Datasets:
Tasks:
Translation
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
machine-generated
Annotations Creators:
machine-generated
Source Datasets:
original
Tags:
License:
sentence
stringlengths 14
82
| fol_translation
stringlengths 14
264
|
|---|---|
Some engineers were astronauts. | exists x1.(_engineer(x1) & exists x2.(_astronaut(x2) & (x1 = x2))) |
Some items were facts. | exists x1.(_item(x1) & exists x2.(_fact(x2) & (x1 = x2))) |
Some sites are wetlands. | exists x1.(_site(x1) & exists x2.(_wetland(x2) & (x1 = x2))) |
Some objects are artefacts. | exists x1.(_object(x1) & exists x2.(_artefact(x2) & (x1 = x2))) |
Some objects are siblings. | exists x1.(_object(x1) & exists x2.(_sibling(x2) & (x1 = x2))) |
Some others are conflations. | exists x1.(_other(x1) & exists x2.(_conflation(x2) & (x1 = x2))) |
Some cases are subdivisions. | exists x1.(_case(x1) & exists x2.(_subdivision(x2) & (x1 = x2))) |
Some classes is people. | exists x1.(_class(x1) & exists x2.(_people(x2) & (x1 = x2))) |
Some customs are ones. | exists x1.(_custom(x1) & exists x2.(_one(x2) & (x1 = x2))) |
Some members are masters. | exists x1.(_member(x1) & exists x2.(_master(x2) & (x1 = x2))) |
Some owners were gentlemen. | exists x1.(_owner(x1) & exists x2.(_gentleman(x2) & (x1 = x2))) |
Some people are fans. | exists x1.(_people(x1) & exists x2.(_fan(x2) & (x1 = x2))) |
Some teams are winners. | exists x1.(_team(x1) & exists x2.(_winner(x2) & (x1 = x2))) |
Some people were slaves. | exists x1.(_people(x1) & exists x2.(_slave(x2) & (x1 = x2))) |
Some suspicions are sins. | exists x1.(_suspicion(x1) & exists x2.(_sin(x2) & (x1 = x2))) |
Some way are fabrics. | exists x1.(_way(x1) & exists x2.(_fabric(x2) & (x1 = x2))) |
Some groups are civilians. | exists x1.(_group(x1) & exists x2.(_civilian(x2) & (x1 = x2))) |
Some Poles were accomplices. | exists x1.(_poles(x1) & exists x2.(_accomplice(x2) & (x1 = x2))) |
Some contraries are contradictories. | exists x1.(_contrary(x1) & exists x2.(_contradictory(x2) & (x1 = x2))) |
Some others are evildoers. | exists x1.(_other(x1) & exists x2.(_evildoer(x2) & (x1 = x2))) |
Some members are centres. | exists x1.(_member(x1) & exists x2.(_centre(x2) & (x1 = x2))) |
Some men are wolves. | exists x1.(_man(x1) & exists x2.(_wolf(x2) & (x1 = x2))) |
Some stones are wrecks. | exists x1.(_stone(x1) & exists x2.(_wreck(x2) & (x1 = x2))) |
Some others were robbers. | exists x1.(_other(x1) & exists x2.(_robber(x2) & (x1 = x2))) |
Some people are volunteers. | exists x1.(_people(x1) & exists x2.(_volunteer(x2) & (x1 = x2))) |
Some people are victims. | exists x1.(_people(x1) & exists x2.(_victim(x2) & (x1 = x2))) |
Some packages are dependencies. | exists x1.(_package(x1) & exists x2.(_dependency(x2) & (x1 = x2))) |
Some people are pessimists. | exists x1.(_people(x1) & exists x2.(_pessimist(x2) & (x1 = x2))) |
Some players are tenpai. | exists x1.(_player(x1) & exists x2.(_tenpaus(x2) & (x1 = x2))) |
Some MS were measures. | exists x1.(_m(x1) & exists x2.(_measure(x2) & (x1 = x2))) |
Some Congressmen were members. | exists x1.(_congressman(x1) & exists x2.(_member(x2) & (x1 = x2))) |
Some tenants are nonprofits. | exists x1.(_tenant(x1) & exists x2.(_nonprofit(x2) & (x1 = x2))) |
Some mammals are legislators. | exists x1.(_mammal(x1) & exists x2.(_legislator(x2) & (x1 = x2))) |
Some people are perfectionists. | exists x1.(_people(x1) & exists x2.(_perfectionist(x2) & (x1 = x2))) |
Some extent was shares. | exists x1.(_extent(x1) & exists x2.(_share(x2) & (x1 = x2))) |
Some creatures are armies. | exists x1.(_creature(x1) & exists x2.(_army(x2) & (x1 = x2))) |
Some people are homosexuals. | exists x1.(_people(x1) & exists x2.(_homosexual(x2) & (x1 = x2))) |
Some animals are pests. | exists x1.(_animal(x1) & exists x2.(_pest(x2) & (x1 = x2))) |
Some lobbyists are members. | exists x1.(_lobbyist(x1) & exists x2.(_member(x2) & (x1 = x2))) |
Some days are days. | exists x1.(_day(x1) & exists x2.(_day(x2) & (x1 = x2))) |
Some strategies are winners. | exists x1.(_strategy(x1) & exists x2.(_winner(x2) & (x1 = x2))) |
Some extent is enzymes. | exists x1.(_extent(x1) & exists x2.(_enzyme(x2) & (x1 = x2))) |
Some walls were people. | exists x1.(_wall(x1) & exists x2.(_people(x2) & (x1 = x2))) |
Some places are dwellings. | exists x1.(_place(x1) & exists x2.(_dwelling(x2) & (x1 = x2))) |
Some people are thought-leaders. | exists x1.(_people(x1) & exists x2.(_thought_dash_leader(x2) & (x1 = x2))) |
Some markets were indications. | exists x1.(_market(x1) & exists x2.(_indication(x2) & (x1 = x2))) |
Some Samaritans were Jews. | exists x1.(_samaritan(x1) & exists x2.(_jews(x2) & (x1 = x2))) |
Some Romans were benefactors. | exists x1.(_roman(x1) & exists x2.(_benefactor(x2) & (x1 = x2))) |
Some Arabs are Christians. | exists x1.(_arab(x1) & exists x2.(_christian(x2) & (x1 = x2))) |
Some people were slaves. | exists x1.(_people(x1) & exists x2.(_slave(x2) & (x1 = x2))) |
Some beggars are locals. | exists x1.(_beggar(x1) & exists x2.(_local(x2) & (x1 = x2))) |
Some Europeans are grasshoppers. | exists x1.(_european(x1) & exists x2.(_grasshopper(x2) & (x1 = x2))) |
Some states are members. | exists x1.(_state(x1) & exists x2.(_member(x2) & (x1 = x2))) |
Some point were awardees. | exists x1.(_point(x1) & exists x2.(_awardee(x2) & (x1 = x2))) |
Some vendors are laggards. | exists x1.(_vendor(x1) & exists x2.(_laggard(x2) & (x1 = x2))) |
Some things are nomdok. | exists x1.(_thing(x1) & _nomdok(x1)) |
Some Syrians are terrorists. | exists x1.(_syrian(x1) & exists x2.(_terrorist(x2) & (x1 = x2))) |
Some donors were people. | exists x1.(_donor(x1) & exists x2.(_people(x2) & (x1 = x2))) |
Some ideas are catalysts. | exists x1.(_idea(x1) & exists x2.(_catalyst(x2) & (x1 = x2))) |
Some suspicions are sins. | exists x1.(_suspicion(x1) & exists x2.(_sin(x2) & (x1 = x2))) |
Some minds were fears. | exists x1.(_mind(x1) & exists x2.(_fear(x2) & (x1 = x2))) |
Some Buddhists are hypocrites. | exists x1.(_buddhist(x1) & exists x2.(_hypocrite(x2) & (x1 = x2))) |
Some men are thieves. | exists x1.(_man(x1) & exists x2.(_thief(x2) & (x1 = x2))) |
Some time is cities. | exists x1.(_time(x1) & exists x2.(_city(x2) & (x1 = x2))) |
Some boats were children. | exists x1.(_boat(x1) & exists x2.(_child(x2) & (x1 = x2))) |
Some police are people. | exists x1.(_police(x1) & exists x2.(_people(x2) & (x1 = x2))) |
Some rhythm were necessities. | exists x1.(_rhythm(x1) & exists x2.(_necessity(x2) & (x1 = x2))) |
Some women are biddies. | exists x1.(_woman(x1) & exists x2.(_biddy(x2) & (x1 = x2))) |
Some stages are races. | exists x1.(_stage(x1) & exists x2.(_race(x2) & (x1 = x2))) |
Some people are connectors. | exists x1.(_people(x1) & exists x2.(_connector(x2) & (x1 = x2))) |
Some people are socialists. | exists x1.(_people(x1) & exists x2.(_socialist(x2) & (x1 = x2))) |
Some Democrats are die-hards. | exists x1.(_democrat(x1) & exists x2.(_die_dash_hard(x2) & (x1 = x2))) |
Some things are givens. | exists x1.(_thing(x1) & exists x2.(_given(x2) & (x1 = x2))) |
Some messengers are experts. | exists x1.(_messenger(x1) & exists x2.(_expert(x2) & (x1 = x2))) |
Some bouncers are assholes. | exists x1.(_bouncer(x1) & exists x2.(_asshole(x2) & (x1 = x2))) |
Some songs are classics. | exists x1.(_song(x1) & exists x2.(_classic(x2) & (x1 = x2))) |
Some extent were curricula. | exists x1.(_extent(x1) & exists x2.(_curriculum(x2) & (x1 = x2))) |
Some Mountains are fulgamites. | exists x1.(_mountain(x1) & exists x2.(_fulgamite(x2) & (x1 = x2))) |
Some antibiotics are mutagens. | exists x1.(_antibiotic(x1) & exists x2.(_mutagen(x2) & (x1 = x2))) |
Some people are multiples. | exists x1.(_people(x1) & exists x2.(_multiple(x2) & (x1 = x2))) |
Some practitioners are masters. | exists x1.(_practitioner(x1) & exists x2.(_master(x2) & (x1 = x2))) |
Some data is words. | exists x1.(_datum(x1) & exists x2.(_word(x2) & (x1 = x2))) |
Some men are officers. | exists x1.(_man(x1) & exists x2.(_officer(x2) & (x1 = x2))) |
Some people are members. | exists x1.(_people(x1) & exists x2.(_member(x2) & (x1 = x2))) |
Some bends are characters. | exists x1.(_bend(x1) & exists x2.(_character(x2) & (x1 = x2))) |
Some countries are children. | exists x1.(_country(x1) & exists x2.(_child(x2) & (x1 = x2))) |
Some people are potatoes. | exists x1.(_people(x1) & exists x2.(_potato(x2) & (x1 = x2))) |
Some anarchists are pacifists. | exists x1.(_anarchist(x1) & exists x2.(_pacifist(x2) & (x1 = x2))) |
Some cases are fronts. | exists x1.(_case(x1) & exists x2.(_front(x2) & (x1 = x2))) |
Some lines are trifles. | exists x1.(_line(x1) & exists x2.(_trifle(x2) & (x1 = x2))) |
Some men are fools. | exists x1.(_man(x1) & exists x2.(_fool(x2) & (x1 = x2))) |
Some trails are multi-purpose. | exists x1.(_trail(x1) & _multi_dash_purpose(x1)) |
Some people are winners. | exists x1.(_people(x1) & exists x2.(_winner(x2) & (x1 = x2))) |
Some residents are Christians. | exists x1.(_resident(x1) & exists x2.(_christian(x2) & (x1 = x2))) |
Some combatants are citizens. | exists x1.(_combatant(x1) & exists x2.(_citizen(x2) & (x1 = x2))) |
Some cases are superintendents. | exists x1.(_case(x1) & exists x2.(_superintendent(x2) & (x1 = x2))) |
Some countries are innovators. | exists x1.(_country(x1) & exists x2.(_innovator(x2) & (x1 = x2))) |
Some Kuna are traders. | exists x1.(_kuna(x1) & exists x2.(_trader(x2) & (x1 = x2))) |
Some plants are indicators. | exists x1.(_plant(x1) & exists x2.(_indicator(x2) & (x1 = x2))) |
Some nuts are seeds. | exists x1.(_nut(x1) & exists x2.(_seed(x2) & (x1 = x2))) |
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Dataset Card for text2log
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.
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