Asking It All: Generating Contextualized Questions for any Semantic Role
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.
Code (1)
Tasks
AllQuestion GenerationQuestion-GenerationSimilar Papers 제목 키워드 기반
Generating Uncontextualized and Contextualized Questions for Document-Level Event Argument Extraction
This paper presents multiple question generation strategies for document-level event argument extraction. These strategies do not require human involvement and result in uncontextualized questions as well as contextualiz…
Event Argument ExtractionQuestion GenerationQuestion-GenerationSTaR-GATE: Teaching Language Models to Ask Clarifying Questions
When prompting language models to complete a task, users often leave important aspects unsaid. While asking questions could resolve this ambiguity (GATE; Li et al., 2023), models often struggle to ask good questions. We …
Language ModelingLanguage ModellingGenerating Lexical Representations of Frames using Lexical Substitution
Semantic frames are formal linguistic structures describing situations/actions/events, e.g. Commercial transfer of goods. Each frame provides a set of roles corresponding to the situation participants, e.g. Buyer and Goo…
A Semantic Role-based Approach to Open-Domain Automatic Question Generation
We present a novel rule-based system for automatic generation of factual questions from sentences, using semantic role labeling (SRL) as the main form of text analysis. The system is capable of generating both wh-questio…
Dependency ParsingQuestion GenerationQuestion-GenerationReading Comprehension+1Exploiting Semantic Role Contextualized Video Features for Multi-Instance Text-Video Retrieval EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022
In this report, we present our approach for EPIC-KITCHENS-100 Multi-Instance Retrieval Challenge 2022. We first parse sentences into semantic roles corresponding to verbs and nouns; then utilize self-attentions to exploi…
Multi-Instance RetrievalRetrievalSemantic SimilaritySemantic Textual Similarity+2