4,785 research outputs found
Semantic Structure and Interpretability of Word Embeddings
Dense word embeddings, which encode semantic meanings of words to low
dimensional vector spaces have become very popular in natural language
processing (NLP) research due to their state-of-the-art performances in many
NLP tasks. Word embeddings are substantially successful in capturing semantic
relations among words, so a meaningful semantic structure must be present in
the respective vector spaces. However, in many cases, this semantic structure
is broadly and heterogeneously distributed across the embedding dimensions,
which makes interpretation a big challenge. In this study, we propose a
statistical method to uncover the latent semantic structure in the dense word
embeddings. To perform our analysis we introduce a new dataset (SEMCAT) that
contains more than 6500 words semantically grouped under 110 categories. We
further propose a method to quantify the interpretability of the word
embeddings; the proposed method is a practical alternative to the classical
word intrusion test that requires human intervention.Comment: 11 Pages, 8 Figures, accepted by IEEE/ACM Transactions on Audio,
Speech, and Language Processin
AspeRa: Aspect-based Rating Prediction Model
We propose a novel end-to-end Aspect-based Rating Prediction model (AspeRa)
that estimates user rating based on review texts for the items and at the same
time discovers coherent aspects of reviews that can be used to explain
predictions or profile users. The AspeRa model uses max-margin losses for joint
item and user embedding learning and a dual-headed architecture; it
significantly outperforms recently proposed state-of-the-art models such as
DeepCoNN, HFT, NARRE, and TransRev on two real world data sets of user reviews.
With qualitative examination of the aspects and quantitative evaluation of
rating prediction models based on these aspects, we show how aspect embeddings
can be used in a recommender system.Comment: accepted to ECIR 201
Imparting Interpretability to Word Embeddings while Preserving Semantic Structure
As an ubiquitous method in natural language processing, word embeddings are
extensively employed to map semantic properties of words into a dense vector
representation. They capture semantic and syntactic relations among words but
the vectors corresponding to the words are only meaningful relative to each
other. Neither the vector nor its dimensions have any absolute, interpretable
meaning. We introduce an additive modification to the objective function of the
embedding learning algorithm that encourages the embedding vectors of words
that are semantically related to a predefined concept to take larger values
along a specified dimension, while leaving the original semantic learning
mechanism mostly unaffected. In other words, we align words that are already
determined to be related, along predefined concepts. Therefore, we impart
interpretability to the word embedding by assigning meaning to its vector
dimensions. The predefined concepts are derived from an external lexical
resource, which in this paper is chosen as Roget's Thesaurus. We observe that
alignment along the chosen concepts is not limited to words in the Thesaurus
and extends to other related words as well. We quantify the extent of
interpretability and assignment of meaning from our experimental results.
Manual human evaluation results have also been presented to further verify that
the proposed method increases interpretability. We also demonstrate the
preservation of semantic coherence of the resulting vector space by using
word-analogy and word-similarity tests. These tests show that the
interpretability-imparted word embeddings that are obtained by the proposed
framework do not sacrifice performances in common benchmark tests.Comment: 14 pages, 5 figure
Analytical Methods for Interpretable Ultradense Word Embeddings
Word embeddings are useful for a wide variety of tasks, but they lack
interpretability. By rotating word spaces, interpretable dimensions can be
identified while preserving the information contained in the embeddings without
any loss. In this work, we investigate three methods for making word spaces
interpretable by rotation: Densifier (Rothe et al., 2016), linear SVMs and
DensRay, a new method we propose. In contrast to Densifier, DensRay can be
computed in closed form, is hyperparameter-free and thus more robust than
Densifier. We evaluate the three methods on lexicon induction and set-based
word analogy. In addition we provide qualitative insights as to how
interpretable word spaces can be used for removing gender bias from embeddings.Comment: EMNLP 201
SESA: Supervised Explicit Semantic Analysis
In recent years supervised representation learning has provided state of the
art or close to the state of the art results in semantic analysis tasks
including ranking and information retrieval. The core idea is to learn how to
embed items into a latent space such that they optimize a supervised objective
in that latent space. The dimensions of the latent space have no clear
semantics, and this reduces the interpretability of the system. For example, in
personalization models, it is hard to explain why a particular item is ranked
high for a given user profile. We propose a novel model of representation
learning called Supervised Explicit Semantic Analysis (SESA) that is trained in
a supervised fashion to embed items to a set of dimensions with explicit
semantics. The model learns to compare two objects by representing them in this
explicit space, where each dimension corresponds to a concept from a knowledge
base. This work extends Explicit Semantic Analysis (ESA) with a supervised
model for ranking problems. We apply this model to the task of Job-Profile
relevance in LinkedIn in which a set of skills defines our explicit dimensions
of the space. Every profile and job are encoded to this set of skills their
similarity is calculated in this space. We use RNNs to embed text input into
this space. In addition to interpretability, our model makes use of the
web-scale collaborative skills data that is provided by users for each LinkedIn
profile. Our model provides state of the art result while it remains
interpretable
Learning and Evaluating Sparse Interpretable Sentence Embeddings
Previous research on word embeddings has shown that sparse representations,
which can be either learned on top of existing dense embeddings or obtained
through model constraints during training time, have the benefit of increased
interpretability properties: to some degree, each dimension can be understood
by a human and associated with a recognizable feature in the data. In this
paper, we transfer this idea to sentence embeddings and explore several
approaches to obtain a sparse representation. We further introduce a novel,
quantitative and automated evaluation metric for sentence embedding
interpretability, based on topic coherence methods. We observe an increase in
interpretability compared to dense models, on a dataset of movie dialogs and on
the scene descriptions from the MS COCO dataset.Comment: Will be presented at the workshop "Analyzing and interpreting neural
networks for NLP", collocated with the EMNLP 2018 conference in Brussel
Identification, Interpretability, and Bayesian Word Embeddings
Social scientists have recently turned to analyzing text using tools from
natural language processing like word embeddings to measure concepts like
ideology, bias, and affinity. However, word embeddings are difficult to use in
the regression framework familiar to social scientists: embeddings are are
neither identified, nor directly interpretable. I offer two advances on
standard embedding models to remedy these problems. First, I develop Bayesian
Word Embeddings with Automatic Relevance Determination priors, relaxing the
assumption that all embedding dimensions have equal weight. Second, I apply
work identifying latent variable models to anchor the dimensions of the
resulting embeddings, identifying them, and making them interpretable and
usable in a regression. I then apply this model and anchoring approach to two
cases, the shift in internationalist rhetoric in the American presidents'
inaugural addresses, and the relationship between bellicosity in American
foreign policy decision-makers' deliberations. I find that inaugural addresses
became less internationalist after 1945, which goes against the conventional
wisdom, and that an increase in bellicosity is associated with an increase in
hostile actions by the United States, showing that elite deliberations are not
cheap talk, and helping confirm the validity of the model.Comment: Accepted to the Third Workshop on Natural Language Processing and
Computational Social Science at NAACL-HLT 201
Rotations and Interpretability of Word Embeddings: the Case of the Russian Language
Consider a continuous word embedding model. Usually, the cosines between word
vectors are used as a measure of similarity of words. These cosines do not
change under orthogonal transformations of the embedding space. We demonstrate
that, using some canonical orthogonal transformations from SVD, it is possible
both to increase the meaning of some components and to make the components more
stable under re-learning. We study the interpretability of components for
publicly available models for the Russian language (RusVectores, fastText,
RDT)
Improving Moderation of Online Discussions via Interpretable Neural Models
Growing amount of comments make online discussions difficult to moderate by
human moderators only. Antisocial behavior is a common occurrence that often
discourages other users from participating in discussion. We propose a neural
network based method that partially automates the moderation process. It
consists of two steps. First, we detect inappropriate comments for moderators
to see. Second, we highlight inappropriate parts within these comments to make
the moderation faster. We evaluated our method on data from a major Slovak news
discussion platform.Comment: ALW
CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions
Word embedding is a useful approach to capture co-occurrence structures in
large text corpora. However, in addition to the text data itself, we often have
additional covariates associated with individual corpus documents---e.g. the
demographic of the author, time and venue of publication---and we would like
the embedding to naturally capture this information. We propose CoVeR, a new
tensor decomposition model for vector embeddings with covariates. CoVeR jointly
learns a \emph{base} embedding for all the words as well as a weighted diagonal
matrix to model how each covariate affects the base embedding. To obtain author
or venue-specific embedding, for example, we can then simply multiply the base
embedding by the associated transformation matrix. The main advantages of our
approach are data efficiency and interpretability of the covariate
transformation. Our experiments demonstrate that our joint model learns
substantially better covariate-specific embeddings compared to the standard
approach of learning a separate embedding for each covariate using only the
relevant subset of data, as well as other related methods. Furthermore, CoVeR
encourages the embeddings to be "topic-aligned" in that the dimensions have
specific independent meanings. This allows our covariate-specific embeddings to
be compared by topic, enabling downstream differential analysis. We empirically
evaluate the benefits of our algorithm on datasets, and demonstrate how it can
be used to address many natural questions about covariate effects.
Accompanying code to this paper can be found at
http://github.com/kjtian/CoVeR.Comment: 12 pages. Appears in ICML 201
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