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Data · dataset · 2022

biu-nlp/qamr

Listed in Hugging Face Datasets

Question-Answer Meaning Representations (QAMR) are a new paradigm for representing predicate-argument structure, which makes use of free-form questions and their answers in order to represent a wide range of semantic phenomena.

Description

The semantic expressivity of QAMR compares to (and in some cases exceeds) that of existing formalisms, while the representations can be annotated by non-experts (in particular, using crowdsourcing).

Formal

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Notes

* The `answer_ranges` feature here has a different meaning from that of the `qanom` and `qa_srl` datasets, although both are structured the same way; while in qasrl/qanom, each "answer range" (i.e. each span, represented as [begin-idx, end-idx]) stands for an independant answer which is read separately (e.g., "John Vincen", "head of marketing"), in this `qamr` dataset each question has a single answer who might be conposed of non-consecutive spans; that is, all given spans should be read successively.

  • Another difference is that the meaning of `predicate` in QAMR is different and softer than in QASRL/QANom - here, the predicate is not necessarily within the question, it can also be in the answer; it is generally what the annotator marked as the focus of the QA.

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Where it is published

Catalogue records · 1

Topics

Provenance · 1 source records, 7 field assertions
SourceKeyLast seenRaw
Hugging Face Datasetsbiu-nlp/qamr11 d agoJSON v1
FieldAssertionExtractorEvidence
access_levelsource · Hugging Faceconnector:huggingface@1.0.0/gated
concepts[field].local:field:computer-science-aimapping · Hugging Faceconnector:huggingface@1.0.0
created_datesource · Hugging Faceconnector:huggingface@1.0.0
descriptionsource · Hugging Faceconnector:huggingface@1.0.0/description
publication_datesource · Hugging Faceconnector:huggingface@1.0.0
titlesource · Hugging Faceconnector:huggingface@1.0.0/id
updated_datesource · Hugging Faceconnector:huggingface@1.0.0