Pinned Repositories
01-desafio
how_to
JFLEX
Lexical analyzer for C
madelbrot_pthread
petinder
unbIAs
winopt
VanessaSchenkel's Repositories
VanessaSchenkel/madelbrot_pthread
VanessaSchenkel/how_to
VanessaSchenkel/JFLEX
Lexical analyzer for C
VanessaSchenkel/unbIAs
VanessaSchenkel/winopt
VanessaSchenkel/02-desafio
VanessaSchenkel/02-dtmoney
VanessaSchenkel/04-letmeask
VanessaSchenkel/adminescolas
Trabalho de banco de dados
VanessaSchenkel/annotations
VanessaSchenkel/awesome-fairness-papers
Papers on fairness in NLP
VanessaSchenkel/cg
VanessaSchenkel/codewars
VanessaSchenkel/contrastive-conditioning
Code and data accompanying the paper "Contrastive Conditioning for Assessing Disambiguation in MT: A Case Study of Distilled Bias"
VanessaSchenkel/corefBias
To analyze and remove gender bias in coreference resolution systems
VanessaSchenkel/debiaswe
Remove problematic gender bias from word embeddings.
VanessaSchenkel/FrequencyWords
Repository for Frequency Word List Generator and processed files
VanessaSchenkel/jef
VanessaSchenkel/LM_bias
[ICML 2021] Towards Understanding and Mitigating Social Biases in Language Models
VanessaSchenkel/mitigating-gender-bias-wmt-2020
A project that explores using word grammatical genders as an additional source of information in neural machine translation.
VanessaSchenkel/mpitutorial
MPI programming lessons in C and executable code examples
VanessaSchenkel/myblog
VanessaSchenkel/NatGenMT
This dataset is intended as an evaluation benchmark for gender issues in Machine Translation. We consider the challenges in modeling and handling gendered language in the context of machine translation and extend over previous work that identifies issues using synthetic examples. We focus on the class of issues which surface when a neutral reference to a person is translated to a gendered form. For this class of examples, the MT task requires a system to produce a single translation without source cues, thus exposing a model's preferred gender for the reference form. We include English source sentences, and four target gendered languages across three language families (French, German, Spanish, and Russian). The examples included in the dataset expose where MT encodings are gendered, finding new issues not covered in previous manual approaches.
VanessaSchenkel/nlp-in-python-tutorial
comparing stand up comedians using natural language processing
VanessaSchenkel/nmt-gender-rerank
Scripts and nbest lists for "First the worst: Finding better gender translations during beam search" (Findings of ACL 2022)
VanessaSchenkel/pt-inflections
VanessaSchenkel/rede-petri
VanessaSchenkel/teste
VanessaSchenkel/transl
VanessaSchenkel/Translation-Gender-Bias