README.md

metadata

annotations_creators:
  - machine-generated
language_creators:
  - machine-generated
language:
  - en
license:
  - unknown
multilinguality:
  - monolingual
pretty_name: text2log
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - translation
task_ids: []
dataset_info:
  features:
    - name: sentence
      dtype: string
    - name: fol_translation
      dtype: string
  splits:
    - name: train
      num_bytes: 10358134
      num_examples: 101931
  download_size: 9746473
  dataset_size: 10358134

Dataset Card for text2log

Table of Contents

• Dataset Description
  • Dataset Summary
  • Supported Tasks
  • 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

Dataset Description

• Homepage:
• Repository: GitHub
• Paper:
• Leaderboard:
• Point of Contact: https://github.com/alevkov

Dataset Summary

The dataset contains 100,000 simple English sentences selected and filtered from enTenTen15 and their translation into First Order Logic (FOL) using ccg2lambda.

Supported Tasks and Leaderboards

'semantic-parsing': The data set is used to train models which can generate FOL statements from natural language text

Languages

en-US

Dataset Structure

Data Instances

{
'clean':'All things that are new are good.',
'trans':'all x1.(_thing(x1) -> (_new(x1) -> _good(x1)))'
}

Data Fields

• 'clean': a simple English sentence
• 'trans': the corresponding translation into Lambda Dependency-based Compositional Semantics

Data Splits

No predefined train/test split is given. The authors used a 80/20 split

Dataset Creation

Curation Rationale

The text2log data set is used to improve FOL statement generation from natural text

Source Data

Initial Data Collection and Normalization

Short text samples selected from enTenTen15

Who are the source language producers?

See https://www.sketchengine.eu/ententen-english-corpus/

Annotations

Annotation process

Machine generated using https://github.com/mynlp/ccg2lambda

Who are the annotators?

none

Personal and Sensitive Information

The dataset does not contain personal or sensitive information.

Considerations for Using the Data

Social Impact of Dataset

[Needs More Information]

Discussion of Biases

[Needs More Information]

Other Known Limitations

[Needs More Information]

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

None given

Citation Information

@INPROCEEDINGS{9401852, 
author={Levkovskyi, Oleksii and Li, Wei},
booktitle={SoutheastCon 2021},
title={Generating Predicate Logic Expressions from Natural Language},
year={2021},
volume={},
number={},
pages={1-8},
doi={10.1109/SoutheastCon45413.2021.9401852}
}

Contributions

Thanks to @apergo-ai for adding this dataset.