pwc-archive/papers-with-abstracts huggingface dataset:
Creating New Language and Voice Components for the Updated MaryTTS Text-to-Speech Synthesis Platform
Authors: Ingmar Steiner, Sébastien Le Maguer
Date: 2017-12-13 00:00:00
Proceeding: LREC 2018 5
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

We present a new workflow to create components for the MaryTTS text-to-speech
synthesis platform, which is popular with researchers and developers, extending
it to support new languages and custom synthetic voices. This workflow replaces
the previous toolkit with an efficient, flexible process that leverages modern
build automation and cloud-hosted infrastructure. Moreover, it is compatible
with the updated MaryTTS architecture, enabling new features and
state-of-the-art paradigms such as synthesis based on deep neural networks
(DNNs). Like MaryTTS itself, the new tools are free, open source software
(FOSS), and promote the use of open data.
Multi-representation Ensembles and Delayed SGD Updates Improve Syntax-based NMT
Authors: Danielle Saunders, Felix Stahlberg, Adria de Gispert, Bill Byrne
Date: 2018-05-01 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, NMT, Sentence, Translation

We explore strategies for incorporating target syntax into Neural Machine
Translation. We specifically focus on syntax in ensembles containing multiple
sentence representations. We formulate beam search over such ensembles using
WFSTs, and describe a delayed SGD update training procedure that is especially
effective for long representations like linearized syntax. Our approach gives
state-of-the-art performance on a difficult Japanese-English task.
The risk of sub-optimal use of Open Source NLP Software: UKB is inadvertently state-of-the-art in knowledge-based WSD
Authors: Eneko Agirre, Oier López de Lacalle, Aitor Soroa
Date: 2018-05-11 00:00:00
Proceeding: WS 2018 7
Tasks: Word Sense Disambiguation

UKB is an open source collection of programs for performing, among other
tasks, knowledge-based Word Sense Disambiguation (WSD). Since it was released
in 2009 it has been often used out-of-the-box in sub-optimal settings. We show
that nine years later it is the state-of-the-art on knowledge-based WSD. This
case shows the pitfalls of releasing open source NLP software without optimal
default settings and precise instructions for reproducibility.
Clinical evaluation of semi-automatic opensource algorithmic software segmentation of the mandibular bone: Practical feasibility and assessment of a new course of action
Authors: Jürgen Wallner, Kerstin Hochegger, Xiaojun Chen, Irene Mischak, Knut Reinbacher, Mauro Pau, Tomislav Zrnc, Katja Schwenzer-Zimmerer, Wolfgang Zemann, Dieter Schmalstieg, Jan Egger
Date: 2018-05-11 00:00:00
Tasks: Anatomy, Segmentation

Computer assisted technologies based on algorithmic software segmentation are
an increasing topic of interest in complex surgical cases. However - due to
functional instability, time consuming software processes, personnel resources
or licensed-based financial costs many segmentation processes are often
outsourced from clinical centers to third parties and the industry. Therefore,
the aim of this trial was to assess the practical feasibility of an easy
available, functional stable and licensed-free segmentation approach to be used
in the clinical practice. In this retrospective, randomized, controlled trail
the accuracy and accordance of the open-source based segmentation algorithm
GrowCut (GC) was assessed through the comparison to the manually generated
ground truth of the same anatomy using 10 CT lower jaw data-sets from the
clinical routine. Assessment parameters were the segmentation time, the volume,
the voxel number, the Dice Score (DSC) and the Hausdorff distance (HD). Overall
segmentation times were about one minute. Mean DSC values of over 85% and HD
below 33.5 voxel could be achieved. Statistical differences between the
assessment parameters were not significant (p<0.05) and correlation
coefficients were close to the value one (r > 0.94). Complete functional stable
and time saving segmentations with high accuracy and high positive correlation
could be performed by the presented interactive open-source based approach. In
the cranio-maxillofacial complex the used method could represent an algorithmic
alternative for image-based segmentation in the clinical practice for e.g.
surgical treatment planning or visualization of postoperative results and
offers several advantages. Systematic comparisons to other segmentation
approaches or with a greater data amount are areas of future works.
An $O(N)$ Sorting Algorithm: Machine Learning Sort
Authors: Hanqing Zhao, Yuehan Luo
Date: 2018-05-11 00:00:00
Tasks: BIG-bench Machine Learning, GPU

We propose an $O(N\cdot M)$ sorting algorithm by Machine Learning method,
which shows a huge potential sorting big data. This sorting algorithm can be
applied to parallel sorting and is suitable for GPU or TPU acceleration.
Furthermore, we discuss the application of this algorithm to sparse hash table.
Neural Open Information Extraction
Authors: Lei Cui, Furu Wei, Ming Zhou
Date: 2018-05-11 00:00:00
Proceeding: ACL 2018 7
Tasks: Computational Efficiency, Decoder, Open Information Extraction

Conventional Open Information Extraction (Open IE) systems are usually built
on hand-crafted patterns from other NLP tools such as syntactic parsing, yet
they face problems of error propagation. In this paper, we propose a neural
Open IE approach with an encoder-decoder framework. Distinct from existing
methods, the neural Open IE approach learns highly confident arguments and
relation tuples bootstrapped from a state-of-the-art Open IE system. An
empirical study on a large benchmark dataset shows that the neural Open IE
system significantly outperforms several baselines, while maintaining
comparable computational efficiency.
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition
Authors: Jinmian Ye, Linnan Wang, Guangxi Li, Di Chen, Shandian Zhe, Xinqi Chu, Zenglin Xu
Date: 2017-12-14 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Recognition, Action Recognition In Videos, Image Captioning, Image Generation, Temporal Action Localization, Tensor Decomposition

Recurrent Neural Networks (RNNs) are powerful sequence modeling tools.
However, when dealing with high dimensional inputs, the training of RNNs
becomes computational expensive due to the large number of model parameters.
This hinders RNNs from solving many important computer vision tasks, such as
Action Recognition in Videos and Image Captioning. To overcome this problem, we
propose a compact and flexible structure, namely Block-Term tensor
decomposition, which greatly reduces the parameters of RNNs and improves their
training efficiency. Compared with alternative low-rank approximations, such as
tensor-train RNN (TT-RNN), our method, Block-Term RNN (BT-RNN), is not only
more concise (when using the same rank), but also able to attain a better
approximation to the original RNNs with much fewer parameters. On three
challenging tasks, including Action Recognition in Videos, Image Captioning and
Image Generation, BT-RNN outperforms TT-RNN and the standard RNN in terms of
both prediction accuracy and convergence rate. Specifically, BT-LSTM utilizes
17,388 times fewer parameters than the standard LSTM to achieve an accuracy
improvement over 15.6\% in the Action Recognition task on the UCF11 dataset.
PALM: An Incremental Construction of Hyperplanes for Data Stream Regression
Authors: Md Meftahul Ferdaus, Mahardhika Pratama, Sreenatha G. Anavatti, Matthew A. Garratt
Date: 2018-05-11 00:00:00
Tasks: Autonomous Vehicles, Clustering, regression

Data stream has been the underlying challenge in the age of big data because
it calls for real-time data processing with the absence of a retraining process
and/or an iterative learning approach. In realm of fuzzy system community, data
stream is handled by algorithmic development of self-adaptive neurofuzzy
systems (SANFS) characterized by the single-pass learning mode and the open
structure property which enables effective handling of fast and rapidly
changing natures of data streams. The underlying bottleneck of SANFSs lies in
its design principle which involves a high number of free parameters (rule
premise and rule consequent) to be adapted in the training process. This figure
can even double in the case of type-2 fuzzy system. In this work, a novel
SANFS, namely parsimonious learning machine (PALM), is proposed. PALM features
utilization of a new type of fuzzy rule based on the concept of hyperplane
clustering which significantly reduces the number of network parameters because
it has no rule premise parameters. PALM is proposed in both type-1 and type-2
fuzzy systems where all of which characterize a fully dynamic rule-based
system. That is, it is capable of automatically generating, merging and tuning
the hyperplane-based fuzzy rule in the single pass manner. Moreover, an
extension of PALM, namely recurrent PALM (rPALM), is proposed and adopts the
concept of teacher-forcing mechanism in the deep learning literature. The
efficacy of PALM has been evaluated through numerical study with six real-world
and synthetic data streams from public database and our own real-world project
of autonomous vehicles. The proposed model showcases significant improvements
in terms of computational complexity and number of required parameters against
several renowned SANFSs, while attaining comparable and often better predictive
accuracy.
A Multimodal Corpus of Expert Gaze and Behavior during Phonetic Segmentation Tasks
Authors: Arif Khan, Ingmar Steiner, Yusuke Sugano, Andreas Bulling, Ross Macdonald
Date: 2017-12-13 00:00:00
Proceeding: LREC 2018 5
Tasks: Segmentation

Phonetic segmentation is the process of splitting speech into distinct
phonetic units. Human experts routinely perform this task manually by analyzing
auditory and visual cues using analysis software, which is an extremely
time-consuming process. Methods exist for automatic segmentation, but these are
not always accurate enough. In order to improve automatic segmentation, we need
to model it as close to the manual segmentation as possible. This corpus is an
effort to capture the human segmentation behavior by recording experts
performing a segmentation task. We believe that this data will enable us to
highlight the important aspects of manual segmentation, which can be used in
automatic segmentation to improve its accuracy.
Entropic selection of concepts unveils hidden topics in documents corpora
Authors: Andrea Martini, Alessio Cardillo, Paolo De Los Rios
Date: 2017-05-18 00:00:00

The organization and evolution of science has recently become itself an
object of scientific quantitative investigation, thanks to the wealth of
information that can be extracted from scientific documents, such as citations
between papers and co-authorship between researchers. However, only few studies
have focused on the concepts that characterize full documents and that can be
extracted and analyzed, revealing the deeper organization of scientific
knowledge. Unfortunately, several concepts can be so common across documents
that they hinder the emergence of the underlying topical structure of the
document corpus, because they give rise to a large amount of spurious and
trivial relations among documents. To identify and remove common concepts, we
introduce a method to gauge their relevance according to an objective
information-theoretic measure related to the statistics of their occurrence
across the document corpus. After progressively removing concepts that,
according to this metric, can be considered as generic, we find that the topic
organization displays a correspondingly more refined structure.
Argument Harvesting Using Chatbots
Authors: Lisa A. Chalaguine, Anthony Hunter, Henry W. W. Potts, Fiona L. Hamilton
Date: 2018-05-11 00:00:00
Tasks: Argument Mining, Chatbot

Much research in computational argumentation assumes that arguments and
counterarguments can be obtained in some way. Yet, to improve and apply models
of argument, we need methods for acquiring them. Current approaches include
argument mining from text, hand coding of arguments by researchers, or
generating arguments from knowledge bases. In this paper, we propose a new
approach, which we call argument harvesting, that uses a chatbot to enter into
a dialogue with a participant to get arguments and counterarguments from him or
her. Because it is automated, the chatbot can be used repeatedly in many
dialogues, and thereby it can generate a large corpus. We describe the
architecture of the chatbot, provide methods for managing a corpus of arguments
and counterarguments, and an evaluation of our approach in a case study
concerning attitudes of women to participation in sport.
Adaptive Selection of Deep Learning Models on Embedded Systems
Authors: Ben Taylor, Vicent Sanz Marco, Willy Wolff, Yehia Elkhatib, Zheng Wang
Date: 2018-05-11 00:00:00
Tasks: Deep Learning, image-classification, Image Classification

The recent ground-breaking advances in deep learning networks ( DNNs ) make
them attractive for embedded systems. However, it can take a long time for DNNs
to make an inference on resource-limited embedded devices. Offloading the
computation into the cloud is often infeasible due to privacy concerns, high
latency, or the lack of connectivity. As such, there is a critical need to find
a way to effectively execute the DNN models locally on the devices. This paper
presents an adaptive scheme to determine which DNN model to use for a given
input, by considering the desired accuracy and inference time. Our approach
employs machine learning to develop a predictive model to quickly select a
pre-trained DNN to use for a given input and the optimization constraint. We
achieve this by first training off-line a predictive model, and then use the
learnt model to select a DNN model to use for new, unseen inputs. We apply our
approach to the image classification task and evaluate it on a Jetson TX2
embedded deep learning platform using the ImageNet ILSVRC 2012 validation
dataset. We consider a range of influential DNN models. Experimental results
show that our approach achieves a 7.52% improvement in inference accuracy, and
a 1.8x reduction in inference time over the most-capable single DNN model.
Deep 2.5D Vehicle Classification with Sparse SfM Depth Prior for Automated Toll Systems
Authors: Georg Waltner, Michael Maurer, Thomas Holzmann, Patrick Ruprecht, Michael Opitz, Horst Possegger, Friedrich Fraundorfer, Horst Bischof
Date: 2018-05-09 00:00:00
Tasks: 3D Object Classification, Classification, General Classification, Object

Automated toll systems rely on proper classification of the passing vehicles.
This is especially difficult when the images used for classification only cover
parts of the vehicle. To obtain information about the whole vehicle. we
reconstruct the vehicle as 3D object and exploit this additional information
within a Convolutional Neural Network (CNN). However, when using deep networks
for 3D object classification, large amounts of dense 3D models are required for
good accuracy, which are often neither available nor feasible to process due to
memory requirements. Therefore, in our method we reproject the 3D object onto
the image plane using the reconstructed points, lines or both. We utilize this
sparse depth prior within an auxiliary network branch that acts as a
regularizer during training. We show that this auxiliary regularizer helps to
improve accuracy compared to 2D classification on a real-world dataset.
Furthermore due to the design of the network, at test time only the 2D camera
images are required for classification which enables the usage in portable
computer vision systems.
Reciprocal Attention Fusion for Visual Question Answering
Authors: Moshiur R. Farazi, Salman H. Khan
Date: 2018-05-11 00:00:00
Tasks: Object, Question Answering, Tensor Decomposition, Visual Question Answering, Visual Question Answering (VQA)

Existing attention mechanisms either attend to local image grid or object
level features for Visual Question Answering (VQA). Motivated by the
observation that questions can relate to both object instances and their parts,
we propose a novel attention mechanism that jointly considers reciprocal
relationships between the two levels of visual details. The bottom-up attention
thus generated is further coalesced with the top-down information to only focus
on the scene elements that are most relevant to a given question. Our design
hierarchically fuses multi-modal information i.e., language, object- and
gird-level features, through an efficient tensor decomposition scheme. The
proposed model improves the state-of-the-art single model performances from
67.9% to 68.2% on VQAv1 and from 65.7% to 67.4% on VQAv2, demonstrating a
significant boost.
Convex Programming Based Spectral Clustering
Authors: Tomohiko Mizutani
Date: 2018-05-11 00:00:00
Tasks: Clustering

Clustering is a fundamental task in data analysis, and spectral clustering has been recognized as a promising approach to it. Given a graph describing the relationship between data, spectral clustering explores the underlying cluster structure in two stages. The first stage embeds the nodes of the graph in real space, and the second stage groups the embedded nodes into several clusters. The use of the $k$-means method in the grouping stage is currently standard practice. We present a spectral clustering algorithm that uses convex programming in the grouping stage and study how well it works. This algorithm is designed based on the following observation. If a graph is well-clustered, then the nodes with the largest degree in each cluster can be found by computing an enclosing ellipsoid of the nodes embedded in real space, and the clusters can be identified by using those nodes. We show that, for well-clustered graphs, the algorithm can find clusters of nodes with minimal conductance. We also give an experimental assessment of the algorithm's performance.
Diffusion Based Network Embedding
Authors: Yong Shi, Minglong Lei, Peng Zhang, Lingfeng Niu
Date: 2018-05-09 00:00:00
Tasks: Network Embedding, Node Classification

In network embedding, random walks play a fundamental role in preserving
network structures. However, random walk based embedding methods have two
limitations. First, random walk methods are fragile when the sampling frequency
or the number of node sequences changes. Second, in disequilibrium networks
such as highly biases networks, random walk methods often perform poorly due to
the lack of global network information. In order to solve the limitations, we
propose in this paper a network diffusion based embedding method. To solve the
first limitation, our method employs a diffusion driven process to capture both
depth information and breadth information. The time dimension is also attached
to node sequences that can strengthen information preserving. To solve the
second limitation, our method uses the network inference technique based on
cascades to capture the global network information. To verify the performance,
we conduct experiments on node classification tasks using the learned
representations. Results show that compared with random walk based methods,
diffusion based models are more robust when samplings under each node is rare.
We also conduct experiments on a highly imbalanced network. Results shows that
the proposed model are more robust under the biased network structure.
KSR: A Semantic Representation of Knowledge Graph within a Novel Unsupervised Paradigm
Authors: Han Xiao, Minlie Huang, Xiaoyan Zhu
Date: 2016-08-27 00:00:00
Tasks: Entity Retrieval, Graph Embedding, Knowledge Graph Embedding, Question Answering, Retrieval

Knowledge representation is a long-history topic in AI, which is very important. A variety of models have been proposed for knowledge graph embedding, which projects symbolic entities and relations into continuous vector space. However, most related methods merely focus on the data-fitting of knowledge graph, and ignore the interpretable semantic expression. Thus, traditional embedding methods are not friendly for applications that require semantic analysis, such as question answering and entity retrieval. To this end, this paper proposes a semantic representation method for knowledge graph \textbf{(KSR)}, which imposes a two-level hierarchical generative process that globally extracts many aspects and then locally assigns a specific category in each aspect for every triple. Since both aspects and categories are semantics-relevant, the collection of categories in each aspect is treated as the semantic representation of this triple. Extensive experiments show that our model outperforms other state-of-the-art baselines substantially.
Just-in-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors
Authors: Chamara Saroj Weerasekera, Thanuja Dharmasiri, Ravi Garg, Tom Drummond, Ian Reid
Date: 2018-05-11 00:00:00
Tasks: Depth Estimation, Depth Prediction

We present ``just-in-time reconstruction" as real-time image-guided
inpainting of a map with arbitrary scale and sparsity to generate a fully dense
depth map for the image. In particular, our goal is to inpaint a sparse map ---
obtained from either a monocular visual SLAM system or a sparse sensor ---
using a single-view depth prediction network as a virtual depth sensor. We
adopt a fairly standard approach to data fusion, to produce a fused depth map
by performing inference over a novel fully-connected Conditional Random Field
(CRF) which is parameterized by the input depth maps and their pixel-wise
confidence weights. Crucially, we obtain the confidence weights that
parameterize the CRF model in a data-dependent manner via Convolutional Neural
Networks (CNNs) which are trained to model the conditional depth error
distributions given each source of input depth map and the associated RGB
image. Our CRF model penalises absolute depth error in its nodes and pairwise
scale-invariant depth error in its edges, and the confidence-based fusion
minimizes the impact of outlier input depth values on the fused result. We
demonstrate the flexibility of our method by real-time inpainting of ORB-SLAM,
Kinect, and LIDAR depth maps acquired both indoors and outdoors at arbitrary
scale and varied amount of irregular sparsity.
Randomized Smoothing SVRG for Large-scale Nonsmooth Convex Optimization
Authors: Wenjie Huang
Date: 2018-05-11 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we consider the problem of minimizing the average of a large
number of nonsmooth and convex functions. Such problems often arise in typical
machine learning problems as empirical risk minimization, but are
computationally very challenging. We develop and analyze a new algorithm that
achieves robust linear convergence rate, and both its time complexity and
gradient complexity are superior than state-of-art nonsmooth algorithms and
subgradient-based schemes. Besides, our algorithm works without any extra error
bound conditions on the objective function as well as the common
strongly-convex condition. We show that our algorithm has wide applications in
optimization and machine learning problems, and demonstrate experimentally that
it performs well on a large-scale ranking problem.