Measuring Sentiment Bias in Machine Translation

06/12/2023
by   Kai Hartung, et al.
0

Biases induced to text by generative models have become an increasingly large topic in recent years. In this paper we explore how machine translation might introduce a bias in sentiments as classified by sentiment analysis models. For this, we compare three open access machine translation models for five different languages on two parallel corpora to test if the translation process causes a shift in sentiment classes recognized in the texts. Though our statistic test indicate shifts in the label probability distributions, we find none that appears consistent enough to assume a bias induced by the translation process.

READ FULL TEXT
research
07/28/2020

Preparation of Sentiment tagged Parallel Corpus and Testing its effect on Machine Translation

In the current work, we explore the enrichment in the machine translatio...
research
12/15/2016

Building a robust sentiment lexicon with (almost) no resource

Creating sentiment polarity lexicons is labor intensive. Automatically t...
research
09/19/2020

Towards Computational Linguistics in Minangkabau Language: Studies on Sentiment Analysis and Machine Translation

Although some linguists (Rusmali et al., 1985; Crouch, 2009) have fairly...
research
12/13/2022

Towards a general purpose machine translation system for Sranantongo

Machine translation for Sranantongo (Sranan, srn), a low-resource Creole...
research
05/28/2018

A Stochastic Decoder for Neural Machine Translation

The process of translation is ambiguous, in that there are typically man...
research
05/08/2020

Latent Racial Bias – Evaluating Racism in Police Stop-and-Searches

In this paper, we introduce the latent racial bias, a metric and method ...
research
01/20/2023

Machine Translation for Accessible Multi-Language Text Analysis

English is the international standard of social research, but scholars a...

Please sign up or login with your details

Forgot password? Click here to reset