# ISEEQ: Information Seeking Question Generation using Dynamic Meta-Information Retrieval and Knowledge Graphs

arXiv: https://arxiv.org/abs/2112.07622 · HTML: https://mlsanity.com/paper/2112.07622/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 32 of its 44 references that are in the map.
- First posted on arXiv 13 Dec 2021.

## Builds on (most cited first, 30 of 32)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering](https://mlsanity.com/paper/1809.09600/) (2018)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [REALM: Retrieval-Augmented Language Model Pre-Training](https://mlsanity.com/paper/2002.08909/) (2020)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [ConceptNet 5.5: An Open Multilingual Graph of General Knowledge](https://mlsanity.com/paper/1612.03975/) (2016)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [ProphetNet: Predicting Future N-gram for Sequence-to-Sequence Pre-training](https://mlsanity.com/paper/2001.04063/) (2020)
- [Constituency Parsing with a Self-Attentive Encoder](https://mlsanity.com/paper/1805.01052/) (2018)
- [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019)
- [Learning to Ask Good Questions: Ranking Clarification Questions using Neural Expected Value of Perfect Information](https://mlsanity.com/paper/1805.04655/) (2018)
- [Interpretation of Natural Language Rules in Conversational Machine Reading](https://mlsanity.com/paper/1809.01494/) (2018)
- [Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering](https://mlsanity.com/paper/1909.06356/) (2019)
- [Improving Coreference Resolution by Learning Entity-Level Distributed Representations](https://mlsanity.com/paper/1606.01323/) (2016)
- [Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models](https://mlsanity.com/paper/2004.01909/) (2020)
- [Conversations Are Not Flat: Modeling the Dynamic Information Flow across Dialogue Utterances](https://mlsanity.com/paper/2106.02227/) (2021)
- [QADiscourse -- Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines](https://mlsanity.com/paper/2010.02815/) (2020)
- [Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading](https://mlsanity.com/paper/2010.01838/) (2020)
- [Knowledge-intensive Language Understanding for Explainable AI](https://mlsanity.com/paper/2108.01174/) (2021)
- [Crowdsourcing Question-Answer Meaning Representations](https://mlsanity.com/paper/1711.05885/) (2017)
- [WikiPassageQA: A Benchmark Collection for Research on Non-factoid Answer Passage Retrieval](https://mlsanity.com/paper/1805.03797/) (2018)
- [Contrastive Multi-document Question Generation](https://mlsanity.com/paper/1911.03047/) (2019)
- [Information Seeking in the Spirit of Learning: a Dataset for Conversational Curiosity](https://mlsanity.com/paper/2005.00172/) (2020)
- [A Large-Scale Analysis of Mixed Initiative in Information-Seeking Dialogues for Conversational Search](https://mlsanity.com/paper/2104.07096/) (2021)
- [Macaw: An Extensible Conversational Information Seeking Platform](https://mlsanity.com/paper/1912.08904/) (2019)
- [Trusting RoBERTa over BERT: Insights from CheckListing the Natural Language Inference Task](https://mlsanity.com/paper/2107.07229/) (2021)

## Built on by (most cited first, 22 of 22)

- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [All You May Need for VQA are Image Captions](https://mlsanity.com/paper/2205.01883/) (2022)
- [Knowledge Graph Embedding: A Survey from the Perspective of Representation Spaces](https://mlsanity.com/paper/2211.03536/) (2022)
- [Learning to Automate Follow-up Question Generation using Process Knowledge for Depression Triage on Reddit Posts](https://mlsanity.com/paper/2205.13884/) (2022)
- [ConfusedPilot: Confused Deputy Risks in RAG-based LLMs](https://mlsanity.com/paper/2408.04870/) (2024)
- [Utilizing BERT for Information Retrieval: Survey, Applications, Resources, and Challenges](https://mlsanity.com/paper/2403.00784/) (2024)
- [Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety](https://mlsanity.com/paper/2206.13349/) (2022)
- [Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety](https://mlsanity.com/paper/2312.06798/) (2023)
- [Synergistic Interplay between Search and Large Language Models for Information Retrieval](https://mlsanity.com/paper/2305.07402/) (2023)
- [ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance](https://mlsanity.com/paper/2305.08010/) (2023)
- [A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression](https://mlsanity.com/paper/2311.13852/) (2023)
- [Dynamic Contexts for Generating Suggestion Questions in RAG Based Conversational Systems](https://mlsanity.com/paper/2403.11413/) (2024)
- [MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation](https://mlsanity.com/paper/2603.23234/) (2026)
- [Thread: A Logic-Based Data Organization Paradigm for How-To Question Answering with Retrieval Augmented Generation](https://mlsanity.com/paper/2406.13372/) (2024)
- [Ranking Free RAG: Replacing Re-ranking with Selection in RAG for Sensitive Domains](https://mlsanity.com/paper/2505.16014/) (2025)
- [Towards Explainable and Safe Conversational Agents for Mental Health: A Survey](https://mlsanity.com/paper/2304.13191/) (2023)
- [LonXplain: Lonesomeness as a Consequence of Mental Disturbance in Reddit Posts](https://mlsanity.com/paper/2305.18736/) (2023)
- [A Survey on Conversational Search and Applications in Biomedicine](https://mlsanity.com/paper/2211.15328/) (2022)
- [Selecting Query-bag as Pseudo Relevance Feedback for Information-seeking Conversations](https://mlsanity.com/paper/2404.04272/) (2024)
- [IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being](https://mlsanity.com/paper/2406.13791/) (2024)
- [SelfGraphRAG: Bridging the Supervision Gap in Graph-Based RAG with Synthetic QA Generation](https://mlsanity.com/paper/2608.25123/) (2026)
- [Mapping the RAG Landscape: A Four Axis Taxonomy of Efficiency, Defense, Interactivity, and Reasoning](https://mlsanity.com/paper/2610.01936/) (2026)

## Abstract (arXiv)

Conversational Information Seeking (CIS) is a relatively new research area within conversational AI that attempts to seek information from end-users in order to understand and satisfy users' needs. If realized, such a system has far-reaching benefits in the real world; for example, a CIS system can assist clinicians in pre-screening or triaging patients in healthcare. A key open sub-problem in CIS that remains unaddressed in the literature is generating Information Seeking Questions (ISQs) based on a short initial query from the end-user. To address this open problem, we propose Information SEEking Question generator (ISEEQ), a novel approach for generating ISQs from just a short user query, given a large text corpus relevant to the user query. Firstly, ISEEQ uses a knowledge graph to enrich the user query. Secondly, ISEEQ uses the knowledge-enriched query to retrieve relevant context passages to ask coherent ISQs adhering to a conceptual flow. Thirdly, ISEEQ introduces a new deep generative-adversarial reinforcement learning-based approach for generating ISQs. We show that ISEEQ can generate high-quality ISQs to promote the development of CIS agents. ISEEQ significantly outperforms comparable baselines on five ISQ evaluation metrics across four datasets having user queries from diverse domains. Further, we argue that ISEEQ is transferable across domains for generating ISQs, as it shows the acceptable performance when trained and tested on different pairs of domains. The qualitative human evaluation confirms ISEEQ-generated ISQs are comparable in quality to human-generated questions and outperform the best comparable baseline.