163 research outputs found
Causally Denoise Word Embeddings Using Half-Sibling Regression
Distributional representations of words, also known as word vectors, have
become crucial for modern natural language processing tasks due to their wide
applications. Recently, a growing body of word vector postprocessing algorithm
has emerged, aiming to render off-the-shelf word vectors even stronger. In line
with these investigations, we introduce a novel word vector postprocessing
scheme under a causal inference framework. Concretely, the postprocessing
pipeline is realized by Half-Sibling Regression (HSR), which allows us to
identify and remove confounding noise contained in word vectors. Compared to
previous work, our proposed method has the advantages of interpretability and
transparency due to its causal inference grounding. Evaluated on a battery of
standard lexical-level evaluation tasks and downstream sentiment analysis
tasks, our method reaches state-of-the-art performance.Comment: Accepted by AAAI 202
A Causal Inference Method for Reducing Gender Bias in Word Embedding Relations
Word embedding has become essential for natural language processing as it
boosts empirical performances of various tasks. However, recent research
discovers that gender bias is incorporated in neural word embeddings, and
downstream tasks that rely on these biased word vectors also produce
gender-biased results. While some word-embedding gender-debiasing methods have
been developed, these methods mainly focus on reducing gender bias associated
with gender direction and fail to reduce the gender bias presented in word
embedding relations. In this paper, we design a causal and simple approach for
mitigating gender bias in word vector relation by utilizing the statistical
dependency between gender-definition word embeddings and gender-biased word
embeddings. Our method attains state-of-the-art results on gender-debiasing
tasks, lexical- and sentence-level evaluation tasks, and downstream coreference
resolution tasks.Comment: Accepted by AAAI 202
A Hybrid SIE-PDE Formulation Without Boundary Condition Requirement for Transverse Magnetic Electromagnetic Analysis
A hybrid surface integral equation partial differential equation (SIE-PDE)
formulation without the boundary condition requirement is proposed to solve the
transverse magnetic (TM) electromagnetic problems. In the proposed formulation,
the computational domain is decomposed into two overlapping domains: the SIE
and PDE domains. In the SIE domain, complex structures with piecewise
homogeneous media, e.g., highly conductive media, are included. An equivalent
model for those structures is constructed by replacing them with the background
medium and introducing a surface equivalent electric current density on an
enclosed boundary to represent their electromagnetic effects. The remaining
computational domain and homogeneous background medium replaced domain consist
of the PDE domain, in which inhomogeneous or non-isotropic media are included.
Through combining the surface equivalent electric current density and the
inhomogeneous Helmholtz equation, a hybrid SIE-PDE formulation is derived. It
requires no boundary conditions, and is mathematically equivalent to the
original physical model. Through careful construction of basis functions to
expand electric fields and the equivalent current density, the discretized
formulation is made compatible with the SIE and PDE domain interface. The
accuracy and efficiency are validated through two numerical examples. Results
show that the proposed SIE-PDE formulation can obtain accurate results, and
significant performance improvements in terms of CPU time and memory
consumption compared with the FEM are achieved
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