5,363 research outputs found
A Transition-Based Directed Acyclic Graph Parser for UCCA
We present the first parser for UCCA, a cross-linguistically applicable
framework for semantic representation, which builds on extensive typological
work and supports rapid annotation. UCCA poses a challenge for existing parsing
techniques, as it exhibits reentrancy (resulting in DAG structures),
discontinuous structures and non-terminal nodes corresponding to complex
semantic units. To our knowledge, the conjunction of these formal properties is
not supported by any existing parser. Our transition-based parser, which uses a
novel transition set and features based on bidirectional LSTMs, has value not
just for UCCA parsing: its ability to handle more general graph structures can
inform the development of parsers for other semantic DAG structures, and in
languages that frequently use discontinuous structures.Comment: 16 pages; Accepted as long paper at ACL201
Effective Approaches to Attention-based Neural Machine Translation
An attentional mechanism has lately been used to improve neural machine
translation (NMT) by selectively focusing on parts of the source sentence
during translation. However, there has been little work exploring useful
architectures for attention-based NMT. This paper examines two simple and
effective classes of attentional mechanism: a global approach which always
attends to all source words and a local one that only looks at a subset of
source words at a time. We demonstrate the effectiveness of both approaches
over the WMT translation tasks between English and German in both directions.
With local attention, we achieve a significant gain of 5.0 BLEU points over
non-attentional systems which already incorporate known techniques such as
dropout. Our ensemble model using different attention architectures has
established a new state-of-the-art result in the WMT'15 English to German
translation task with 25.9 BLEU points, an improvement of 1.0 BLEU points over
the existing best system backed by NMT and an n-gram reranker.Comment: 11 pages, 7 figures, EMNLP 2015 camera-ready version, more training
detail
Fast Landmark Localization with 3D Component Reconstruction and CNN for Cross-Pose Recognition
Two approaches are proposed for cross-pose face recognition, one is based on
the 3D reconstruction of facial components and the other is based on the deep
Convolutional Neural Network (CNN). Unlike most 3D approaches that consider
holistic faces, the proposed approach considers 3D facial components. It
segments a 2D gallery face into components, reconstructs the 3D surface for
each component, and recognizes a probe face by component features. The
segmentation is based on the landmarks located by a hierarchical algorithm that
combines the Faster R-CNN for face detection and the Reduced Tree Structured
Model for landmark localization. The core part of the CNN-based approach is a
revised VGG network. We study the performances with different settings on the
training set, including the synthesized data from 3D reconstruction, the
real-life data from an in-the-wild database, and both types of data combined.
We investigate the performances of the network when it is employed as a
classifier or designed as a feature extractor. The two recognition approaches
and the fast landmark localization are evaluated in extensive experiments, and
compared to stateof-the-art methods to demonstrate their efficacy.Comment: 14 pages, 12 figures, 4 table
Peak Alignment of Gas Chromatography-Mass Spectrometry Data with Deep Learning
We present ChromAlignNet, a deep learning model for alignment of peaks in Gas
Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's
retention time (RT) may not stay fixed across multiple chromatograms. To use
GC-MS data for biomarker discovery requires alignment of identical analyte's RT
from different samples. Current methods of alignment are all based on a set of
formal, mathematical rules. We present a solution to GC-MS alignment using deep
learning neural networks, which are more adept at complex, fuzzy data sets. We
tested our model on several GC-MS data sets of various complexities and
analysed the alignment results quantitatively. We show the model has very good
performance (AUC for simple data sets and AUC for very
complex data sets). Further, our model easily outperforms existing algorithms
on complex data sets. Compared with existing methods, ChromAlignNet is very
easy to use as it requires no user input of reference chromatograms and
parameters. This method can easily be adapted to other similar data such as
those from liquid chromatography. The source code is written in Python and
available online
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