
# Dataset Card for QA-SRL

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [Homepage](https://dada.cs.washington.edu/qasrl/#page-top)
- **Annotation Tool:** [Annotation tool](https://github.com/luheng/qasrl_annotation)
- **Repository:** [Repository](https://dada.cs.washington.edu/qasrl/#dataset)
- **Paper:**    [Qa_srl paper](https://www.aclweb.org/anthology/D15-1076.pdf)
- **Point of Contact:** [Luheng He](luheng@cs.washington.edu)


### Dataset Summary

we model predicate-argument structure of a sentence with a set of question-answer pairs. our method allows practical large-scale annotation of training data. We focus on semantic rather than syntactic annotation, and introduce a scalable method for gathering data that allows both training and evaluation.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

This dataset is in english language.

## Dataset Structure

### Data Instances


We use question-answer pairs to model verbal predicate-argument structure. The questions start with wh-words (Who, What, Where, What, etc.) and contains a verb predicate in the sentence; the answers are phrases in the sentence. For example:

`UCD finished the 2006 championship as Dublin champions , by beating St Vincents in the final .`

Predicate | Question | Answer
---|---|---|
|Finished|Who finished something? |	UCD
|Finished|What did someone finish?|the 2006 championship
|Finished|What did someone finish something as?	|Dublin champions
|Finished|How did someone finish something?	|by beating St Vincents in the final
|beating | Who beat someone? | UCD
|beating|When did someone beat someone?	|in the final
|beating|Who did someone beat?|	St Vincents

### Data Fields

Annotations provided are as follows:

- `sentence`: contains tokenized sentence
- `sent_id`: is the sentence identifier
- `predicate_idx`:the index of the predicate (its position in the sentence) 
- `predicate`: the predicate token
- `question`: contains the question which is a list of tokens. The question always consists of seven slots, as defined in the paper. The empty slots are represented with a marker “_”. The question ends with question mark.
- `answer`: list of answers to the question
               

### Data Splits

Dataset | Sentences | Verbs | QAs
--- | --- | --- |---|
**newswire-train**|744|2020|4904|
**newswire-dev**|249|664|1606|
**newswire-test**|248|652|1599
**Wikipedia-train**|`1174`|`2647`|`6414`|
**Wikipedia-dev**|`392`|`895`|`2183`|
**Wikipedia-test**|`393`|`898`|`2201`|

**Please note**
This dataset only has wikipedia data. Newswire dataset needs  CoNLL-2009 English training data to get the complete data. This training data is under license. Thus, newswire dataset is not included in this data.

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

We annotated over 3000 sentences (nearly 8,000 verbs) in total across two domains: newswire (PropBank) and Wikipedia. 
#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

non-expert annotators were given a short tutorial and a small set of sample annotations (about 10 sentences). Annotators were hired if they showed good understanding of English and the task. The entire screening process usually took less than 2 hours.

#### Who are the annotators?

10 part-time, non-exper annotators from Upwork (Previously oDesk)

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[Luheng He](luheng@cs.washington.edu)

### Licensing Information

[More Information Needed]

### Citation Information

```
@InProceedings{huggingface:dataset,
title = {QA-SRL: Question-Answer Driven Semantic Role Labeling},
authors={Luheng He, Mike Lewis, Luke Zettlemoyer},
year={2015}
publisher = {cs.washington.edu},
howpublished={\\url{https://dada.cs.washington.edu/qasrl/#page-top}},
}
```

### Contributions

Thanks to [@bpatidar](https://github.com/bpatidar) for adding this dataset.
task_categories:question-answering task_ids:multiple-choice-qa task_ids:open-domain-qa annotations_creators:expert-generated language_creators:expert-generated multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:unknown region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["en"], "license": ["unknown"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["question-answering"], "task_ids": ["multiple-choice-qa", "open-domain-qa"], "paperswithcode_id": "qa-srl", "pretty_name": "QA-SRL", "dataset_info": {"features": [{"name": "sentence", "dtype": "string"}, {"name": "sent_id", "dtype": "string"}, {"name": "predicate_idx", "dtype": "int32"}, {"name": "predicate", "dtype": "string"}, {"name": "question", "sequence": "string"}, {"name": "answers", "sequence": "string"}], "config_name": "plain_text", "splits": [{"name": "train", "num_bytes": 1835549, "num_examples": 6414}, {"name": "validation", "num_bytes": 632992, "num_examples": 2183}, {"name": "test", "num_bytes": 637317, "num_examples": 2201}], "download_size": 1087729, "dataset_size": 3105858}}
TAGS
#task_categories-question-answering #task_ids-multiple-choice-qa #task_ids-open-domain-qa #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #region-us 

Dataset Card for QA-SRL
=======================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: Homepage
* Annotation Tool: Annotation tool
* Repository: Repository
* Paper: Qa\_srl paper
* Point of Contact: Luheng He


### Dataset Summary


we model predicate-argument structure of a sentence with a set of question-answer pairs. our method allows practical large-scale annotation of training data. We focus on semantic rather than syntactic annotation, and introduce a scalable method for gathering data that allows both training and evaluation.


### Supported Tasks and Leaderboards


### Languages


This dataset is in english language.


Dataset Structure
-----------------


### Data Instances


We use question-answer pairs to model verbal predicate-argument structure. The questions start with wh-words (Who, What, Where, What, etc.) and contains a verb predicate in the sentence; the answers are phrases in the sentence. For example:


'UCD finished the 2006 championship as Dublin champions , by beating St Vincents in the final .'


Predicate: Finished, Question: Who finished something?, Answer: UCD
Predicate: Finished, Question: What did someone finish?, Answer: the 2006 championship
Predicate: Finished, Question: What did someone finish something as?, Answer: Dublin champions
Predicate: Finished, Question: How did someone finish something?, Answer: by beating St Vincents in the final
Predicate: beating, Question: Who beat someone?, Answer: UCD
Predicate: beating, Question: When did someone beat someone?, Answer: in the final
Predicate: beating, Question: Who did someone beat?, Answer: St Vincents


### Data Fields


Annotations provided are as follows:


* 'sentence': contains tokenized sentence
* 'sent\_id': is the sentence identifier
* 'predicate\_idx':the index of the predicate (its position in the sentence)
* 'predicate': the predicate token
* 'question': contains the question which is a list of tokens. The question always consists of seven slots, as defined in the paper. The empty slots are represented with a marker “\_”. The question ends with question mark.
* 'answer': list of answers to the question


### Data Splits



Please note
This dataset only has wikipedia data. Newswire dataset needs CoNLL-2009 English training data to get the complete data. This training data is under license. Thus, newswire dataset is not included in this data.


Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


We annotated over 3000 sentences (nearly 8,000 verbs) in total across two domains: newswire (PropBank) and Wikipedia.


#### Who are the source language producers?


### Annotations


#### Annotation process


non-expert annotators were given a short tutorial and a small set of sample annotations (about 10 sentences). Annotators were hired if they showed good understanding of English and the task. The entire screening process usually took less than 2 hours.


#### Who are the annotators?


10 part-time, non-exper annotators from Upwork (Previously oDesk)


### Personal and Sensitive Information


Considerations for Using the Data
---------------------------------


### Social Impact of Dataset


### Discussion of Biases


### Other Known Limitations


Additional Information
----------------------


### Dataset Curators


Luheng He


### Licensing Information


### Contributions


Thanks to @bpatidar for adding this dataset.



### Dataset Summary


we model predicate-argument structure of a sentence with a set of question-answer pairs. our method allows practical large-scale annotation of training data. We focus on semantic rather than syntactic annotation, and introduce a scalable method for gathering data that allows both training and evaluation. ### Supported Tasks and Leaderboards ### Languages


This dataset is in english language.


Dataset Structure
----------------- ### Data Instances


We use question-answer pairs to model verbal predicate-argument structure. The questions start with wh-words (Who, What, Where, What, etc.) and contains a verb predicate in the sentence; the answers are phrases in the sentence. For example:


'UCD finished the 2006 championship as Dublin champions , by beating St Vincents in the final .'


Predicate: Finished, Question: Who finished something?, Answer: UCD
Predicate: Finished, Question: What did someone finish?, Answer: the 2006 championship
Predicate: Finished, Question: What did someone finish something as?, Answer: Dublin champions
Predicate: Finished, Question: How did someone finish something?, Answer: by beating St Vincents in the final
Predicate: beating, Question: Who beat someone?, Answer: UCD
Predicate: beating, Question: When did someone beat someone?, Answer: in the final
Predicate: beating, Question: Who did someone beat?, Answer: St Vincents ### Data Fields


Annotations provided are as follows:


* 'sentence': contains tokenized sentence
* 'sent\_id': is the sentence identifier
* 'predicate\_idx':the index of the predicate (its position in the sentence)
* 'predicate': the predicate token
* 'question': contains the question which is a list of tokens. The question always consists of seven slots, as defined in the paper. The empty slots are represented with a marker “\_”. The question ends with question mark.
* 'answer': list of answers to the question ### Data Splits



Please note
This dataset only has wikipedia data. Newswire dataset needs CoNLL-2009 English training data to get the complete data. This training data is under license. Thus, newswire dataset is not included in this data.


Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization


We annotated over 3000 sentences (nearly 8,000 verbs) in total across two domains: newswire (PropBank) and Wikipedia. #### Who are the source language producers? ### Annotations #### Annotation process


non-expert annotators were given a short tutorial and a small set of sample annotations (about 10 sentences). Annotators were hired if they showed good understanding of English and the task. The entire screening process usually took less than 2 hours. #### Who are the annotators?


10 part-time, non-exper annotators from Upwork (Previously oDesk) ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators


Luheng He ### Licensing Information ### Contributions


Thanks to @bpatidar for adding this dataset.
TAGS
#task_categories-question-answering #task_ids-multiple-choice-qa #task_ids-open-domain-qa #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #region-us 
 ### Dataset Summary


we model predicate-argument structure of a sentence with a set of question-answer pairs. our method allows practical large-scale annotation of training data. We focus on semantic rather than syntactic annotation, and introduce a scalable method for gathering data that allows both training and evaluation. ### Supported Tasks and Leaderboards ### Languages


This dataset is in english language.


Dataset Structure
----------------- ### Data Instances


We use question-answer pairs to model verbal predicate-argument structure. The questions start with wh-words (Who, What, Where, What, etc.) and contains a verb predicate in the sentence; the answers are phrases in the sentence. For example:


'UCD finished the 2006 championship as Dublin champions , by beating St Vincents in the final .'


Predicate: Finished, Question: Who finished something?, Answer: UCD
Predicate: Finished, Question: What did someone finish?, Answer: the 2006 championship
Predicate: Finished, Question: What did someone finish something as?, Answer: Dublin champions
Predicate: Finished, Question: How did someone finish something?, Answer: by beating St Vincents in the final
Predicate: beating, Question: Who beat someone?, Answer: UCD
Predicate: beating, Question: When did someone beat someone?, Answer: in the final
Predicate: beating, Question: Who did someone beat?, Answer: St Vincents ### Data Fields


Annotations provided are as follows:


* 'sentence': contains tokenized sentence
* 'sent\_id': is the sentence identifier
* 'predicate\_idx':the index of the predicate (its position in the sentence)
* 'predicate': the predicate token
* 'question': contains the question which is a list of tokens. The question always consists of seven slots, as defined in the paper. The empty slots are represented with a marker “\_”. The question ends with question mark.
* 'answer': list of answers to the question ### Data Splits



Please note
This dataset only has wikipedia data. Newswire dataset needs CoNLL-2009 English training data to get the complete data. This training data is under license. Thus, newswire dataset is not included in this data.


Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization


We annotated over 3000 sentences (nearly 8,000 verbs) in total across two domains: newswire (PropBank) and Wikipedia. #### Who are the source language producers? ### Annotations #### Annotation process


non-expert annotators were given a short tutorial and a small set of sample annotations (about 10 sentences). Annotators were hired if they showed good understanding of English and the task. The entire screening process usually took less than 2 hours. #### Who are the annotators?


10 part-time, non-exper annotators from Upwork (Previously oDesk) ### Personal and Sensitive Information


Considerations for Using the Data
--------------------------------- ### Social Impact of Dataset ### Discussion of Biases ### Other Known Limitations


Additional Information
---------------------- ### Dataset Curators


Luheng He ### Licensing Information ### Contributions


Thanks to @bpatidar for adding this dataset.
passage: TAGS
#task_categories-question-answering #task_ids-multiple-choice-qa #task_ids-open-domain-qa #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unknown #region-us 
### Dataset Summary


we model predicate-argument structure of a sentence with a set of question-answer pairs. our method allows practical large-scale annotation of training data. We focus on semantic rather than syntactic annotation, and introduce a scalable method for gathering data that allows both training and evaluation.### Supported Tasks and Leaderboards### Languages


This dataset is in english language.


Dataset Structure
-----------------### Data Instances


We use question-answer pairs to model verbal predicate-argument structure. The questions start with wh-words (Who, What, Where, What, etc.) and contains a verb predicate in the sentence; the answers are phrases in the sentence. For example:


'UCD finished the 2006 championship as Dublin champions , by beating St Vincents in the final .'


Predicate: Finished, Question: Who finished something?, Answer: UCD
Predicate: Finished, Question: What did someone finish?, Answer: the 2006 championship
Predicate: Finished, Question: What did someone finish something as?, Answer: Dublin champions
Predicate: Finished, Question: How did someone finish something?, Answer: by beating St Vincents in the final
Predicate: beating, Question: Who beat someone?, Answer: UCD
Predicate: beating, Question: When did someone beat someone?, Answer: in the final
Predicate: beating, Question: Who did someone beat?, Answer: St Vincents