pwc-archive/papers-with-abstracts huggingface dataset:
Spoken Language Understanding on the Edge
Authors: Alaa Saade, Alice Coucke, Alexandre Caulier, Joseph Dureau, Adrien Ball, Théodore Bluche, David Leroy, Clément Doumouro, Thibault Gisselbrecht, Francesco Caltagirone, Thibaut Lavril, Maël Primet
Date: 2018-10-30 00:00:00
Tasks: Spoken Language Understanding

We consider the problem of performing Spoken Language Understanding (SLU) on small devices typical of IoT applications. Our contributions are twofold. First, we outline the design of an embedded, private-by-design SLU system and show that it has performance on par with cloud-based commercial solutions. Second, we release the datasets used in our experiments in the interest of reproducibility and in the hope that they can prove useful to the SLU community.
Feature Trajectory Dynamic Time Warping for Clustering of Speech Segments
Authors: Lerato Lerato, Thomas Niesler
Date: 2018-10-30 00:00:00
Tasks: Clustering, Dynamic Time Warping

Dynamic time warping (DTW) can be used to compute the similarity between two
sequences of generally differing length. We propose a modification to DTW that
performs individual and independent pairwise alignment of feature trajectories.
The modified technique, termed feature trajectory dynamic time warping (FTDTW),
is applied as a similarity measure in the agglomerative hierarchical clustering
of speech segments. Experiments using MFCC and PLP parametrisations extracted
from TIMIT and from the Spoken Arabic Digit Dataset (SADD) show consistent and
statistically significant improvements in the quality of the resulting clusters
in terms of F-measure and normalised mutual information (NMI).
On the Effectiveness of Interval Bound Propagation for Training Verifiably Robust Models
Authors: Sven Gowal, Krishnamurthy Dvijotham, Robert Stanforth, Rudy Bunel, Chongli Qin, Jonathan Uesato, Relja Arandjelovic, Timothy Mann, Pushmeet Kohli
Date: 2018-10-30 00:00:00

Recent work has shown that it is possible to train deep neural networks that are provably robust to norm-bounded adversarial perturbations. Most of these methods are based on minimizing an upper bound on the worst-case loss over all possible adversarial perturbations. While these techniques show promise, they often result in difficult optimization procedures that remain hard to scale to larger networks. Through a comprehensive analysis, we show how a simple bounding technique, interval bound propagation (IBP), can be exploited to train large provably robust neural networks that beat the state-of-the-art in verified accuracy. While the upper bound computed by IBP can be quite weak for general networks, we demonstrate that an appropriate loss and clever hyper-parameter schedule allow the network to adapt such that the IBP bound is tight. This results in a fast and stable learning algorithm that outperforms more sophisticated methods and achieves state-of-the-art results on MNIST, CIFAR-10 and SVHN. It also allows us to train the largest model to be verified beyond vacuous bounds on a downscaled version of ImageNet.
Unsupervised Neural Machine Translation Initialized by Unsupervised Statistical Machine Translation
Authors: Benjamin Marie, Atsushi Fujita
Date: 2018-10-30 00:00:00
Tasks: Machine Translation, NMT, Translation, Unsupervised Machine Translation

Recent work achieved remarkable results in training neural machine
translation (NMT) systems in a fully unsupervised way, with new and dedicated
architectures that rely on monolingual corpora only. In this work, we propose
to define unsupervised NMT (UNMT) as NMT trained with the supervision of
synthetic bilingual data. Our approach straightforwardly enables the use of
state-of-the-art architectures proposed for supervised NMT by replacing
human-made bilingual data with synthetic bilingual data for training. We
propose to initialize the training of UNMT with synthetic bilingual data
generated by unsupervised statistical machine translation (USMT). The UNMT
system is then incrementally improved using back-translation. Our preliminary
experiments show that our approach achieves a new state-of-the-art for
unsupervised machine translation on the WMT16 German--English news translation
task, for both translation directions.
Compositional Attention Networks for Interpretability in Natural Language Question Answering
Authors: Muru Selvakumar, Suriyadeepan Ramamoorthy, Vaidheeswaran Archana, Malaikannan Sankarasubbu
Date: 2018-10-30 00:00:00
Tasks: Logical Reasoning, Question Answering, Visual Question Answering, Visual Question Answering (VQA)

MAC Net is a compositional attention network designed for Visual Question
Answering. We propose a modified MAC net architecture for Natural Language
Question Answering. Question Answering typically requires Language
Understanding and multi-step Reasoning. MAC net's unique architecture - the
separation between memory and control, facilitates data-driven iterative
reasoning. This makes it an ideal candidate for solving tasks that involve
logical reasoning. Our experiments with 20 bAbI tasks demonstrate the value of
MAC net as a data-efficient and interpretable architecture for Natural Language
Question Answering. The transparent nature of MAC net provides a highly
granular view of the reasoning steps taken by the network in answering a query.
Research Issues in Mining User Behavioral Rules for Context-Aware Intelligent Mobile Applications
Authors: Iqbal H. Sarker
Date: 2018-10-30 00:00:00
Tasks: Management

Context-awareness in smart mobile applications is a growing area of study,
because of it's intelligence in the applications. In order to build
context-aware intelligent applications, mining contextual behavioral rules of
individual smartphone users utilizing their phone log data is the key. However,
to mine these rules, a number of issues, such as the quality of smartphone
data, understanding the relevancy of contexts, discretization of continuous
contextual data, discovery of useful behavioral rules of individuals and their
ordering, knowledge-based interactive post-mining for semantic understanding,
and dynamic updating and management of rules according to their present
behavior, are investigated. In this paper, we briefly discuss these issues and
their potential solution directions for mining individuals' behavioral rules,
for the purpose of building various context-aware intelligent mobile
applications. We also summarize a number of real-life rule-based applications
that intelligently assist individual smartphone users according to their
behavioral rules in their daily activities.
Role of Class-specific Features in Various Classification Frameworks for Human Epithelial (HEp-2) Cell Images
Authors: Vibha Gupta, Arnav Bhavsar
Date: 2018-10-30 00:00:00
Tasks: General Classification

The antinuclear antibody detection with human epithelial cells is a popular
approach for autoimmune diseases diagnosis. The manual evaluation demands time,
effort and capital, and automation in screening can greatly aid the physicians
in these respects. In this work, we employ simple, efficient and visually more
interpretable, class-specific features which defined based on the visual
characteristics of each class. We believe that defining features with a good
visual interpretation, is indeed important in a scenario, where such an
approach is used in an interactive CAD system for pathologists. Considering
that problem consists of few classes, and our rather simplistic feature
definitions, frameworks can be structured as hierarchies of various binary
classifiers. These variants include frameworks which are earlier explored and
some which are not explored for this task. We perform various experiments which
include traditional texture features and demonstrate the effectiveness of
class-specific features in various frameworks. We make insightful comparisons
between different types of classification frameworks given their silent aspects
and pros and cons over each other. We also demonstrate an experiment with only
intermediates samples for testing. The proposed work yields encouraging results
with respect to the state-of-the-art and highlights the role of class-specific
features in different classification frameworks.
Evaluating Text GANs as Language Models
Authors: Guy Tevet, Gavriel Habib, Vered Shwartz, Jonathan Berant
Date: 2018-10-30 00:00:00
Proceeding: NAACL 2019 6
Tasks: Text Generation

Generative Adversarial Networks (GANs) are a promising approach for text
generation that, unlike traditional language models (LM), does not suffer from
the problem of ``exposure bias''. However, A major hurdle for understanding the
potential of GANs for text generation is the lack of a clear evaluation metric.
In this work, we propose to approximate the distribution of text generated by a
GAN, which permits evaluating them with traditional probability-based LM
metrics. We apply our approximation procedure on several GAN-based models and
show that they currently perform substantially worse than state-of-the-art LMs.
Our evaluation procedure promotes better understanding of the relation between
GANs and LMs, and can accelerate progress in GAN-based text generation.
Pseudo-Bayesian Learning with Kernel Fourier Transform as Prior
Authors: Gaël Letarte, Emilie Morvant, Pascal Germain
Date: 2018-10-30 00:00:00
Tasks: Generalization Bounds

We revisit Rahimi and Recht (2007)'s kernel random Fourier features (RFF)
method through the lens of the PAC-Bayesian theory. While the primary goal of
RFF is to approximate a kernel, we look at the Fourier transform as a prior
distribution over trigonometric hypotheses. It naturally suggests learning a
posterior on these hypotheses. We derive generalization bounds that are
optimized by learning a pseudo-posterior obtained from a closed-form
expression. Based on this study, we consider two learning strategies: The first
one finds a compact landmarks-based representation of the data where each
landmark is given by a distribution-tailored similarity measure, while the
second one provides a PAC-Bayesian justification to the kernel alignment method
of Sinha and Duchi (2016).
Hybrid Knowledge Routed Modules for Large-scale Object Detection
Authors: Chenhan Jiang, Hang Xu, Xiangdan Liang, Liang Lin
Date: 2018-10-30 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Object, object-detection, Object Detection

The dominant object detection approaches treat the recognition of each region
separately and overlook crucial semantic correlations between objects in one
scene. This paradigm leads to substantial performance drop when facing heavy
long-tail problems, where very few samples are available for rare classes and
plenty of confusing categories exists. We exploit diverse human commonsense
knowledge for reasoning over large-scale object categories and reaching
semantic coherency within one image. Particularly, we present Hybrid Knowledge
Routed Modules (HKRM) that incorporates the reasoning routed by two kinds of
knowledge forms: an explicit knowledge module for structured constraints that
are summarized with linguistic knowledge (e.g. shared attributes,
relationships) about concepts; and an implicit knowledge module that depicts
some implicit constraints (e.g. common spatial layouts). By functioning over a
region-to-region graph, both modules can be individualized and adapted to
coordinate with visual patterns in each image, guided by specific knowledge
forms. HKRM are light-weight, general-purpose and extensible by easily
incorporating multiple knowledge to endow any detection networks the ability of
global semantic reasoning. Experiments on large-scale object detection
benchmarks show HKRM obtains around 34.5% improvement on VisualGenome (1000
categories) and 30.4% on ADE in terms of mAP. Codes and trained model can be
found in https://github.com/chanyn/HKRM.
Sparse Gaussian Process Audio Source Separation Using Spectrum Priors in the Time-Domain
Authors: Pablo A. Alvarado, Mauricio A. Álvarez, Dan Stowell
Date: 2018-10-30 00:00:00
Tasks: Audio Source Separation

Gaussian process (GP) audio source separation is a time-domain approach that
circumvents the inherent phase approximation issue of spectrogram based
methods. Furthermore, through its kernel, GPs elegantly incorporate prior
knowledge about the sources into the separation model. Despite these compelling
advantages, the computational complexity of GP inference scales cubically with
the number of audio samples. As a result, source separation GP models have been
restricted to the analysis of short audio frames. We introduce an efficient
application of GPs to time-domain audio source separation, without compromising
performance. For this purpose, we used GP regression, together with spectral
mixture kernels, and variational sparse GPs. We compared our method with
LD-PSDTF (positive semi-definite tensor factorization), KL-NMF
(Kullback-Leibler non-negative matrix factorization), and IS-NMF (Itakura-Saito
NMF). Results show that the proposed method outperforms these techniques.
Cascaded CNN-resBiLSTM-CTC: An End-to-End Acoustic Model For Speech Recognition
Authors: Xinpei Zhou, Jiwei Li, Xi Zhou
Date: 2018-10-29 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Deep Learning, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Automatic speech recognition (ASR) tasks are resolved by end-to-end deep
learning models, which benefits us by less preparation of raw data, and easier
transformation between languages. We propose a novel end-to-end deep learning
model architecture namely cascaded CNN-resBiLSTM-CTC. In the proposed model, we
add residual blocks in BiLSTM layers to extract sophisticated phoneme and
semantic information together, and apply cascaded structure to pay more
attention mining information of hard negative samples. By applying both simple
Fast Fourier Transform (FFT) technique and n-gram language model (LM) rescoring
method, we manage to achieve word error rate (WER) of 3.41% on LibriSpeech test
clean corpora. Furthermore, we propose a new batch-varied method to speed up
the training process in length-varied tasks, which result in 25% less training
time.
Generative Adversarial Networks for Unpaired Voice Transformation on Impaired Speech
Authors: Li-Wei Chen, Hung-Yi Lee, Yu Tsao
Date: 2018-10-30 00:00:00
Tasks: Speech Recognition, Voice Conversion

This paper focuses on using voice conversion (VC) to improve the speech intelligibility of surgical patients who have had parts of their articulators removed. Due to the difficulty of data collection, VC without parallel data is highly desired. Although techniques for unparallel VC, for example, CycleGAN, have been developed, they usually focus on transforming the speaker identity, and directly transforming the speech of one speaker to that of another speaker and as such do not address the task here. In this paper, we propose a new approach for unparallel VC. The proposed approach transforms impaired speech to normal speech while preserving the linguistic content and speaker characteristics. To our knowledge, this is the first end-to-end GAN-based unsupervised VC model applied to impaired speech. The experimental results show that the proposed approach outperforms CycleGAN.
Prosodic entrainment in dialog acts
Authors: Uwe D. Reichel, Katalin Mády, Jennifer Cole
Date: 2018-10-30 00:00:00
Tasks: Rhythm

We examined prosodic entrainment in spoken dialogs separately for several
dialog acts in cooperative and competitive games. Entrainment was measured for
intonation features derived from a superpositional intonation stylization as
well as for rhythm features. The found differences can be related to the
cooperative or competitive nature of the game, as well as to dialog act
properties as its intrinsic authority, supportiveness and distributional
characteristics. In cooperative games dialog acts with a high authority given
by knowledge and with a high frequency showed the most entrainment. The results
are discussed amongst others with respect to the degree of active entrainment
control in cooperative behavior.
The Responsibility Quantification (ResQu) Model of Human Interaction with Automation
Authors: Nir Douer, Joachim Meyer
Date: 2018-10-30 00:00:00
Tasks: Autonomous Vehicles, Decision Making

Intelligent systems and advanced automation are involved in information collection and evaluation, in decision-making and in the implementation of chosen actions. In such systems, human responsibility becomes equivocal. Understanding human casual responsibility is particularly important when intelligent autonomous systems can harm people, as with autonomous vehicles or, most notably, with autonomous weapon systems (AWS). Using Information Theory, we develop a responsibility quantification (ResQu) model of human involvement in intelligent automated systems and demonstrate its applications on decisions regarding AWS. The analysis reveals that human comparative responsibility to outcomes is often low, even when major functions are allocated to the human. Thus, broadly stated policies of keeping humans in the loop and having meaningful human control are misleading and cannot truly direct decisions on how to involve humans in intelligent systems and advanced automation. The current model is an initial step in the complex goal to create a comprehensive responsibility model, that will enable quantification of human causal responsibility. It assumes stationarity, full knowledge regarding the characteristic of the human and automation and ignores temporal aspects. Despite these limitations, it can aid in the analysis of systems designs alternatives and policy decisions regarding human responsibility in intelligent systems and advanced automation.
Neuromorphic hardware as a self-organizing computing system
Authors: Lyes Khacef, Bernard Girau, Nicolas Rougier, Andres Upegui, Benoit Miramond
Date: 2018-10-30 00:00:00

This paper presents the self-organized neuromorphic architecture named SOMA.
The objective is to study neural-based self-organization in computing systems
and to prove the feasibility of a self-organizing hardware structure.
Considering that these properties emerge from large scale and fully connected
neural maps, we will focus on the definition of a self-organizing hardware
architecture based on digital spiking neurons that offer hardware efficiency.
From a biological point of view, this corresponds to a combination of the
so-called synaptic and structural plasticities. We intend to define
computational models able to simultaneously self-organize at both computation
and communication levels, and we want these models to be hardware-compliant,
fault tolerant and scalable by means of a neuro-cellular structure.
Towards End-to-end Automatic Code-Switching Speech Recognition
Authors: Genta Indra Winata, Andrea Madotto, Chien-Sheng Wu, Pascale Fung
Date: 2018-10-30 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Speech recognition in mixed language has difficulties to adapt end-to-end
framework due to the lack of data and overlapping phone sets, for example in
words such as "one" in English and "w\`an" in Chinese. We propose a CTC-based
end-to-end automatic speech recognition model for intra-sentential
English-Mandarin code-switching. The model is trained by joint training on
monolingual datasets, and fine-tuning with the mixed-language corpus. During
the decoding process, we apply a beam search and combine CTC predictions and
language model score. The proposed method is effective in leveraging
monolingual corpus and detecting language transitions and it improves the CER
by 5%.
Deep Learning as Feature Encoding for Emotion Recognition
Authors: Bhalaji Nagarajan, V Ramana Murthy Oruganti
Date: 2018-10-30 00:00:00
Tasks: Deep Learning, Emotion Recognition, General Classification

Deep learning is popular as an end-to-end framework extracting the prominent
features and performing the classification also. In this paper, we extensively
investigate deep networks as an alternate to feature encoding technique of low
level descriptors for emotion recognition on the benchmark EmoDB dataset.
Fusion performance with such obtained encoded features with other available
features is also investigated. Highest performance to date in the literature is
observed.
Adaptive Transfer Learning in Deep Neural Networks: Wind Power Prediction using Knowledge Transfer from Region to Region and Between Different Task Domains
Authors: Aqsa Saeed Qureshi, Asifullah Khan
Date: 2018-10-30 00:00:00
Tasks: Prediction, Transfer Learning

Transfer Learning (TL) in Deep Neural Networks is gaining importance because
in most of the applications, the labeling of data is costly and time-consuming.
Additionally, TL also provides an effective weight initialization strategy for
Deep Neural Networks . This paper introduces the idea of Adaptive Transfer
Learning in Deep Neural Networks (ATL-DNN) for wind power prediction.
Specifically, we show in case of wind power prediction that adaptive TL of Deep
Neural Networks system can be adaptively modified as regards training on a
different wind farm is concerned. The proposed ATL-DNN technique is tested for
short-term wind power prediction, where continuously arriving information has
to be exploited. Adaptive TL not only helps in providing good weight
initialization, but is also helpful to utilize the incoming data for effective
learning. Additionally, the proposed ATL-DNN technique is shown to transfer
knowledge between different task domains (wind power to wind speed prediction)
and from one region to another region. The simulation results show that the
proposed ATL-DNN technique achieves average values of 0.0637,0.0986, and 0.0984
for the Mean-Absolute-Error, Root-Mean-Squared-Error, and
Standard-Deviation-Error, respectively.
Waveform generation for text-to-speech synthesis using pitch-synchronous multi-scale generative adversarial networks
Authors: Lauri Juvela, Bajibabu Bollepalli, Junichi Yamagishi, Paavo Alku
Date: 2018-10-30 00:00:00
Tasks: Image Generation, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis, Voice Similarity

The state-of-the-art in text-to-speech synthesis has recently improved
considerably due to novel neural waveform generation methods, such as WaveNet.
However, these methods suffer from their slow sequential inference process,
while their parallel versions are difficult to train and even more expensive
computationally. Meanwhile, generative adversarial networks (GANs) have
achieved impressive results in image generation and are making their way into
audio applications; parallel inference is among their lucrative properties. By
adopting recent advances in GAN training techniques, this investigation studies
waveform generation for TTS in two domains (speech signal and glottal
excitation). Listening test results show that while direct waveform generation
with GAN is still far behind WaveNet, a GAN-based glottal excitation model can
achieve quality and voice similarity on par with a WaveNet vocoder.