31 research outputs found
Unsupervised patient representations from clinical notes with interpretable classification decisions
We have two main contributions in this work: 1. We explore the usage of a
stacked denoising autoencoder, and a paragraph vector model to learn
task-independent dense patient representations directly from clinical notes. We
evaluate these representations by using them as features in multiple supervised
setups, and compare their performance with those of sparse representations. 2.
To understand and interpret the representations, we explore the best encoded
features within the patient representations obtained from the autoencoder
model. Further, we calculate the significance of the input features of the
trained classifiers when we use these pretrained representations as input.Comment: Accepted poster at NIPS 2017 Workshop on Machine Learning for Health
(https://ml4health.github.io/2017/
Language classification from bilingual word embedding graphs
We study the role of the second language in bilingual word embeddings in
monolingual semantic evaluation tasks. We find strongly and weakly positive
correlations between down-stream task performance and second language
similarity to the target language. Additionally, we show how bilingual word
embeddings can be employed for the task of semantic language classification and
that joint semantic spaces vary in meaningful ways across second languages. Our
results support the hypothesis that semantic language similarity is influenced
by both structural similarity as well as geography/contact.Comment: To be published at Coling 201
Target-Side Context for Discriminative Models in Statistical Machine Translation
Discriminative translation models utilizing source context have been shown to
help statistical machine translation performance. We propose a novel extension
of this work using target context information. Surprisingly, we show that this
model can be efficiently integrated directly in the decoding process. Our
approach scales to large training data sizes and results in consistent
improvements in translation quality on four language pairs. We also provide an
analysis comparing the strengths of the baseline source-context model with our
extended source-context and target-context model and we show that our extension
allows us to better capture morphological coherence. Our work is freely
available as part of Moses.Comment: Accepted as a long paper for ACL 201
Bridge Correlational Neural Networks for Multilingual Multimodal Representation Learning
Recently there has been a lot of interest in learning common representations
for multiple views of data. Typically, such common representations are learned
using a parallel corpus between the two views (say, 1M images and their English
captions). In this work, we address a real-world scenario where no direct
parallel data is available between two views of interest (say, and )
but parallel data is available between each of these views and a pivot view
(). We propose a model for learning a common representation for ,
and using only the parallel data available between and
. The proposed model is generic and even works when there are views
of interest and only one pivot view which acts as a bridge between them. There
are two specific downstream applications that we focus on (i) transfer learning
between languages ,,..., using a pivot language and (ii)
cross modal access between images and a language using a pivot language
. Our model achieves state-of-the-art performance in multilingual document
classification on the publicly available multilingual TED corpus and promising
results in multilingual multimodal retrieval on a new dataset created and
released as a part of this work.Comment: Published at NAACL-HLT 201
An Analysis of Convolutional Neural Networks for Sentence Classification
Over the past few years, neural networks have reemerged as powerful machine-learning models, yielding state-ofthe- art results in fields such as image recognition and speech processing. More recently, neural network models started to be applied also to textual natural language signals, again with very promising results. This paper show a series of experiments with Convolutional Neural Networks for sentence-level classification tasks with different hyperparameter settings and how sensitive model performance is to changes in these configurations.Sociedad Argentina de Informática e Investigación Operativa (SADIO