Abstract
Rich and diverse knowledge bases (KB) are foundational building blocks for online knowledge sharing communities such as StackOverflow and Quora, and applications such as conversational assistants (aka chatbots). A popular format for knowledge bases is question-answer pairs (or FAQs), where questions are designed to accurately match a multitude of queries. In this paper, we address the problem of automatic creation of such Q&A-based knowledge bases from domain-specific, long-form textual content (e.g., web articles). Specifically, we consider the problem of question generation, which is the task of generating questions given a paragraph of text as input, with a goal to achieve both diversity and fidelity of the generated questions. Towards this goal we propose PROTEGE, a diverse question generation framework which consists of (1) a novel encoder-decoder based Large Language Model (LLM) architecture which can take a variety of prompts and generate a diverse set of candidate questions, and (2) a hill-climbing algorithm that maximizes a sub-modular objective function to balance diversity with fidelity. Through our experiments on three popular public Q&A datasets, we demonstrate that PROTEGE improves diversity by +16% and fidelity by +8% over diverse beam search and prompt-based baselines.- Anthology ID:
- 2023.findings-emnlp.362
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5449–5463
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.362
- DOI:
- 10.18653/v1/2023.findings-emnlp.362
- Cite (ACL):
- Vinayak Puranik, Anirban Majumder, and Vineet Chaoji. 2023. PROTEGE: Prompt-based Diverse Question Generation from Web Articles. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 5449–5463, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- PROTEGE: Prompt-based Diverse Question Generation from Web Articles (Puranik et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.findings-emnlp.362.pdf