Papers with QA-SRL
Crowdsourcing Question-Answer Meaning Representations (N18-2)
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| Challenge: | Existing datasets for predicate-argument relationships are lacking highly skilled and trained annotators. |
| Approach: | They propose a crowdsourcing scheme to generate question-answer pairs that represent predicate-argument relationships in sentences as a set of question-announcer pairs. |
| Outcome: | The proposed model covers the vast majority of predicate-argument relationships in existing datasets along with many previously under-resourced ones, including implicit arguments and relations. |
Effective QA-Driven Annotation of Predicate–Argument Relations Across Languages (2026.eacl-long)
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| Challenge: | Explicit representations of predicate-argument relations are a cornerstone of natural language understanding. |
| Approach: | They propose a cross-linguistic projection approach that reuses an English QA-SRL parser within a constrained translation and word-alignment pipeline to automatically generate question-answer annotations aligned with target-language predicates. |
| Outcome: | The proposed approach outperforms strong multilingual LLMs in Hebrew, Russian, and French. |
Large-Scale QA-SRL Parsing (P18-1)
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| Challenge: | a crowd-sourced approach to learning semantic parsers to predict predicateargument structures is open to many researchers. |
| Approach: | They propose a large-scale corpus of Question-Answer driven Semantic Role Labeling annotations . they also propose QA-SRL Bank 2.0, a crowd-sourcing scheme that can be used to train high quality parsers . |
| Outcome: | The proposed QA-SRL parser can generate high-quality questions at low cost and is intuitive to non-experts. |
Inducing Semantic Roles Without Syntax (2021.findings-acl)
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| Challenge: | Semantic roles are a key component of linguistic predicate-argument structure, but syntax can be difficult to define, annotate, and predict. |
| Approach: | They propose to use QA-SRL to automatically induce semantic roles from ontologies that use question-answer pairs to represent predicate-argument structure. |
| Outcome: | The proposed method outperforms existing models and a state-of-the-art model over gold syntax. |
Controlled Crowdsourcing for High-Quality QA-SRL Annotation (2020.acl-main)
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Paul Roit, Ayal Klein, Daniela Stepanov, Jonathan Mamou, Julian Michael, Gabriel Stanovsky, Luke Zettlemoyer, Ido Dagan
| Challenge: | Question-answer driven Semantic Role Labeling (QA-SRL) is an open and natural flavour of SRL, potentially attainable from laymen. |
| Approach: | They propose a question-answer driven semantic role labeling approach that uses question-announced questions to label predicate-argument relationships. |
| Outcome: | The proposed method yields high-quality annotation with dramatically higher coverage, enabling future replicable research of natural semantic annotations. |
QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions (2021.emnlp-main)
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| Challenge: | Existing approaches to consolidate textual inputs are difficult to implement . a recent study aims to capture content overlap by combining multiple textual elements . |
| Approach: | They propose to align predicate-argument relations across texts to represent content overlap . their setting exploits QA-SRL, utilizing question-answer pairs to capture predicates . |
| Outcome: | The proposed task captures content overlap beyond lexical similarity and complements cross-document coreference with proposition-level links, offering potential use for downstream tasks. |