
# Dataset Card for Google Query-wellformedness Dataset

## 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:** [GitHub](https://github.com/google-research-datasets/query-wellformedness)
- **Repository:** [GitHub](https://github.com/google-research-datasets/query-wellformedness)
- **Paper:** [ARXIV](https://arxiv.org/abs/1808.09419)
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed.

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

English

## Dataset Structure

### Data Instances

```
{'rating': 0.2, 'content': 'The European Union includes how many ?'}
```

### Data Fields

- `rating`: a `float` between 0-1
- `sentence`: query which you want to rate

### Data Splits

|                            | Train   | Valid | Test |
| -----                      | ------ | ----- | ---- |
| Input Sentences            |   17500     |   3750    |  3850    |

## Dataset Creation

### Curation Rationale

Understanding search queries is a hard problem as it involves dealing with “word salad” text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. This dataset introduce a new task of identifying a well-formed natural language question. 

### Source Data

Used the Paralex corpus (Fader et al., 2013) that contains pairs of noisy paraphrase questions. These questions were issued by users in WikiAnswers (a Question-Answer forum) and consist of both web-search query like constructs (“5 parts of chloroplast?”) and well-formed questions (“What is the punishment for grand theft?”).

#### Initial Data Collection and Normalization

Selected 25,100 queries from the unique list of queries extracted from the corpus such that no two queries in the selected set are paraphrases.

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

The queries are annotated into well-formed or non-wellformed questions if it satisfies the following:

1. Query is grammatical.
2. Query is an explicit question.
3. Query does not contain spelling errors.

#### Who are the annotators?

Every query was labeled by five different crowdworkers with a binary label indicating whether a query is well-formed or not. And average of the ratings of the five annotators was reported, to get the probability of a query being well-formed.

### 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

[More Information Needed]

### Licensing Information

Query-wellformedness dataset is licensed under CC BY-SA 4.0. Any third party content or data is provided “As Is” without any warranty, express or implied.

### Citation Information

```
@InProceedings{FaruquiDas2018,
   title = {{Identifying Well-formed Natural Language Questions}},
   author = {Faruqui, Manaal and Das, Dipanjan},
   booktitle = {Proc. of EMNLP},
   year = {2018}
}
```

### Contributions

Thanks to [@vasudevgupta7](https://github.com/vasudevgupta7) for adding this dataset.
task_categories:text-classification task_ids:text-scoring annotations_creators:crowdsourced language_creators:found multilinguality:monolingual size_categories:10K<n<100K source_datasets:extended language:en license:cc-by-sa-4.0 arxiv:1808.09419 region:us
{"annotations_creators": ["crowdsourced"], "language_creators": ["found"], "language": ["en"], "license": ["cc-by-sa-4.0"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["extended"], "task_categories": ["text-classification"], "task_ids": ["text-scoring"], "pretty_name": "GoogleWellformedQuery", "dataset_info": {"features": [{"name": "rating", "dtype": "float32"}, {"name": "content", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 857391, "num_examples": 17500}, {"name": "test", "num_bytes": 189503, "num_examples": 3850}, {"name": "validation", "num_bytes": 184110, "num_examples": 3750}], "download_size": 1157019, "dataset_size": 1231004}}
TAGS
#task_categories-text-classification #task_ids-text-scoring #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended #language-English #license-cc-by-sa-4.0 #arxiv-1808.09419 #region-us 

Dataset Card for Google Query-wellformedness Dataset
====================================================


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: GitHub
* Repository: GitHub
* Paper: ARXIV
* Leaderboard:
* Point of Contact:


### Dataset Summary


Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed.


### Supported Tasks and Leaderboards


### Languages


English


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


### Data Instances


### Data Fields


* 'rating': a 'float' between 0-1
* 'sentence': query which you want to rate


### Data Splits



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


### Curation Rationale


Understanding search queries is a hard problem as it involves dealing with “word salad” text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. This dataset introduce a new task of identifying a well-formed natural language question.


### Source Data


Used the Paralex corpus (Fader et al., 2013) that contains pairs of noisy paraphrase questions. These questions were issued by users in WikiAnswers (a Question-Answer forum) and consist of both web-search query like constructs (“5 parts of chloroplast?”) and well-formed questions (“What is the punishment for grand theft?”).


#### Initial Data Collection and Normalization


Selected 25,100 queries from the unique list of queries extracted from the corpus such that no two queries in the selected set are paraphrases.


#### Who are the source language producers?


### Annotations


#### Annotation process


The queries are annotated into well-formed or non-wellformed questions if it satisfies the following:


1. Query is grammatical.
2. Query is an explicit question.
3. Query does not contain spelling errors.


#### Who are the annotators?


Every query was labeled by five different crowdworkers with a binary label indicating whether a query is well-formed or not. And average of the ratings of the five annotators was reported, to get the probability of a query being well-formed.


### 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


Query-wellformedness dataset is licensed under CC BY-SA 4.0. Any third party content or data is provided “As Is” without any warranty, express or implied.


### Contributions


Thanks to @vasudevgupta7 for adding this dataset.



### Dataset Summary


Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed. ### Supported Tasks and Leaderboards ### Languages


English


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


* 'rating': a 'float' between 0-1
* 'sentence': query which you want to rate ### Data Splits



Dataset Creation
---------------- ### Curation Rationale


Understanding search queries is a hard problem as it involves dealing with “word salad” text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. This dataset introduce a new task of identifying a well-formed natural language question. ### Source Data


Used the Paralex corpus (Fader et al., 2013) that contains pairs of noisy paraphrase questions. These questions were issued by users in WikiAnswers (a Question-Answer forum) and consist of both web-search query like constructs (“5 parts of chloroplast?”) and well-formed questions (“What is the punishment for grand theft?”). #### Initial Data Collection and Normalization


Selected 25,100 queries from the unique list of queries extracted from the corpus such that no two queries in the selected set are paraphrases. #### Who are the source language producers? ### Annotations #### Annotation process


The queries are annotated into well-formed or non-wellformed questions if it satisfies the following:


1. Query is grammatical.
2. Query is an explicit question.
3. Query does not contain spelling errors. #### Who are the annotators?


Every query was labeled by five different crowdworkers with a binary label indicating whether a query is well-formed or not. And average of the ratings of the five annotators was reported, to get the probability of a query being well-formed. ### 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


Query-wellformedness dataset is licensed under CC BY-SA 4.0. Any third party content or data is provided “As Is” without any warranty, express or implied. ### Contributions


Thanks to @vasudevgupta7 for adding this dataset.
TAGS
#task_categories-text-classification #task_ids-text-scoring #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended #language-English #license-cc-by-sa-4.0 #arxiv-1808.09419 #region-us 
 ### Dataset Summary


Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed. ### Supported Tasks and Leaderboards ### Languages


English


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


* 'rating': a 'float' between 0-1
* 'sentence': query which you want to rate ### Data Splits



Dataset Creation
---------------- ### Curation Rationale


Understanding search queries is a hard problem as it involves dealing with “word salad” text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. This dataset introduce a new task of identifying a well-formed natural language question. ### Source Data


Used the Paralex corpus (Fader et al., 2013) that contains pairs of noisy paraphrase questions. These questions were issued by users in WikiAnswers (a Question-Answer forum) and consist of both web-search query like constructs (“5 parts of chloroplast?”) and well-formed questions (“What is the punishment for grand theft?”). #### Initial Data Collection and Normalization


Selected 25,100 queries from the unique list of queries extracted from the corpus such that no two queries in the selected set are paraphrases. #### Who are the source language producers? ### Annotations #### Annotation process


The queries are annotated into well-formed or non-wellformed questions if it satisfies the following:


1. Query is grammatical.
2. Query is an explicit question.
3. Query does not contain spelling errors. #### Who are the annotators?


Every query was labeled by five different crowdworkers with a binary label indicating whether a query is well-formed or not. And average of the ratings of the five annotators was reported, to get the probability of a query being well-formed. ### 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


Query-wellformedness dataset is licensed under CC BY-SA 4.0. Any third party content or data is provided “As Is” without any warranty, express or implied. ### Contributions


Thanks to @vasudevgupta7 for adding this dataset.
passage: TAGS
#task_categories-text-classification #task_ids-text-scoring #annotations_creators-crowdsourced #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-extended #language-English #license-cc-by-sa-4.0 #arxiv-1808.09419 #region-us 
### Dataset Summary


Google's query wellformedness dataset was created by crowdsourcing well-formedness annotations for 25,100 queries from the Paralex corpus. Every query was annotated by five raters each with 1/0 rating of whether or not the query is well-formed.### Supported Tasks and Leaderboards### Languages


English


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


* 'rating': a 'float' between 0-1
* 'sentence': query which you want to rate### Data Splits



Dataset Creation
----------------### Curation Rationale


Understanding search queries is a hard problem as it involves dealing with “word salad” text ubiquitously issued by users. However, if a query resembles a well-formed question, a natural language processing pipeline is able to perform more accurate interpretation, thus reducing downstream compounding errors. Hence, identifying whether or not a query is well formed can enhance query understanding. This dataset introduce a new task of identifying a well-formed natural language question.### Source Data


Used the Paralex corpus (Fader et al., 2013) that contains pairs of noisy paraphrase questions. These questions were issued by users in WikiAnswers (a Question-Answer forum) and consist of both web-search query like constructs (“5 parts of chloroplast?”) and well-formed questions (“What is the punishment for grand theft?”).#### Initial Data Collection and Normalization


Selected 25,100 queries from the unique list of queries extracted from the corpus such that no two queries in the selected set are paraphrases.