4,286 research outputs found
Learning Language Representations for Typology Prediction
One central mystery of neural NLP is what neural models "know" about their
subject matter. When a neural machine translation system learns to translate
from one language to another, does it learn the syntax or semantics of the
languages? Can this knowledge be extracted from the system to fill holes in
human scientific knowledge? Existing typological databases contain relatively
full feature specifications for only a few hundred languages. Exploiting the
existence of parallel texts in more than a thousand languages, we build a
massive many-to-one neural machine translation (NMT) system from 1017 languages
into English, and use this to predict information missing from typological
databases. Experiments show that the proposed method is able to infer not only
syntactic, but also phonological and phonetic inventory features, and improves
over a baseline that has access to information about the languages' geographic
and phylogenetic neighbors.Comment: EMNLP 201
Learn to synthesize and synthesize to learn
Attribute guided face image synthesis aims to manipulate attributes on a face
image. Most existing methods for image-to-image translation can either perform
a fixed translation between any two image domains using a single attribute or
require training data with the attributes of interest for each subject.
Therefore, these methods could only train one specific model for each pair of
image domains, which limits their ability in dealing with more than two
domains. Another disadvantage of these methods is that they often suffer from
the common problem of mode collapse that degrades the quality of the generated
images. To overcome these shortcomings, we propose attribute guided face image
generation method using a single model, which is capable to synthesize multiple
photo-realistic face images conditioned on the attributes of interest. In
addition, we adopt the proposed model to increase the realism of the simulated
face images while preserving the face characteristics. Compared to existing
models, synthetic face images generated by our method present a good
photorealistic quality on several face datasets. Finally, we demonstrate that
generated facial images can be used for synthetic data augmentation, and
improve the performance of the classifier used for facial expression
recognition.Comment: Accepted to Computer Vision and Image Understanding (CVIU
Recurrent Memory Networks for Language Modeling
Recurrent Neural Networks (RNN) have obtained excellent result in many
natural language processing (NLP) tasks. However, understanding and
interpreting the source of this success remains a challenge. In this paper, we
propose Recurrent Memory Network (RMN), a novel RNN architecture, that not only
amplifies the power of RNN but also facilitates our understanding of its
internal functioning and allows us to discover underlying patterns in data. We
demonstrate the power of RMN on language modeling and sentence completion
tasks. On language modeling, RMN outperforms Long Short-Term Memory (LSTM)
network on three large German, Italian, and English dataset. Additionally we
perform in-depth analysis of various linguistic dimensions that RMN captures.
On Sentence Completion Challenge, for which it is essential to capture sentence
coherence, our RMN obtains 69.2% accuracy, surpassing the previous
state-of-the-art by a large margin.Comment: 8 pages, 6 figures. Accepted at NAACL 201
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