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add gender randomizer #229

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45 changes: 45 additions & 0 deletions transformations/gender_randomizer/README.md
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# Gender Randomizer 🦎 + ⌨️ → 🐍

Author name: Tabitha Sugumar
Author email: t.sugumar@elsevier.com
Author Affiliation: Elsevier

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Thanks for your changes @tk-sugumar . Please add your email and affiliation.

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Added!

## What type of a transformation is this?
This transformation changes names in English texts, randomizing selection so there's an even chance of male and female names. It modifies pronouns to match the selected name.
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Please add an acknowledgement that names are not deterministic identifiers of someones pronouns/gender :)

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Added!


## What tasks does it intend to benefit?
This is intended to avoid gender bias in natural language processing models. Run this transformation on text data prior to using it to train a model.

## Previous Work
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Importantly please add a Data and Code Provenance section to your transformation. Also, seems you've added about a 109 files which are hard to evaluate. I would suggest moving this into a separate pip project out of this and then adding it to the requirements.txt.

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Thanks! I've expanded on the data and code provenance, and put the description in a Data and Code Provenance section in the Readme.

On the 109 files -- most of them come from the coreferee directory -- this actually already exists as a library installable by pip, but when I was working on this was only installable in python 3.8 and the current version requires python 3.9. Since these transformations are required to be compatible with python 3.7, I downloaded here to make it installable in python 3.7.

This uses the coreferee library (https://github.com/msg-systems/coreferee). The code is downloaded and included locally, to allow for slight modifications to the setup file to allow for installation in python 3.7, as required for this transformations (the coreferee library was designed/tested in python 3.8).

The names directory comes from https://www.kaggle.com/nltkdata/names. The README within the directory provides more detail.

## What are the limitations of this transformation?
This transformation does not handle gendered words such as actor/actess, waiter/waitress, etc. The handling of pronouns is limited to what the coreferee library can identify, and, in this version is limited to the segment of text fed in one iteration (ie: if a text is separated and fed in batches, the pronouns/names will not be consistent accross the text).

## Examples of this transformation

Because this is a randomized transformation, in both the selection of gender and selection of name, test examples are impossible -- the output for a single sentence is expected to be different in each successive run. Instead I've provided some example sentences and outputs for reference.
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I believe you can use a default seed in the argument in init of your GenderRandomizer transformation so you can generate consistent results for your test cases so you can include them in your test.json

Quite a few of the PRs use this approach for test cases.

See for example:
https://github.com/GEM-benchmark/NL-Augmenter/pull/164/files

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@tk-sugumar tk-sugumar Sep 12, 2021

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Thanks Timothy! When I tried this, the same name was predicted for each sentence, so for use as intended I think the user would have to modify the code after downloading. Should I still go ahead and do this?

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Hi Timothy, I added in the seed in the initializer, the name names does get predicted each time though, I hope it's ok! Test cases are also added in the test.json


1) Input: '“Edward turned to Miss Marple. “It’s like this, you see. As Uncle Mathew grew older, he got more and more suspicious. He didn’t trust anybody.” “Very wise of him,” said Miss Marple. “The depravity of human nature is unbelievable.” '

Possible Output: '“Edward turned to Tandie. “It’s like this, you see. As Elvira grew older, she got more and more suspicious. She didn’t trust anybody.” “Very wise of her,” said Tandie. “The depravity of human nature is unbelievable.” '

2) Input: 'Alex told me the hat was his.'

Possible Output: 'Emeline told me the hat was hers.'

3) Input: 'Angela wanted to study abroad that summer but she decided to travel with her friends instead.'

Possible Output: 'Dominique wanted to study abroad that summer but he decided to travel with his friends instead.'

4) Input: 'I thought that Michael would go to medschool, but he told me he was applying for law.'

Possible Output: 'I thought that Arabele would go to medschool, but she told me she was applying for law.'

5) Input: 'Mattias went to New York for Christmas last year, but he wanted to stay with family for New Years.'

Possible Output: 'Dinah went to New York for Christmas last year, but she wanted to stay with family for New Years.'


1 change: 1 addition & 0 deletions transformations/gender_randomizer/__init__.py
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from .transformation import *
129 changes: 129 additions & 0 deletions transformations/gender_randomizer/coreferee/.gitignore
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# Byte-compiled / optimized / DLL files
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# Pyre type checker
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201 changes: 201 additions & 0 deletions transformations/gender_randomizer/coreferee/LICENSE
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4 changes: 4 additions & 0 deletions transformations/gender_randomizer/coreferee/NOTICE
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