# QA-SRL 2020 (Gold Standard) 
The dataset contains question-answer pairs to model verbal predicate-argument structure. 
The questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence.
This dataset, a.k.a "QASRL-GS" (Gold Standard) or "QASRL-2020", which was constructed via controlled crowdsourcing, includes high-quality QA-SRL annotations to serve as an evaluation set (dev and test) for models trained on the large-scale QA-SRL dataset (you can find it in this hub as [biu-nlp/qa_srl2018](https://huggingface.co/datasets/biu-nlp/qa_srl2018)).

See the paper for details: [Controlled Crowdsourcing for High-Quality QA-SRL Annotation, Roit et. al., 2020](https://aclanthology.org/2020.acl-main.626/).

Check out our [GitHub repository](https://github.com/plroit/qasrl-gs) to find code for evaluation. 

The dataset was annotated by selected workers from Amazon Mechanical Turk.
region:us
{}
TAGS
#region-us 

# QA-SRL 2020 (Gold Standard) 
The dataset contains question-answer pairs to model verbal predicate-argument structure. 
The questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence.
This dataset, a.k.a "QASRL-GS" (Gold Standard) or "QASRL-2020", which was constructed via controlled crowdsourcing, includes high-quality QA-SRL annotations to serve as an evaluation set (dev and test) for models trained on the large-scale QA-SRL dataset (you can find it in this hub as biu-nlp/qa_srl2018).

See the paper for details: Controlled Crowdsourcing for High-Quality QA-SRL Annotation, Roit et. al., 2020.

Check out our GitHub repository to find code for evaluation. 

The dataset was annotated by selected workers from Amazon Mechanical Turk.
# QA-SRL 2020 (Gold Standard) 
The dataset contains question-answer pairs to model verbal predicate-argument structure. 
The questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence.
This dataset, a.k.a "QASRL-GS" (Gold Standard) or "QASRL-2020", which was constructed via controlled crowdsourcing, includes high-quality QA-SRL annotations to serve as an evaluation set (dev and test) for models trained on the large-scale QA-SRL dataset (you can find it in this hub as biu-nlp/qa_srl2018).

See the paper for details: Controlled Crowdsourcing for High-Quality QA-SRL Annotation, Roit et. al., 2020.

Check out our GitHub repository to find code for evaluation. 

The dataset was annotated by selected workers from Amazon Mechanical Turk.
TAGS
#region-us 
 # QA-SRL 2020 (Gold Standard) 
The dataset contains question-answer pairs to model verbal predicate-argument structure. 
The questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence.
This dataset, a.k.a "QASRL-GS" (Gold Standard) or "QASRL-2020", which was constructed via controlled crowdsourcing, includes high-quality QA-SRL annotations to serve as an evaluation set (dev and test) for models trained on the large-scale QA-SRL dataset (you can find it in this hub as biu-nlp/qa_srl2018).

See the paper for details: Controlled Crowdsourcing for High-Quality QA-SRL Annotation, Roit et. al., 2020.

Check out our GitHub repository to find code for evaluation. 

The dataset was annotated by selected workers from Amazon Mechanical Turk.
passage: TAGS
#region-us 
# QA-SRL 2020 (Gold Standard) 
The dataset contains question-answer pairs to model verbal predicate-argument structure. 
The questions start with wh-words (Who, What, Where, What, etc.) and contain a verb predicate in the sentence; the answers are phrases in the sentence.
This dataset, a.k.a "QASRL-GS" (Gold Standard) or "QASRL-2020", which was constructed via controlled crowdsourcing, includes high-quality QA-SRL annotations to serve as an evaluation set (dev and test) for models trained on the large-scale QA-SRL dataset (you can find it in this hub as biu-nlp/qa_srl2018).

See the paper for details: Controlled Crowdsourcing for High-Quality QA-SRL Annotation, Roit et. al., 2020.

Check out our GitHub repository to find code for evaluation. 

The dataset was annotated by selected workers from Amazon Mechanical Turk.