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Found 9 papers, 6 papers with code
QADiscourse - Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines
Discourse relations describe how two propositions relate to one another, and identifying them automatically is an integral part of natural language understanding.
Opinion-based Relational Pivoting for Cross-domain Aspect Term Extraction
In this paper, we investigate and establish empirically a prior conjecture, which suggests that the linguistic relations connecting opinion terms to their aspects transfer well across domains and therefore can be leveraged for cross-domain aspect term extraction.
QAPyramid: Fine-grained Evaluation of Content Selection for Text Summarization
The Pyramid human evaluation protocol, which assesses content selection by breaking the reference summary into sub-units and verifying their presence in the system summary, has been widely adopted.
Explicating the Implicit: Argument Detection Beyond Sentence Boundaries
Detecting semantic arguments of a predicate word has been conventionally modeled as a sentence-level task.
QASem Parsing: Text-to-text Modeling of QA-based Semantics
Several recent works have suggested to represent semantic relations with questions and answers, decomposing textual information into separate interrogative natural language statements.
QA-Align: Representing Cross-Text Content Overlap by Aligning Question-Answer Propositions
Multi-text applications, such as multi-document summarization, are typically required to model redundancies across related texts.
QANom: Question-Answer driven SRL for Nominalizations
We propose a new semantic scheme for capturing predicate-argument relations for nominalizations, termed QANom.
QADiscourse -- Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines
Discourse relations describe how two propositions relate to one another, and identifying them automatically is an integral part of natural language understanding.
Controlled Crowdsourcing for High-Quality QA-SRL Annotation
Question-answer driven Semantic Role Labeling (QA-SRL) was proposed as an attractive open and natural flavour of SRL, potentially attainable from laymen.
