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:[e-mail])

### 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.

原始信息汇总

数据集概述

数据集名称

• 名称: Active/Passive/Logical Transforms

数据集摘要

• 摘要: 该数据集是一个包含结构到结构转换任务的合成数据集，涉及英语句子的三种形式：主动、被动和逻辑。数据集还包括多个树转换诊断/热身任务。

语言

• 语言: 英语

数据集大小

• 大小:
  • 10K<n<100K
  • 1K<n<10K
  • n<1K

数据集来源

• 来源: 原始数据

数据集标签

• 标签: struct2struct, tree2tree

任务类别

• 任务类别: text2text-generation

数据集结构

数据集子集（任务）

• 子集:
  • 诊断/热身任务: 共12个，如car_cdr_cons, car_cdr_cons_tuc等。
  • 核心任务: 共22个，如active_active_stb, active_logical_ssb等。

数据集分割

• 分割:
  • 训练集
  • 验证集
  • 测试集
  • ood_new
  • ood_long
  • ood_all

数据实例

• 实例: 每个样本包含源字符串、目标字符串及0-2个注释字符串。

数据字段

• 字段:
  • source: 源字符串，表示待转换的序列或树结构。
  • target: 目标字符串，表示黄金（即标签）序列或树结构。
  • direction: 描述转换方向（正向、反向）。
  • count: 描述转换所需的符号操作计数。
  • class: 描述所需的翻译类型。

数据集创建

数据集生成

• 生成方式: 机器生成
• 设计者: Paul Smolensky 和 Roland Fernandez

许可证

• 许可证: 其他

个人和敏感信息

• 信息: 数据集中不包含姓名或其他敏感信息。