# Asking It All: Generating Contextualized Questions for any Semantic Role

arXiv: https://arxiv.org/abs/2109.04832 · HTML: https://mlsanity.com/paper/2109.04832/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 33 references that are in the map.
- First posted on arXiv 10 Sep 2021.

## Builds on (most cited first, 17 of 17)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://mlsanity.com/paper/1606.05250/) (2016)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [Zero-Shot Relation Extraction via Reading Comprehension](https://mlsanity.com/paper/1706.04115/) (2017)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [Event Extraction by Answering (Almost) Natural Questions](https://mlsanity.com/paper/2004.13625/) (2020)
- [Simple BERT Models for Relation Extraction and Semantic Role Labeling](https://mlsanity.com/paper/1904.05255/) (2019)
- [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019)
- [Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation](https://mlsanity.com/paper/1904.03396/) (2019)
- [Large-Scale QA-SRL Parsing](https://mlsanity.com/paper/1805.05377/) (2018)
- [Controlled Crowdsourcing for High-Quality QA-SRL Annotation](https://mlsanity.com/paper/1911.03243/) (2019)
- [QADiscourse -- Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines](https://mlsanity.com/paper/2010.02815/) (2020)
- [Stay Hungry, Stay Focused: Generating Informative and Specific Questions in Information-Seeking Conversations](https://mlsanity.com/paper/2004.14530/) (2020)
- [Zero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types](https://mlsanity.com/paper/1802.06842/) (2018)
- [Syn-QG: Syntactic and Shallow Semantic Rules for Question Generation](https://mlsanity.com/paper/2004.08694/) (2020)

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

- [Generating Literal and Implied Subquestions to Fact-check Complex Claims](https://mlsanity.com/paper/2205.06938/) (2022)
- [Conditional Generation with a Question-Answering Blueprint](https://mlsanity.com/paper/2207.00397/) (2022)
- [Generative Language Models for Paragraph-Level Question Generation](https://mlsanity.com/paper/2210.03992/) (2022)
- [Peek Across: Improving Multi-Document Modeling via Cross-Document Question-Answering](https://mlsanity.com/paper/2305.15387/) (2023)
- [Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric Summarization](https://mlsanity.com/paper/2203.14187/) (2022)
- [Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation](https://mlsanity.com/paper/2209.11000/) (2022)
- [An Empirical Comparison of LM-based Question and Answer Generation Methods](https://mlsanity.com/paper/2305.17002/) (2023)
- [NLI under the Microscope: What Atomic Hypothesis Decomposition Reveals](https://mlsanity.com/paper/2502.08080/) (2025)
- [A Practical Toolkit for Multilingual Question and Answer Generation](https://mlsanity.com/paper/2305.17416/) (2023)
- ["What makes a question inquisitive?" A Study on Type-Controlled Inquisitive Question Generation](https://mlsanity.com/paper/2205.08056/) (2022)
- [Semantic Role Labeling Meets Definition Modeling: Using Natural Language to Describe Predicate-Argument Structures](https://mlsanity.com/paper/2212.01094/) (2022)
- [Promptly Predicting Structures: The Return of Inference](https://mlsanity.com/paper/2401.06877/) (2024)
- [Explicating the Implicit: Argument Detection Beyond Sentence Boundaries](https://mlsanity.com/paper/2408.04246/) (2024)
- [DIFFQG: Generating Questions to Summarize Factual Changes](https://mlsanity.com/paper/2303.00242/) (2023)
- [Graph Guided Question Answer Generation for Procedural Question-Answering](https://mlsanity.com/paper/2401.13594/) (2024)
- [Ask to Understand: Question Generation for Multi-hop Question Answering](https://mlsanity.com/paper/2203.09073/) (2022)
- [Repro: An Open-Source Library for Improving the Reproducibility and Usability of Publicly Available Research Code](https://mlsanity.com/paper/2204.13848/) (2022)
- [Covering Uncommon Ground: Gap-Focused Question Generation for Answer Assessment](https://mlsanity.com/paper/2307.03319/) (2023)
- [The Case for Scalable, Data-Driven Theory: A Paradigm for Scientific Progress in NLP](https://mlsanity.com/paper/2312.00349/) (2023)
- [Dense Paraphrasing for Textual Enrichment](https://mlsanity.com/paper/2210.11563/) (2022)
- [QOG:Question and Options Generation based on Language Model](https://mlsanity.com/paper/2406.12381/) (2024)
- [Modeling Complex Event Scenarios via Simple Entity-focused Questions](https://mlsanity.com/paper/2302.07139/) (2023)

## Abstract (arXiv)

Asking questions about a situation is an inherent step towards understanding it. To this end, we introduce the task of role question generation, which, given a predicate mention and a passage, requires producing a set of questions asking about all possible semantic roles of the predicate. We develop a two-stage model for this task, which first produces a context-independent question prototype for each role and then revises it to be contextually appropriate for the passage. Unlike most existing approaches to question generation, our approach does not require conditioning on existing answers in the text. Instead, we condition on the type of information to inquire about, regardless of whether the answer appears explicitly in the text, could be inferred from it, or should be sought elsewhere. Our evaluation demonstrates that we generate diverse and well-formed questions for a large, broad-coverage ontology of predicates and roles.