Datasets:

rfernand
/

basic_sentence_transforms

	---
	annotations_creators:
	- machine-generated
	language:
	- en
	language_creators:
	- machine-generated
	license:
	- other
	multilinguality:
	- monolingual
	pretty_name: Active/Passive/Logical Transforms
	size_categories:
	- 10K<n<100K
	- 1K<n<10K
	- n<1K
	source_datasets:
	- original
	tags:
	- struct2struct
	- tree2tree
	task_categories:
	- text2text-generation
	task_ids: []
	---
	# Dataset Card for Active/Passive/Logical Transforms

	## 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)
	- [Dataset Subsets (Tasks)](#data-tasks)
	- [Dataset Splits](#data-splits)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [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:
	- Repository:
	- Paper:
	- Leaderboard:
	- Point of Contact: [Roland Fernandez](mailto:[email protected])

	### Dataset Summary

	This dataset is a synthetic dataset containing structure-to-structure transformation tasks between
	English sentences in 3 forms: active, passive, and logical. The dataset also includes several
	tree-transformation diagnostic/warm-up tasks.

	### Supported Tasks and Leaderboards

	[TBD]

	### Languages

	All data is in English.

	## Dataset Structure

	The dataset consists of several subsets, or tasks. Each task contains a train split, a validation split, and a
	test split, with most tasks also containing two out-of-distruction splits (one for new adjectives and one for longer adjective phrases).

	Each sample in a split contains a source string, a target string, and 0-2 annotation strings.

	### Dataset Subsets (Tasks)
	The dataset consists of diagnostic/warm-up tasks and core tasks. The core tasks represent the translation of English sentences between the active, passive, and logical forms.

	The 12 diagnostic/warm-up tasks are:

	```
	- car_cdr_cons (small phrase translation tasks that require only: CAR, CDR, or CAR+CDR+CONS operations)
	- car_cdr_cons_tuc (same task as car_cdr_cons, but requires mapping lowercase fillers to their uppercase tokens)
	- car_cdr_rcons (same task as car_cdr_cons, but the CONS samples have their left/right children swapped)
	- car_cdr_rcons_tuc (same task as car_cdr_rcons, but requires mapping lowercase fillers to their uppercase tokens)
	- car_cdr_seq (each samples requires 1-4 combinations of CAR and CDR, as identified by the root filler oken)
	- car_cdr_seq_40k (same task as car_cdr_seq, but train samples increased from 10K to 40K)
	- car_cdr_seq_tuc (same task as car_cdr_seq, but requires mapping lowercase fillers to their uppercase tokens)
	- car_cdr_seq_40k_tuc (same task as car_cdr_seq_tuc, but train samples increased from 10K to 40K)
	- car_cdr_seq_path (similiar to car_cdr_seq, but each needed operation in represented as a node in the left child of the root)
	- car_cdr_seq_path_40k (same task as car_cdr_seq_path, but train samples increased from 10K to 40K)
	- car_cdr_seq_path_40k_tuc (same task as car_cdr_seq_path_40k, but requires mapping lowercase fillers to their uppercase tokens)
	- car_cdr_seq_path_tuc (same task as car_cdr_seq_path, but requires mapping lowercase fillers to their uppercase tokens)
	```

	There are 22 core tasks are:
	```
	- active_active_stb (active sentence translation, from sentence to parenthesized tree form, both directions)
	- active_active_stb_40k (same task as active_active_stb, but train samples increased from 10K to 40K)

	- active_logical_ssb (active to logical sentence translation, in both directions)
	- active_logical_ssb_40k (same task as active_logical_ssb, but train samples increased from 10K to 40K)
	- active_logical_ttb (active to logical tree translation, in both directions)
	- active_logical_ttb_40k (same task as active_logical_ttb, but train samples increased from 10K to 40K)

	- active_passive_ssb (active to passive sentence translation, in both directions)
	- active_passive_ssb_40k (same task as active_passive_ssb, but train samples increased from 10K to 40K)
	- active_passive_ttb (active to passive tree translation, in both directions)
	- active_passive_ttb_40k (same task as active_passive_ttb, but train samples increased from 10K to 40K)

	- actpass_logical_ss (mixture of active to logical and passive to logical sentence translations, single direction)
	- actpass_logical_ss_40k (same task as actpass_logical_ss, but train samples increased from 10K to 40K)
	- actpass_logical_tt (mixture of active to logical and passive to logical tree translations, single direction)
	- actpass_logical_tt_40k (same task as actpass_logical_tt, but train samples increased from 10K to 40K)

	- logical_logical_stb (logical form sentence translation, from sentence to parenthesized tree form, both directions)
	- logical_logical_stb_40k (same task as logical_logical_stb, but train samples increased from 10K to 40K)

	- passive_logical_ssb (passive to logical sentence translation, in both directions)
	- passive_logical_ssb_40k (same task as passive_logical_ssb, but train samples increased from 10K to 40K)
	- passive_logical_ttb (passive to logical tree translation, in both directions)
	- passive_logical_ttb_40k (same task as passive_logical_ttb, but train samples increased from 10K to 40K)

	- passive_passive_stb (passive sentence translation, from sentence to parenthesized tree form, both directions)
	- passive_passive_stb_40k (same task as passive_passive_stb, but train samples increased from 10K to 40K)
	```

	### Data Splits

	Most tasks have the following splits:
	- train
	- validation
	- test
	- ood_new
	- ood_long
	- ood_all

	Here is a table showing how the number of examples varies by split (for most tasks):

	\| Dataset Split \| Number of Instances in Split \|
	\| ------------- \| ------------------------------------------- \|
	\| train \| 10,000 \|
	\| validation \| 1,250 \|
	\| test \| 1,250 \|
	\| ood_new \| 1,250 \|
	\| ood_long \| 1,250 \|
	\| ood_all \| 1,250 \|


	### Data Instances

	For each sample, there is source and target string. Source and target string are either plain text, or a parenthesized
	version of a tree, depending on the task.

	Here is an example from the train split of the active_passive_ttb task:

	```
	{
	'source': '( S ( NP ( DET his ) ( AP ( N cat ) ) ) ( VP ( V discovered ) ( NP ( DET the ) ( AP ( ADJ blue ) ( AP ( N priest ) ) ) ) ) )',
	'target': '( S ( NP ( DET the ) ( AP ( ADJ blue ) ( AP ( N priest ) ) ) ) ( VP ( AUXPS was ) ( VPPS ( V discovered ) ( PPPS ( PPS by ) ( NP ( DET his ) ( AP ( N cat ) ) ) ) ) ) )',
	'direction': 'forward'
	}
	```

	### Data Fields

	- `source`: the string denoting the sequence or tree structure to be translated
	- `target`: the string denoting the gold (aka label) sequence or tree structure

	Optional annotation fields (their presence varies by task):

	- `direction`: describes the direction of the translation (forward, backward), relative to the task name
	- `count` : a string denoting the count of symbolic operations needed (e.g., "s3") to translate the source to the target
	- `class` : a string denoting the type of translation needed

	## Dataset Creation

	### Curation Rationale

	We wanted a dataset comprised of relatively simple English active/passive/logical form translations, where we could focus
	on two types of out of distribution generalization: longer source sequences and new adjectives.

	### Source Data

	[N/A]

	#### Initial Data Collection and Normalization

	[N/A]

	#### Who are the source language producers?

	The dataset by generated from templates designed by Paul Smolensky and Roland Fernandez.

	### Annotations

	Besides the source and target structured sequences, some of the subsets (tasks) contain 1-2 additional columns that
	describe the category and tree depth of each sample.

	#### Annotation process

	The annotation columns were generated from the each sample template and source sequence.

	#### Who are the annotators?

	[N/A]

	### Personal and Sensitive Information

	No names or other sensitive information are included in the data.

	## Considerations for Using the Data

	### Social Impact of Dataset

	The purpose of this dataset is to help develop models that can translated structured data from one form to another, in a
	way that generalizes to out of distribution adjective values and lengths.

	### Discussion of Biases

	[TBD]

	### Other Known Limitations

	[TBD]

	## Additional Information

	The internal name of this dataset is nc_pat.

	### Dataset Curators

	The dataset by generated from templates designed by Paul Smolensky and Roland Fernandez.

	### Licensing Information

	This dataset is released under the [Permissive 2.0 license](https://cdla.dev/permissive-2-0/).

	### Citation Information

	[TBD]

	### Contributions

	Thanks to [The Neurocompositional AI group at Microsoft Research](https://www.microsoft.com/en-us/research/project/neurocompositional-ai/) for creating and adding this dataset.