ar-seq2seq-gender (encoder)

This is a seq2seq model (encoder half) to "flip" gender in first-person Arabic sentences. The model can augment your existing Arabic data, or generate counterfactuals to test a model's decisions (would changing the gender of the subject or speaker change output?).

Intended Examples:

'أنا سعيد' <=> 'انا سعيدة'
'ركض إلى المتجر' <=> 'ركضت إلى المتجر'

People's names, gender pronouns, gendered words (father, mother), and many other values are currently unchanged by this model. Future versions may be trained on more data.

Sample Code

import torch
from transformers import AutoTokenizer, EncoderDecoderModel

model = EncoderDecoderModel.from_encoder_decoder_pretrained(
  "monsoon-nlp/ar-seq2seq-gender-encoder",
  "monsoon-nlp/ar-seq2seq-gender-decoder",
  min_length=40
)
tokenizer = AutoTokenizer.from_pretrained('monsoon-nlp/ar-seq2seq-gender-decoder') # same as MARBERT original

input_ids = torch.tensor(tokenizer.encode("أنا سعيدة")).unsqueeze(0)
generated = model.generate(input_ids, decoder_start_token_id=model.config.decoder.pad_token_id)
tokenizer.decode(generated.tolist()[0][1 : len(input_ids[0]) - 1])
> 'انا سعيد'

https://colab.research.google.com/drive/1S0kE_2WiV82JkqKik_sBW-0TUtzUVmrV?usp=sharing

Training

I originally developed a gender flip Python script for Spanish sentences, using BETO, and spaCy. More about this project: https://medium.com/ai-in-plain-english/gender-bias-in-spanish-bert-1f4d76780617

The Arabic model encoder and decoder started with weights and vocabulary from MARBERT from UBC-NLP, and was trained on the Arabic Parallel Gender Corpus from NYU Abu Dhabi. The text is first-person sentences from OpenSubtitles, with parallel gender-reinflected sentences generated by Arabic speakers.

Training notebook: https://colab.research.google.com/drive/1TuDfnV2gQ-WsDtHkF52jbn699bk6vJZV

Non-binary gender

This model is useful to generate male and female text samples, but falls short of capturing gender diversity in the world and in the Arabic language. This subject is discussed in the bias statement of the Gender Reinflection paper.