Learning Gender-Neutral Word Embeddings
Jieyu Zhao,Yichao Zhou,Zeyu Li,Wei Wang,Kai-Wei Chang +4 more
- pp 4847-4853
TLDR
This article proposed a novel training procedure for learning gender-neutral word embeddings, which aims to preserve gender information in certain dimensions of word vectors while compelling other dimensions to be free of gender influence.Abstract:
Word embedding models have become a fundamental component in a wide range of Natural Language Processing (NLP) applications However, embeddings trained on human-generated corpora have been demonstrated to inherit strong gender stereotypes that reflect social constructs To address this concern, in this paper, we propose a novel training procedure for learning gender-neutral word embeddings Our approach aims to preserve gender information in certain dimensions of word vectors while compelling other dimensions to be free of gender influence Based on the proposed method, we generate a Gender-Neutral variant of GloVe (GN-GloVe) Quantitative and qualitative experiments demonstrate that GN-GloVe successfully isolates gender information without sacrificing the functionality of the embedding modelread more
Citations
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References
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Proceedings ArticleDOI
Glove: Global Vectors for Word Representation
TL;DR: A new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods and produces a vector space with meaningful substructure.
Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
Posted Content
Efficient Estimation of Word Representations in Vector Space
TL;DR: This paper proposed two novel model architectures for computing continuous vector representations of words from very large data sets, and the quality of these representations is measured in a word similarity task and the results are compared to the previously best performing techniques based on different types of neural networks.
Proceedings Article
Neural Machine Translation by Jointly Learning to Align and Translate
TL;DR: It is conjecture that the use of a fixed-length vector is a bottleneck in improving the performance of this basic encoder-decoder architecture, and it is proposed to extend this by allowing a model to automatically (soft-)search for parts of a source sentence that are relevant to predicting a target word, without having to form these parts as a hard segment explicitly.
Journal ArticleDOI
WordNet : an electronic lexical database
TL;DR: The lexical database: nouns in WordNet, Katherine J. Miller a semantic network of English verbs, and applications of WordNet: building semantic concordances are presented.