| | --- |
| | 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: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 14 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_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_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) |
| | - 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 |
| | |
| | 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 | |
| |
|
| |
|
| | ### 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 |
| |
|
| | ### 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. |
| |
|
