, Pratik Saini, Tapas Nayak, Indrajit Bhattacharya


Abstract
Predicting difficulty of questions is crucial for technical interviews. However, such questions are long-form and more open-ended than factoid and multiple choice questions explored so far for question difficulty prediction. Existing models also require large volumes of candidate response data for training. We study weak-supervision and use unsupervised algorithms for both question generation and difficulty prediction. We create a dataset of interview questions with difficulty scores for deep learning and use it to evaluate SOTA models for question difficulty prediction trained using weak supervision. Our analysis brings out the task’s difficulty as well as the promise of weak supervision for it.
Anthology ID:
2022.coling-1.400
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
4537–4543
Language:
URL:
https://aclanthology.org/2022.coling-1.400
DOI:
Bibkey:
Cite (ACL):
Arpita Kundu, Subhasish Ghosh, Pratik Saini, Tapas Nayak, and Indrajit Bhattacharya. 2022. A Weak Supervision Approach for Predicting Difficulty of Technical Interview Questions. In Proceedings of the 29th International Conference on Computational Linguistics, pages 4537–4543, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
A Weak Supervision Approach for Predicting Difficulty of Technical Interview Questions (Kundu et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.400.pdf