README.md

metadata

task_categories:
  - text-classification
multilinguality:
  - monolingual
task_ids:
  - text-scoring
language:
  - en
annotations_creators:
  - crowdsourced
source_datasets:
  - extended
size_categories:
  - 10K<n<100K
license:
  - cc-by-sa-4.0
paperswithcode_id: null
pretty_name: GoogleWellformedQuery
language_creators:
  - found
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

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

[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 for adding this dataset.