Arpita Kundu


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Unsupervised Generation of Long-form Technical Questions from Textbook Metadata using Structured Templates
Indrajit Bhattacharya | Subhasish Ghosh | Arpita Kundu | Pratik Saini | Tapas Nayak
Proceedings of the First Workshop on Pattern-based Approaches to NLP in the Age of Deep Learning

We explore the task of generating long-form technical questions from textbooks. Semi-structured metadata of a textbook — the table of contents and the index — provide rich cues for technical question generation. Existing literature for long-form question generation focuses mostly on reading comprehension assessment, and does not use semi-structured metadata for question generation. We design unsupervised template based algorithms for generating questions based on structural and contextual patterns in the index and ToC. We evaluate our approach on textbooks on diverse subjects and show that our approach generates high quality questions of diverse types. We show that, in comparison, zero-shot question generation using pre-trained LLMs on the same meta-data has much poorer quality.

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A Weak Supervision Approach for Predicting Difficulty of Technical Interview Questions
Arpita Kundu | Subhasish Ghosh | Pratik Saini | Tapas Nayak | Indrajit Bhattacharya
Proceedings of the 29th International Conference on Computational Linguistics

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.


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Discovering Knowledge Graph Schema from Short Natural Language Text via Dialog
Subhasis Ghosh | Arpita Kundu | Aniket Pramanick | Indrajit Bhattacharya
Proceedings of the 21th Annual Meeting of the Special Interest Group on Discourse and Dialogue

We study the problem of schema discovery for knowledge graphs. We propose a solution where an agent engages in multi-turn dialog with an expert for this purpose. Each mini-dialog focuses on a short natural language statement, and looks to elicit the expert’s desired schema-based interpretation of that statement, taking into account possible augmentations to the schema. The overall schema evolves by performing dialog over a collection of such statements. We take into account the probability that the expert does not respond to a query, and model this probability as a function of the complexity of the query. For such mini-dialogs with response uncertainty, we propose a dialog strategy that looks to elicit the schema over as short a dialog as possible. By combining the notion of uncertainty sampling from active learning with generalized binary search, the strategy asks the query with the highest expected reduction of entropy. We show that this significantly reduces dialog complexity while engaging the expert in meaningful dialog.