# 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:rfernand@microsoft.com)

### 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.

task_categories:text2text-generation annotations_creators:machine-generated language_creators:machine-generated multilinguality:monolingual size_categories:10K<n<100K size_categories:1K<n<10K size_categories:n<1K source_datasets:original language:en license:other struct2struct tree2tree region:us
{"annotations_creators": ["machine-generated"], "language_creators": ["machine-generated"], "language": ["en"], "license": ["other"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K", "1K<n<10K", "n<1K"], "source_datasets": ["original"], "task_categories": ["text2text-generation"], "task_ids": [], "pretty_name": "Active/Passive/Logical Transforms", "tags": ["struct2struct", "tree2tree"]}
TAGS
#task_categories-text2text-generation #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-n<1K #source_datasets-original #language-English #license-other #struct2struct #tree2tree #region-us 

Dataset Card for Active/Passive/Logical Transforms
==================================================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Dataset Subsets (Tasks)
	+ Dataset Splits
	+ Data Instances
	+ Data Fields
* 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:
* Repository:
* Paper:
* Leaderboard:
* Point of Contact: Roland Fernandez


### 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:


There are 22 core tasks are:


### 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):



### 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:


### 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.


[TBD]


### Contributions


Thanks to The Neurocompositional AI group at Microsoft Research for creating and adding this dataset.



### 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:


There are 22 core tasks are: ### 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): ### 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: ### 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.


[TBD] ### Contributions


Thanks to The Neurocompositional AI group at Microsoft Research for creating and adding this dataset.
TAGS
#task_categories-text2text-generation #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-n<1K #source_datasets-original #language-English #license-other #struct2struct #tree2tree #region-us 
 ### 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:


There are 22 core tasks are: ### 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): ### 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: ### 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.


[TBD] ### Contributions


Thanks to The Neurocompositional AI group at Microsoft Research for creating and adding this dataset.
passage: TAGS
#task_categories-text2text-generation #annotations_creators-machine-generated #language_creators-machine-generated #multilinguality-monolingual #size_categories-10K<n<100K #size_categories-1K<n<10K #size_categories-n<1K #source_datasets-original #language-English #license-other #struct2struct #tree2tree #region-us 
### 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:


There are 22 core tasks are:### 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):### 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: