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Paper

Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem

by Independent / Community 00059087c954c1af6ece33115315e3e0ecc2f2c2

Free2AITools Nexus Index

70.7

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 88

P: Popularity 65

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Training data for NLP tasks often exhibits gender bias in that fewer sentences refer to women than to men. In Neural Machine Translation (NMT) gender bias has been shown to reduce translation quality, particularly when the target language has grammatical gender. The recent WinoMT challenge set allows us to measure this effect directly (Stanovsky et al, 2019) Ideally we would reduce system bias by simply debiasing all data prior to training, but achieving this effectively is itself a challenge...

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Semantic Scholar 157 Citations

Paper Information Summary
Entity Passport
Registry ID	00059087c954c1af6ece33115315e3e0ecc2f2c2
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{00059087c954c1af6ece33115315e3e0ecc2f2c2,
  author = {Unknown},
  title = {Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/00059087c954c1af6ece33115315e3e0ecc2f2c2}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem [Paper]. Free2AITools. https://api.semanticscholar.org/00059087c954c1af6ece33115315e3e0ecc2f2c2

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 88

Popularity (P) 65

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem: Authority (A:88), Popularity (P:65), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

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📝 Executive Summary

"Training data for NLP tasks often exhibits gender bias in that fewer sentences refer to women than to men. In Neural Machine Translation (NMT) gender bias has been shown to reduce translation quality, particularly when the target language has grammatical gender. The recent WinoMT challenge set allows us to measure this effect directly (Stanovsky et al, 2019) Ideally we would reduce system bias by simply debiasing all data prior to training, but achieving this effectively is itself a challenge..."

❝ Cite Node

@article{Unknown2026Reducing,
  title={Reducing Gender Bias in Neural Machine Translation as a Domain Adaptation Problem},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

📊 Research Signals

📈157CitationsSemantic Scholar

🏛️88AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️text generationField

📦Data Source: semantic_scholar

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Source summary: Based on semantic_scholar metadata. Not a recommendation.

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🛡️ Paper Transparency Report

Technical metadata sourced from upstream repositories.

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

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downloads: 0
stars: null
forks: null
citations: 157

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