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--- |
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annotations_creators: |
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- machine-generated |
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language: |
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- en |
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language_creators: |
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- machine-generated |
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license: |
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- other |
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multilinguality: |
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- monolingual |
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pretty_name: Active/Passive/Logical Transforms |
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size_categories: |
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- 10K<n<100K |
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- 1K<n<10K |
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- n<1K |
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source_datasets: |
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- original |
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tags: |
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- struct2struct |
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- tree2tree |
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task_categories: |
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- text2text-generation |
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task_ids: [] |
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--- |
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# Dataset Card for Active/Passive/Logical Transforms |
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## Table of Contents |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Dataset Subsets (Tasks)](#data-tasks) |
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- [Dataset Splits](#data-splits) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Dataset Creation](#dataset-creation) |
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- [Curation Rationale](#curation-rationale) |
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- [Source Data](#source-data) |
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- [Annotations](#annotations) |
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- [Personal and Sensitive Information](#personal-and-sensitive-information) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Discussion of Biases](#discussion-of-biases) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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## Dataset Description |
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- **Homepage:** |
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- **Repository:** |
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- **Paper:** |
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- **Leaderboard:** |
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- **Point of Contact:** [Roland Fernandez](mailto:[email protected]) |
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### Dataset Summary |
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This dataset is a synthetic dataset containing structure-to-structure transformation tasks between |
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English sentences in 3 forms: active, passive, and logical. The dataset also includes several |
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tree-transformation diagnostic/warm-up tasks. |
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### Supported Tasks and Leaderboards |
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[TBD] |
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### Languages |
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All data is in English. |
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## Dataset Structure |
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The dataset consists of several subsets, or tasks. Each task contains a train split, a validation split, and a |
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test split, with most tasks also containing two out-of-distruction splits (one for new adjectives and one for longer adjective phrases). |
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Each sample in a split contains a source string, a target string, and 0-2 annotation strings. |
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### Dataset Subsets (Tasks) |
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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. |
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The 12 diagnostic/warm-up tasks are: |
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``` |
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- car_cdr_cons (small phrase translation tasks that require only: CAR, CDR, or CAR+CDR+CONS operations) |
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- car_cdr_cons_tuc (same task as car_cdr_cons, but requires mapping lowercase fillers to their uppercase tokens) |
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- car_cdr_rcons (same task as car_cdr_cons, but the CONS samples have their left/right children swapped) |
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- car_cdr_rcons_tuc (same task as car_cdr_rcons, but requires mapping lowercase fillers to their uppercase tokens) |
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- car_cdr_seq (each samples requires 1-4 combinations of CAR and CDR, as identified by the root filler oken) |
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- car_cdr_seq_40k (same task as car_cdr_seq, but train samples increased from 10K to 40K) |
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- car_cdr_seq_tuc (same task as car_cdr_seq, but requires mapping lowercase fillers to their uppercase tokens) |
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- car_cdr_seq_40k_tuc (same task as car_cdr_seq_tuc, but train samples increased from 10K to 40K) |
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- 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) |
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- car_cdr_seq_path_40k (same task as car_cdr_seq_path, but train samples increased from 10K to 40K) |
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- car_cdr_seq_path_40k_tuc (same task as car_cdr_seq_path_40k, but requires mapping lowercase fillers to their uppercase tokens) |
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- car_cdr_seq_path_tuc (same task as car_cdr_seq_path, but requires mapping lowercase fillers to their uppercase tokens) |
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``` |
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There are 22 core tasks are: |
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``` |
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- active_active_stb (active sentence translation, from sentence to parenthesized tree form, both directions) |
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- active_active_stb_40k (same task as active_active_stb, but train samples increased from 10K to 40K) |
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- active_logical_ssb (active to logical sentence translation, in both directions) |
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- active_logical_ssb_40k (same task as active_logical_ssb, but train samples increased from 10K to 40K) |
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- active_logical_ttb (active to logical tree translation, in both directions) |
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- active_logical_ttb_40k (same task as active_logical_ttb, but train samples increased from 10K to 40K) |
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- active_passive_ssb (active to passive sentence translation, in both directions) |
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- active_passive_ssb_40k (same task as active_passive_ssb, but train samples increased from 10K to 40K) |
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- active_passive_ttb (active to passive tree translation, in both directions) |
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- active_passive_ttb_40k (same task as active_passive_ttb, but train samples increased from 10K to 40K) |
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- actpass_logical_ss (mixture of active to logical and passive to logical sentence translations, single direction) |
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- actpass_logical_ss_40k (same task as actpass_logical_ss, but train samples increased from 10K to 40K) |
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- actpass_logical_tt (mixture of active to logical and passive to logical tree translations, single direction) |
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- actpass_logical_tt_40k (same task as actpass_logical_tt, but train samples increased from 10K to 40K) |
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- logical_logical_stb (logical form sentence translation, from sentence to parenthesized tree form, both directions) |
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- logical_logical_stb_40k (same task as logical_logical_stb, but train samples increased from 10K to 40K) |
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- passive_logical_ssb (passive to logical sentence translation, in both directions) |
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- passive_logical_ssb_40k (same task as passive_logical_ssb, but train samples increased from 10K to 40K) |
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- passive_logical_ttb (passive to logical tree translation, in both directions) |
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- passive_logical_ttb_40k (same task as passive_logical_ttb, but train samples increased from 10K to 40K) |
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- passive_passive_stb (passive sentence translation, from sentence to parenthesized tree form, both directions) |
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- passive_passive_stb_40k (same task as passive_passive_stb, but train samples increased from 10K to 40K) |
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``` |
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### Data Splits |
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Most tasks have the following splits: |
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- train |
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- validation |
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- test |
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- ood_new |
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- ood_long |
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- ood_all |
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Here is a table showing how the number of examples varies by split (for most tasks): |
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| Dataset Split | Number of Instances in Split | |
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| ------------- | ------------------------------------------- | |
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| train | 10,000 | |
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| validation | 1,250 | |
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| test | 1,250 | |
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| ood_new | 1,250 | |
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| ood_long | 1,250 | |
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| ood_all | 1,250 | |
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### Data Instances |
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For each sample, there is source and target string. Source and target string are either plain text, or a parenthesized |
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version of a tree, depending on the task. |
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Here is an example from the *train* split of the *active_passive_ttb* task: |
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``` |
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{ |
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'source': '( S ( NP ( DET his ) ( AP ( N cat ) ) ) ( VP ( V discovered ) ( NP ( DET the ) ( AP ( ADJ blue ) ( AP ( N priest ) ) ) ) ) )', |
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'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 ) ) ) ) ) ) )', |
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'direction': 'forward' |
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} |
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``` |
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### Data Fields |
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- `source`: the string denoting the sequence or tree structure to be translated |
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- `target`: the string denoting the gold (aka label) sequence or tree structure |
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Optional annotation fields (their presence varies by task): |
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- `direction`: describes the direction of the translation (forward, backward), relative to the task name |
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- `count` : a string denoting the count of symbolic operations needed (e.g., "s3") to translate the source to the target |
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- `class` : a string denoting the type of translation needed |
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## Dataset Creation |
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### Curation Rationale |
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We wanted a dataset comprised of relatively simple English active/passive/logical form translations, where we could focus |
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on two types of out of distribution generalization: longer source sequences and new adjectives. |
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### Source Data |
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[N/A] |
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#### Initial Data Collection and Normalization |
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[N/A] |
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#### Who are the source language producers? |
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The dataset by generated from templates designed by Paul Smolensky and Roland Fernandez. |
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### Annotations |
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Besides the source and target structured sequences, some of the subsets (tasks) contain 1-2 additional columns that |
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describe the category and tree depth of each sample. |
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#### Annotation process |
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The annotation columns were generated from the each sample template and source sequence. |
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#### Who are the annotators? |
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[N/A] |
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### Personal and Sensitive Information |
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No names or other sensitive information are included in the data. |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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The purpose of this dataset is to help develop models that can translated structured data from one form to another, in a |
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way that generalizes to out of distribution adjective values and lengths. |
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### Discussion of Biases |
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[TBD] |
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### Other Known Limitations |
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[TBD] |
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## Additional Information |
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The internal name of this dataset is nc_pat. |
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### Dataset Curators |
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The dataset by generated from templates designed by Paul Smolensky and Roland Fernandez. |
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### Licensing Information |
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This dataset is released under the [Permissive 2.0 license](https://cdla.dev/permissive-2-0/). |
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### Citation Information |
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[TBD] |
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### Contributions |
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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. |
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