769 research outputs found
Crosslingual Document Embedding as Reduced-Rank Ridge Regression
There has recently been much interest in extending vector-based word
representations to multiple languages, such that words can be compared across
languages. In this paper, we shift the focus from words to documents and
introduce a method for embedding documents written in any language into a
single, language-independent vector space. For training, our approach leverages
a multilingual corpus where the same concept is covered in multiple languages
(but not necessarily via exact translations), such as Wikipedia. Our method,
Cr5 (Crosslingual reduced-rank ridge regression), starts by training a
ridge-regression-based classifier that uses language-specific bag-of-word
features in order to predict the concept that a given document is about. We
show that, when constraining the learned weight matrix to be of low rank, it
can be factored to obtain the desired mappings from language-specific
bags-of-words to language-independent embeddings. As opposed to most prior
methods, which use pretrained monolingual word vectors, postprocess them to
make them crosslingual, and finally average word vectors to obtain document
vectors, Cr5 is trained end-to-end and is thus natively crosslingual as well as
document-level. Moreover, since our algorithm uses the singular value
decomposition as its core operation, it is highly scalable. Experiments show
that our method achieves state-of-the-art performance on a crosslingual
document retrieval task. Finally, although not trained for embedding sentences
and words, it also achieves competitive performance on crosslingual sentence
and word retrieval tasks.Comment: In The Twelfth ACM International Conference on Web Search and Data
Mining (WSDM '19
Rationalizing Text Matching: Learning Sparse Alignments via Optimal Transport
Selecting input features of top relevance has become a popular method for
building self-explaining models. In this work, we extend this selective
rationalization approach to text matching, where the goal is to jointly select
and align text pieces, such as tokens or sentences, as a justification for the
downstream prediction. Our approach employs optimal transport (OT) to find a
minimal cost alignment between the inputs. However, directly applying OT often
produces dense and therefore uninterpretable alignments. To overcome this
limitation, we introduce novel constrained variants of the OT problem that
result in highly sparse alignments with controllable sparsity. Our model is
end-to-end differentiable using the Sinkhorn algorithm for OT and can be
trained without any alignment annotations. We evaluate our model on the
StackExchange, MultiNews, e-SNLI, and MultiRC datasets. Our model achieves very
sparse rationale selections with high fidelity while preserving prediction
accuracy compared to strong attention baseline models.Comment: To appear at ACL 202
Improved Techniques for Adversarial Discriminative Domain Adaptation
Adversarial discriminative domain adaptation (ADDA) is an efficient framework
for unsupervised domain adaptation in image classification, where the source
and target domains are assumed to have the same classes, but no labels are
available for the target domain. We investigate whether we can improve
performance of ADDA with a new framework and new loss formulations. Following
the framework of semi-supervised GANs, we first extend the discriminator output
over the source classes, in order to model the joint distribution over domain
and task. We thus leverage on the distribution over the source encoder
posteriors (which is fixed during adversarial training) and propose maximum
mean discrepancy (MMD) and reconstruction-based loss functions for aligning the
target encoder distribution to the source domain. We compare and provide a
comprehensive analysis of how our framework and loss formulations extend over
simple multi-class extensions of ADDA and other discriminative variants of
semi-supervised GANs. In addition, we introduce various forms of regularization
for stabilizing training, including treating the discriminator as a denoising
autoencoder and regularizing the target encoder with source examples to reduce
overfitting under a contraction mapping (i.e., when the target per-class
distributions are contracting during alignment with the source). Finally, we
validate our framework on standard domain adaptation datasets, such as SVHN and
MNIST. We also examine how our framework benefits recognition problems based on
modalities that lack training data, by introducing and evaluating on a
neuromorphic vision sensing (NVS) sign language recognition dataset, where the
source and target domains constitute emulated and real neuromorphic spike
events respectively. Our results on all datasets show that our proposal
competes or outperforms the state-of-the-art in unsupervised domain adaptation.Comment: To appear in IEEE Transactions on Image Processin
Data Visualization, Dimensionality Reduction, and Data Alignment via Manifold Learning
The high dimensionality of modern data introduces significant challenges in descriptive and exploratory data analysis. These challenges gave rise to extensive work on dimensionality reduction and manifold learning aiming to provide low dimensional representations that preserve or uncover intrinsic patterns and structures in the data. In this thesis, we expand the current literature in manifold learning developing two methods called DIG (Dynamical Information Geometry) and GRAE (Geometry Regularized Autoencoders). DIG is a method capable of finding low-dimensional representations of high-frequency multivariate time series data, especially suited for visualization. GRAE is a general framework which splices the well-established machinery from kernel manifold learning methods to recover a sensitive geometry, alongside the parametric structure of autoencoders.
Manifold learning can also be useful to study data collected from different measurement instruments, conditions, or protocols of the same underlying system. In such cases the data is acquired in a multi-domain representation. The last two Chapters of this thesis are devoted to two new methods capable of aligning multi-domain data, leveraging their geometric structure alongside limited common information. First, we present DTA (Diffusion Transport Alignment), a semi-supervised manifold alignment method that exploits prior one-to-one correspondence knowledge between distinct data views and finds an aligned common representation. And finally, we introduce MALI (Manifold Alignment with Label Information). Here we drop the one-to-one prior correspondences assumption, since in many scenarios such information can not be provided, either due to the nature of the experimental design, or it becomes extremely costly. Instead, MALI only needs side-information in the form of discrete labels/classes present in both domains
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