403 research outputs found
Factors Influencing the Surprising Instability of Word Embeddings
Despite the recent popularity of word embedding methods, there is only a
small body of work exploring the limitations of these representations. In this
paper, we consider one aspect of embedding spaces, namely their stability. We
show that even relatively high frequency words (100-200 occurrences) are often
unstable. We provide empirical evidence for how various factors contribute to
the stability of word embeddings, and we analyze the effects of stability on
downstream tasks.Comment: NAACL HLT 201
Characterizing the impact of geometric properties of word embeddings on task performance
Analysis of word embedding properties to inform their use in downstream NLP
tasks has largely been studied by assessing nearest neighbors. However,
geometric properties of the continuous feature space contribute directly to the
use of embedding features in downstream models, and are largely unexplored. We
consider four properties of word embedding geometry, namely: position relative
to the origin, distribution of features in the vector space, global pairwise
distances, and local pairwise distances. We define a sequence of
transformations to generate new embeddings that expose subsets of these
properties to downstream models and evaluate change in task performance to
understand the contribution of each property to NLP models. We transform
publicly available pretrained embeddings from three popular toolkits (word2vec,
GloVe, and FastText) and evaluate on a variety of intrinsic tasks, which model
linguistic information in the vector space, and extrinsic tasks, which use
vectors as input to machine learning models. We find that intrinsic evaluations
are highly sensitive to absolute position, while extrinsic tasks rely primarily
on local similarity. Our findings suggest that future embedding models and
post-processing techniques should focus primarily on similarity to nearby
points in vector space.Comment: Appearing in the Third Workshop on Evaluating Vector Space
Representations for NLP (RepEval 2019). 7 pages + reference
Variation and Instability in Dialect-Based Embedding Spaces
This paper measures variation in embedding spaces which have been trained on
different regional varieties of English while controlling for instability in
the embeddings. While previous work has shown that it is possible to
distinguish between similar varieties of a language, this paper experiments
with two follow-up questions: First, does the variety represented in the
training data systematically influence the resulting embedding space after
training? This paper shows that differences in embeddings across varieties are
significantly higher than baseline instability. Second, is such dialect-based
variation spread equally throughout the lexicon? This paper shows that specific
parts of the lexicon are particularly subject to variation. Taken together,
these experiments confirm that embedding spaces are significantly influenced by
the dialect represented in the training data. This finding implies that there
is semantic variation across dialects, in addition to previously-studied
lexical and syntactic variation
Understanding Word Embedding Stability Across Languages and Applications
Despite the recent popularity of word embedding methods, there is only a small body of work exploring the limitations of these representations. In this thesis, we consider several aspects of embedding spaces, including their stability. First, we propose a definition of stability, and show that common English word embeddings are surprisingly unstable. We explore how properties of data, words, and algorithms relate to instability. We extend this work to approximately 100 world languages, considering how linguistic typology relates to stability. Additionally, we consider contextualized output embedding spaces. Using paraphrases, we explore properties and assumptions of BERT, a popular embedding algorithm.
Second, we consider how stability and other word embedding properties affect tasks where embeddings are commonly used. We consider both word embeddings used as features in downstream applications and corpus-centered applications, where embeddings are used to study characteristics of language and individual writers. In addition to stability, we also consider other word embedding properties, specifically batching and curriculum learning, and how methodological choices made for these properties affect downstream tasks.
Finally, we consider how knowledge of stability affects how we use word embeddings. Throughout this thesis, we discuss strategies to mitigate instability and provide analyses highlighting the strengths and weaknesses of word embeddings in different scenarios and languages. We show areas where more work is needed to improve embeddings, and we show where embeddings are already a strong tool.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/162917/1/lburdick_1.pd
Density Matching for Bilingual Word Embedding
Recent approaches to cross-lingual word embedding have generally been based
on linear transformations between the sets of embedding vectors in the two
languages. In this paper, we propose an approach that instead expresses the two
monolingual embedding spaces as probability densities defined by a Gaussian
mixture model, and matches the two densities using a method called normalizing
flow. The method requires no explicit supervision, and can be learned with only
a seed dictionary of words that have identical strings. We argue that this
formulation has several intuitively attractive properties, particularly with
the respect to improving robustness and generalization to mappings between
difficult language pairs or word pairs. On a benchmark data set of bilingual
lexicon induction and cross-lingual word similarity, our approach can achieve
competitive or superior performance compared to state-of-the-art published
results, with particularly strong results being found on etymologically distant
and/or morphologically rich languages.Comment: Accepted by NAACL-HLT 201
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