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400 | Adversarial Examples for Semantic Image Segmentation | stat.ML | Machine learning methods in general and Deep Neural Networks in particular
have shown to be vulnerable to adversarial perturbations. So far this
phenomenon has mainly been studied in the context of whole-image
classification. In this contribution, we analyse how adversarial perturbations
can affect the task of semantic... | computer science |
401 | Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box
Machine Learning Models | stat.ML | Many machine learning algorithms are vulnerable to almost imperceptible
perturbations of their inputs. So far it was unclear how much risk adversarial
perturbations carry for the safety of real-world machine learning applications
because most methods used to generate such perturbations rely either on
detailed model inf... | computer science |
402 | Towards Building an Intelligent Anti-Malware System: A Deep Learning
Approach using Support Vector Machine (SVM) for Malware Classification | cs.NE | Effective and efficient mitigation of malware is a long-time endeavor in the
information security community. The development of an anti-malware system that
can counteract an unknown malware is a prolific activity that may benefit
several sectors. We envision an intelligent anti-malware system that utilizes
the power of... | computer science |
403 | Feature extraction using Latent Dirichlet Allocation and Neural
Networks: A case study on movie synopses | cs.CL | Feature extraction has gained increasing attention in the field of machine
learning, as in order to detect patterns, extract information, or predict
future observations from big data, the urge of informative features is crucial.
The process of extracting features is highly linked to dimensionality reduction
as it impli... | computer science |
404 | A Survey of Available Corpora for Building Data-Driven Dialogue Systems | cs.CL | During the past decade, several areas of speech and language understanding
have witnessed substantial breakthroughs from the use of data-driven models. In
the area of dialogue systems, the trend is less obvious, and most practical
systems are still built through significant engineering and expert knowledge.
Nevertheles... | computer science |
405 | Generative Topic Embedding: a Continuous Representation of Documents
(Extended Version with Proofs) | cs.CL | Word embedding maps words into a low-dimensional continuous embedding space
by exploiting the local word collocation patterns in a small context window. On
the other hand, topic modeling maps documents onto a low-dimensional topic
space, by utilizing the global word collocation patterns in the same document.
These two ... | computer science |
406 | Fine-Grained Entity Typing with High-Multiplicity Assignments | cs.CL | As entity type systems become richer and more fine-grained, we expect the
number of types assigned to a given entity to increase. However, most
fine-grained typing work has focused on datasets that exhibit a low degree of
type multiplicity. In this paper, we consider the high-multiplicity regime
inherent in data source... | computer science |
407 | Towards a Visual Turing Challenge | cs.AI | As language and visual understanding by machines progresses rapidly, we are
observing an increasing interest in holistic architectures that tightly
interlink both modalities in a joint learning and inference process. This trend
has allowed the community to progress towards more challenging and open tasks
and refueled t... | computer science |
408 | Interactive Robot Learning of Gestures, Language and Affordances | cs.RO | A growing field in robotics and Artificial Intelligence (AI) research is
human-robot collaboration, whose target is to enable effective teamwork between
humans and robots. However, in many situations human teams are still superior
to human-robot teams, primarily because human teams can easily agree on a
common goal wit... | computer science |
409 | Visual Features for Context-Aware Speech Recognition | cs.CL | Automatic transcriptions of consumer-generated multi-media content such as
"Youtube" videos still exhibit high word error rates. Such data typically
occupies a very broad domain, has been recorded in challenging conditions, with
cheap hardware and a focus on the visual modality, and may have been
post-processed or edit... | computer science |
410 | Examining Cooperation in Visual Dialog Models | cs.CV | In this work we propose a blackbox intervention method for visual dialog
models, with the aim of assessing the contribution of individual linguistic or
visual components. Concretely, we conduct structured or randomized
interventions that aim to impair an individual component of the model, and
observe changes in task pe... | computer science |
411 | Video Highlight Prediction Using Audience Chat Reactions | cs.CL | Sports channel video portals offer an exciting domain for research on
multimodal, multilingual analysis. We present methods addressing the problem of
automatic video highlight prediction based on joint visual features and textual
analysis of the real-world audience discourse with complex slang, in both
English and trad... | computer science |
412 | Invariant Representations for Noisy Speech Recognition | cs.CL | Modern automatic speech recognition (ASR) systems need to be robust under
acoustic variability arising from environmental, speaker, channel, and
recording conditions. Ensuring such robustness to variability is a challenge in
modern day neural network-based ASR systems, especially when all types of
variability are not s... | computer science |
413 | Self-Supervised Vision-Based Detection of the Active Speaker as a
Prerequisite for Socially-Aware Language Acquisition | cs.CV | This paper presents a self-supervised method for detecting the active speaker
in a multi-person spoken interaction scenario. We argue that this capability is
a fundamental prerequisite for any artificial cognitive system attempting to
acquire language in social settings. Our methods are able to detect an
arbitrary numb... | computer science |
414 | Product Characterisation towards Personalisation: Learning Attributes
from Unstructured Data to Recommend Fashion Products | stat.ML | In this paper, we describe a solution to tackle a common set of challenges in
e-commerce, which arise from the fact that new products are continually being
added to the catalogue. The challenges involve properly personalising the
customer experience, forecasting demand and planning the product range. We
argue that the ... | computer science |
415 | The Self-Organization of Speech Sounds | cs.LG | The speech code is a vehicle of language: it defines a set of forms used by a
community to carry information. Such a code is necessary to support the
linguistic interactions that allow humans to communicate. How then may a speech
code be formed prior to the existence of linguistic interactions? Moreover, the
human spee... | computer science |
416 | What the F-measure doesn't measure: Features, Flaws, Fallacies and Fixes | cs.IR | The F-measure or F-score is one of the most commonly used single number
measures in Information Retrieval, Natural Language Processing and Machine
Learning, but it is based on a mistake, and the flawed assumptions render it
unsuitable for use in most contexts! Fortunately, there are better
alternatives. | computer science |
417 | A Machine Learning Perspective on Predictive Coding with PAQ | cs.LG | PAQ8 is an open source lossless data compression algorithm that currently
achieves the best compression rates on many benchmarks. This report presents a
detailed description of PAQ8 from a statistical machine learning perspective.
It shows that it is possible to understand some of the modules of PAQ8 and use
this under... | computer science |
418 | A Novel Frank-Wolfe Algorithm. Analysis and Applications to Large-Scale
SVM Training | cs.CV | Recently, there has been a renewed interest in the machine learning community
for variants of a sparse greedy approximation procedure for concave
optimization known as {the Frank-Wolfe (FW) method}. In particular, this
procedure has been successfully applied to train large-scale instances of
non-linear Support Vector M... | computer science |
419 | Semi-supervised Vocabulary-informed Learning | cs.CV | Despite significant progress in object categorization, in recent years, a
number of important challenges remain, mainly, ability to learn from limited
labeled data and ability to recognize object classes within large, potentially
open, set of labels. Zero-shot learning is one way of addressing these
challenges, but it ... | computer science |
420 | Submodular meets Structured: Finding Diverse Subsets in
Exponentially-Large Structured Item Sets | cs.LG | To cope with the high level of ambiguity faced in domains such as Computer
Vision or Natural Language processing, robust prediction methods often search
for a diverse set of high-quality candidate solutions or proposals. In
structured prediction problems, this becomes a daunting task, as the solution
space (image label... | computer science |
421 | ZM-Net: Real-time Zero-shot Image Manipulation Network | cs.CV | Many problems in image processing and computer vision (e.g. colorization,
style transfer) can be posed as 'manipulating' an input image into a
corresponding output image given a user-specified guiding signal. A holy-grail
solution towards generic image manipulation should be able to efficiently alter
an input image wit... | computer science |
422 | Multi-Agent Diverse Generative Adversarial Networks | cs.CV | We propose an intuitive generalization to the Generative Adversarial Networks
(GANs) and its conditional variants to address the well known mode collapse
problem. Firstly, we propose a multi-agent GAN architecture incorporating
multiple generators and one discriminator. Secondly, to enforce different
generators to capt... | computer science |
423 | Geometric GAN | stat.ML | Generative Adversarial Nets (GANs) represent an important milestone for
effective generative models, which has inspired numerous variants seemingly
different from each other. One of the main contributions of this paper is to
reveal a unified geometric structure in GAN and its variants. Specifically, we
show that the ad... | computer science |
424 | A Data and Model-Parallel, Distributed and Scalable Framework for
Training of Deep Networks in Apache Spark | stat.ML | Training deep networks is expensive and time-consuming with the training
period increasing with data size and growth in model parameters. In this paper,
we provide a framework for distributed training of deep networks over a cluster
of CPUs in Apache Spark. The framework implements both Data Parallelism and
Model Paral... | computer science |
425 | Understanding and Comparing Deep Neural Networks for Age and Gender
Classification | stat.ML | Recently, deep neural networks have demonstrated excellent performances in
recognizing the age and gender on human face images. However, these models were
applied in a black-box manner with no information provided about which facial
features are actually used for prediction and how these features depend on
image prepro... | computer science |
426 | When is a Convolutional Filter Easy To Learn? | cs.LG | We analyze the convergence of (stochastic) gradient descent algorithm for
learning a convolutional filter with Rectified Linear Unit (ReLU) activation
function. Our analysis does not rely on any specific form of the input
distribution and our proofs only use the definition of ReLU, in contrast with
previous works that ... | computer science |
427 | Learning Sparse Visual Representations with Leaky Capped Norm
Regularizers | cs.LG | Sparsity inducing regularization is an important part for learning
over-complete visual representations. Despite the popularity of $\ell_1$
regularization, in this paper, we investigate the usage of non-convex
regularizations in this problem. Our contribution consists of three parts.
First, we propose the leaky capped ... | computer science |
428 | ConvNets and ImageNet Beyond Accuracy: Explanations, Bias Detection,
Adversarial Examples and Model Criticism | cs.LG | ConvNets and Imagenet have driven the recent success of deep learning for
image classification. However, the marked slowdown in performance improvement,
the recent studies on the lack of robustness of neural networks to adversarial
examples and their tendency to exhibit undesirable biases (e.g racial biases)
questioned... | computer science |
429 | Gradient Descent Learns One-hidden-layer CNN: Don't be Afraid of
Spurious Local Minima | cs.LG | We consider the problem of learning a one-hidden-layer neural network with
non-overlapping convolutional layer and ReLU activation function, i.e.,
$f(\mathbf{Z}; \mathbf{w}, \mathbf{a}) = \sum_j
a_j\sigma(\mathbf{w}^\top\mathbf{Z}_j)$, in which both the convolutional
weights $\mathbf{w}$ and the output weights $\mathbf... | computer science |
430 | Curiosity-driven Exploration by Self-supervised Prediction | cs.LG | In many real-world scenarios, rewards extrinsic to the agent are extremely
sparse, or absent altogether. In such cases, curiosity can serve as an
intrinsic reward signal to enable the agent to explore its environment and
learn skills that might be useful later in its life. We formulate curiosity as
the error in an agen... | computer science |
431 | Houdini: Fooling Deep Structured Prediction Models | stat.ML | Generating adversarial examples is a critical step for evaluating and
improving the robustness of learning machines. So far, most existing methods
only work for classification and are not designed to alter the true performance
measure of the problem at hand. We introduce a novel flexible approach named
Houdini for gene... | computer science |
432 | Recent Advances in Zero-shot Recognition | cs.CV | With the recent renaissance of deep convolution neural networks, encouraging
breakthroughs have been achieved on the supervised recognition tasks, where
each class has sufficient training data and fully annotated training data.
However, to scale the recognition to a large number of classes with few or now
training samp... | computer science |
433 | The loss surface and expressivity of deep convolutional neural networks | cs.LG | We analyze the expressiveness and loss surface of practical deep
convolutional neural networks (CNNs) with shared weights and max pooling
layers. We show that such CNNs produce linearly independent features at a
"wide" layer which has more neurons than the number of training samples. This
condition holds e.g. for the V... | computer science |
434 | Physics-guided Neural Networks (PGNN): An Application in Lake
Temperature Modeling | cs.LG | This paper introduces a novel framework for combining scientific knowledge of
physics-based models with neural networks to advance scientific discovery. This
framework, termed as physics-guided neural network (PGNN), leverages the output
of physics-based model simulations along with observational features to
generate p... | computer science |
435 | Unified Spectral Clustering with Optimal Graph | cs.LG | Spectral clustering has found extensive use in many areas. Most traditional
spectral clustering algorithms work in three separate steps: similarity graph
construction; continuous labels learning; discretizing the learned labels by
k-means clustering. Such common practice has two potential flaws, which may
lead to sever... | computer science |
436 | On the Inductive Bias of Dropout | cs.LG | Dropout is a simple but effective technique for learning in neural networks
and other settings. A sound theoretical understanding of dropout is needed to
determine when dropout should be applied and how to use it most effectively. In
this paper we continue the exploration of dropout as a regularizer pioneered by
Wager,... | computer science |
437 | Surprising properties of dropout in deep networks | cs.LG | We analyze dropout in deep networks with rectified linear units and the
quadratic loss. Our results expose surprising differences between the behavior
of dropout and more traditional regularizers like weight decay. For example, on
some simple data sets dropout training produces negative weights even though
the output i... | computer science |
438 | Training Probabilistic Spiking Neural Networks with First-to-spike
Decoding | stat.ML | Third-generation neural networks, or Spiking Neural Networks (SNNs), aim at
harnessing the energy efficiency of spike-domain processing by building on
computing elements that operate on, and exchange, spikes. In this paper, the
problem of training a two-layer SNN is studied for the purpose of
classification, under a Ge... | computer science |
439 | A Novel Clustering Algorithm Based on Quantum Games | cs.LG | Enormous successes have been made by quantum algorithms during the last
decade. In this paper, we combine the quantum game with the problem of data
clustering, and then develop a quantum-game-based clustering algorithm, in
which data points in a dataset are considered as players who can make decisions
and implement qua... | computer science |
440 | Exact solutions to the nonlinear dynamics of learning in deep linear
neural networks | cs.NE | Despite the widespread practical success of deep learning methods, our
theoretical understanding of the dynamics of learning in deep neural networks
remains quite sparse. We attempt to bridge the gap between the theory and
practice of deep learning by systematically analyzing learning dynamics for the
restricted case o... | computer science |
441 | Entropy of Overcomplete Kernel Dictionaries | cs.IT | In signal analysis and synthesis, linear approximation theory considers a
linear decomposition of any given signal in a set of atoms, collected into a
so-called dictionary. Relevant sparse representations are obtained by relaxing
the orthogonality condition of the atoms, yielding overcomplete dictionaries
with an exten... | computer science |
442 | Rotation-invariant convolutional neural networks for galaxy morphology
prediction | cs.CV | Measuring the morphological parameters of galaxies is a key requirement for
studying their formation and evolution. Surveys such as the Sloan Digital Sky
Survey (SDSS) have resulted in the availability of very large collections of
images, which have permitted population-wide analyses of galaxy morphology.
Morphological... | computer science |
443 | Kernel Nonnegative Matrix Factorization Without the Curse of the
Pre-image - Application to Unmixing Hyperspectral Images | cs.CV | The nonnegative matrix factorization (NMF) is widely used in signal and image
processing, including bio-informatics, blind source separation and
hyperspectral image analysis in remote sensing. A great challenge arises when
dealing with a nonlinear formulation of the NMF. Within the framework of kernel
machines, the mod... | computer science |
444 | Approximation errors of online sparsification criteria | stat.ML | Many machine learning frameworks, such as resource-allocating networks,
kernel-based methods, Gaussian processes, and radial-basis-function networks,
require a sparsification scheme in order to address the online learning
paradigm. For this purpose, several online sparsification criteria have been
proposed to restrict ... | computer science |
445 | Discrete Deep Feature Extraction: A Theory and New Architectures | cs.LG | First steps towards a mathematical theory of deep convolutional neural
networks for feature extraction were made---for the continuous-time case---in
Mallat, 2012, and Wiatowski and B\"olcskei, 2015. This paper considers the
discrete case, introduces new convolutional neural network architectures, and
proposes a mathema... | computer science |
446 | Neural Responding Machine for Short-Text Conversation | cs.CL | We propose Neural Responding Machine (NRM), a neural network-based response
generator for Short-Text Conversation. NRM takes the general encoder-decoder
framework: it formalizes the generation of response as a decoding process based
on the latent representation of the input text, while both encoding and
decoding are re... | computer science |
447 | Deep Active Learning for Dialogue Generation | cs.CL | We propose an online, end-to-end, neural generative conversational model for
open-domain dialogue. It is trained using a unique combination of offline
two-phase supervised learning and online human-in-the-loop active learning.
While most existing research proposes offline supervision or hand-crafted
reward functions fo... | computer science |
448 | Teaching Machines to Read and Comprehend | cs.CL | Teaching machines to read natural language documents remains an elusive
challenge. Machine reading systems can be tested on their ability to answer
questions posed on the contents of documents that they have seen, but until now
large scale training and test datasets have been missing for this type of
evaluation. In thi... | computer science |
449 | Syntax-Aware Multi-Sense Word Embeddings for Deep Compositional Models
of Meaning | cs.CL | Deep compositional models of meaning acting on distributional representations
of words in order to produce vectors of larger text constituents are evolving
to a popular area of NLP research. We detail a compositional distributional
framework based on a rich form of word embeddings that aims at facilitating the
interact... | computer science |
450 | A Deep Architecture for Semantic Matching with Multiple Positional
Sentence Representations | cs.AI | Matching natural language sentences is central for many applications such as
information retrieval and question answering. Existing deep models rely on a
single sentence representation or multiple granularity representations for
matching. However, such methods cannot well capture the contextualized local
information in... | computer science |
451 | LSTM Neural Reordering Feature for Statistical Machine Translation | cs.CL | Artificial neural networks are powerful models, which have been widely
applied into many aspects of machine translation, such as language modeling and
translation modeling. Though notable improvements have been made in these
areas, the reordering problem still remains a challenge in statistical machine
translations. In... | computer science |
452 | Learning Natural Language Inference with LSTM | cs.CL | Natural language inference (NLI) is a fundamentally important task in natural
language processing that has many applications. The recently released Stanford
Natural Language Inference (SNLI) corpus has made it possible to develop and
evaluate learning-centered methods such as deep neural networks for natural
language i... | computer science |
453 | Quantifying the vanishing gradient and long distance dependency problem
in recursive neural networks and recursive LSTMs | cs.AI | Recursive neural networks (RNN) and their recently proposed extension
recursive long short term memory networks (RLSTM) are models that compute
representations for sentences, by recursively combining word embeddings
according to an externally provided parse tree. Both models thus, unlike
recurrent networks, explicitly ... | computer science |
454 | Implicit Discourse Relation Classification via Multi-Task Neural
Networks | cs.CL | Without discourse connectives, classifying implicit discourse relations is a
challenging task and a bottleneck for building a practical discourse parser.
Previous research usually makes use of one kind of discourse framework such as
PDTB or RST to improve the classification performance on discourse relations.
Actually,... | computer science |
455 | Enhancing Sentence Relation Modeling with Auxiliary Character-level
Embedding | cs.CL | Neural network based approaches for sentence relation modeling automatically
generate hidden matching features from raw sentence pairs. However, the quality
of matching feature representation may not be satisfied due to complex semantic
relations such as entailment or contradiction. To address this challenge, we
propos... | computer science |
456 | Automatic Open Knowledge Acquisition via Long Short-Term Memory Networks
with Feedback Negative Sampling | cs.CL | Previous studies in Open Information Extraction (Open IE) are mainly based on
extraction patterns. They manually define patterns or automatically learn them
from a large corpus. However, these approaches are limited when grasping the
context of a sentence, and they fail to capture implicit relations. In this
paper, we ... | computer science |
457 | Question Answering over Knowledge Base with Neural Attention Combining
Global Knowledge Information | cs.IR | With the rapid growth of knowledge bases (KBs) on the web, how to take full
advantage of them becomes increasingly important. Knowledge base-based question
answering (KB-QA) is one of the most promising approaches to access the
substantial knowledge. Meantime, as the neural network-based (NN-based) methods
develop, NN-... | computer science |
458 | Generating Natural Language Inference Chains | cs.CL | The ability to reason with natural language is a fundamental prerequisite for
many NLP tasks such as information extraction, machine translation and question
answering. To quantify this ability, systems are commonly tested whether they
can recognize textual entailment, i.e., whether one sentence can be inferred
from an... | computer science |
459 | MuFuRU: The Multi-Function Recurrent Unit | cs.NE | Recurrent neural networks such as the GRU and LSTM found wide adoption in
natural language processing and achieve state-of-the-art results for many
tasks. These models are characterized by a memory state that can be written to
and read from by applying gated composition operations to the current input and
the previous ... | computer science |
460 | LSTMVis: A Tool for Visual Analysis of Hidden State Dynamics in
Recurrent Neural Networks | cs.CL | Recurrent neural networks, and in particular long short-term memory (LSTM)
networks, are a remarkably effective tool for sequence modeling that learn a
dense black-box hidden representation of their sequential input. Researchers
interested in better understanding these models have studied the changes in
hidden state re... | computer science |
461 | Compression of Neural Machine Translation Models via Pruning | cs.AI | Neural Machine Translation (NMT), like many other deep learning domains,
typically suffers from over-parameterization, resulting in large storage sizes.
This paper examines three simple magnitude-based pruning schemes to compress
NMT models, namely class-blind, class-uniform, and class-distribution, which
differ in ter... | computer science |
462 | Constructing a Natural Language Inference Dataset using Generative
Neural Networks | cs.AI | Natural Language Inference is an important task for Natural Language
Understanding. It is concerned with classifying the logical relation between
two sentences. In this paper, we propose several text generative neural
networks for generating text hypothesis, which allows construction of new
Natural Language Inference d... | computer science |
463 | Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain
Factoid Question Answering | cs.CL | While question answering (QA) with neural network, i.e. neural QA, has
achieved promising results in recent years, lacking of large scale real-word QA
dataset is still a challenge for developing and evaluating neural QA system. To
alleviate this problem, we propose a large scale human annotated real-world QA
dataset We... | computer science |
464 | Tweet2Vec: Learning Tweet Embeddings Using Character-level CNN-LSTM
Encoder-Decoder | cs.CL | We present Tweet2Vec, a novel method for generating general-purpose vector
representation of tweets. The model learns tweet embeddings using
character-level CNN-LSTM encoder-decoder. We trained our model on 3 million,
randomly selected English-language tweets. The model was evaluated using two
methods: tweet semantic s... | computer science |
465 | Online Segment to Segment Neural Transduction | cs.CL | We introduce an online neural sequence to sequence model that learns to
alternate between encoding and decoding segments of the input as it is read. By
independently tracking the encoding and decoding representations our algorithm
permits exact polynomial marginalization of the latent segmentation during
training, and ... | computer science |
466 | Semantic Parsing with Semi-Supervised Sequential Autoencoders | cs.CL | We present a novel semi-supervised approach for sequence transduction and
apply it to semantic parsing. The unsupervised component is based on a
generative model in which latent sentences generate the unpaired logical forms.
We apply this method to a number of semantic parsing tasks focusing on domains
with limited acc... | computer science |
467 | Exploiting Sentence and Context Representations in Deep Neural Models
for Spoken Language Understanding | cs.AI | This paper presents a deep learning architecture for the semantic decoder
component of a Statistical Spoken Dialogue System. In a slot-filling dialogue,
the semantic decoder predicts the dialogue act and a set of slot-value pairs
from a set of n-best hypotheses returned by the Automatic Speech Recognition.
Most current... | computer science |
468 | The Neural Noisy Channel | cs.CL | We formulate sequence to sequence transduction as a noisy channel decoding
problem and use recurrent neural networks to parameterise the source and
channel models. Unlike direct models which can suffer from explaining-away
effects during training, noisy channel models must produce outputs that explain
their inputs, and... | computer science |
469 | Generative Deep Neural Networks for Dialogue: A Short Review | cs.CL | Researchers have recently started investigating deep neural networks for
dialogue applications. In particular, generative sequence-to-sequence (Seq2Seq)
models have shown promising results for unstructured tasks, such as word-level
dialogue response generation. The hope is that such models will be able to
leverage mass... | computer science |
470 | Learning Python Code Suggestion with a Sparse Pointer Network | cs.NE | To enhance developer productivity, all modern integrated development
environments (IDEs) include code suggestion functionality that proposes likely
next tokens at the cursor. While current IDEs work well for statically-typed
languages, their reliance on type annotations means that they do not provide
the same level of ... | computer science |
471 | OpenNMT: Open-Source Toolkit for Neural Machine Translation | cs.CL | We describe an open-source toolkit for neural machine translation (NMT). The
toolkit prioritizes efficiency, modularity, and extensibility with the goal of
supporting NMT research into model architectures, feature representations, and
source modalities, while maintaining competitive performance and reasonable
training ... | computer science |
472 | Making Neural QA as Simple as Possible but not Simpler | cs.CL | Recent development of large-scale question answering (QA) datasets triggered
a substantial amount of research into end-to-end neural architectures for QA.
Increasingly complex systems have been conceived without comparison to simpler
neural baseline systems that would justify their complexity. In this work, we
propose ... | computer science |
473 | Survey of the State of the Art in Natural Language Generation: Core
tasks, applications and evaluation | cs.CL | This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) ... | computer science |
474 | A Constrained Sequence-to-Sequence Neural Model for Sentence
Simplification | cs.CL | Sentence simplification reduces semantic complexity to benefit people with
language impairments. Previous simplification studies on the sentence level and
word level have achieved promising results but also meet great challenges. For
sentence-level studies, sentences after simplification are fluent but sometimes
are no... | computer science |
475 | Improved Neural Relation Detection for Knowledge Base Question Answering | cs.CL | Relation detection is a core component for many NLP applications including
Knowledge Base Question Answering (KBQA). In this paper, we propose a
hierarchical recurrent neural network enhanced by residual learning that
detects KB relations given an input question. Our method uses deep residual
bidirectional LSTMs to com... | computer science |
476 | ASR error management for improving spoken language understanding | cs.CL | This paper addresses the problem of automatic speech recognition (ASR) error
detection and their use for improving spoken language understanding (SLU)
systems. In this study, the SLU task consists in automatically extracting, from
ASR transcriptions , semantic concepts and concept/values pairs in a e.g
touristic inform... | computer science |
477 | Dynamic Integration of Background Knowledge in Neural NLU Systems | cs.CL | Common-sense or background knowledge is required to understand natural
language, but in most neural natural language understanding (NLU) systems, the
requisite background knowledge is indirectly acquired from static corpora. We
develop a new reading architecture for the dynamic integration of explicit
background knowle... | computer science |
478 | Rethinking Skip-thought: A Neighborhood based Approach | cs.CL | We study the skip-thought model with neighborhood information as weak
supervision. More specifically, we propose a skip-thought neighbor model to
consider the adjacent sentences as a neighborhood. We train our skip-thought
neighbor model on a large corpus with continuous sentences, and then evaluate
the trained model o... | computer science |
479 | Neural Domain Adaptation for Biomedical Question Answering | cs.CL | Factoid question answering (QA) has recently benefited from the development
of deep learning (DL) systems. Neural network models outperform traditional
approaches in domains where large datasets exist, such as SQuAD (ca. 100,000
questions) for Wikipedia articles. However, these systems have not yet been
applied to QA i... | computer science |
480 | Neural Models for Key Phrase Detection and Question Generation | cs.CL | We propose a two-stage neural model to tackle question generation from
documents. Our model first estimates the probability that word sequences in a
document compose "interesting" answers using a neural model trained on a
question-answering corpus. We thus take a data-driven approach to
interestingness. Predicted key p... | computer science |
481 | Neural Question Answering at BioASQ 5B | cs.CL | This paper describes our submission to the 2017 BioASQ challenge. We
participated in Task B, Phase B which is concerned with biomedical question
answering (QA). We focus on factoid and list question, using an extractive QA
model, that is, we restrict our system to output substrings of the provided
text snippets. At the... | computer science |
482 | A Deep Network with Visual Text Composition Behavior | cs.CL | While natural languages are compositional, how state-of-the-art neural models
achieve compositionality is still unclear. We propose a deep network, which not
only achieves competitive accuracy for text classification, but also exhibits
compositional behavior. That is, while creating hierarchical representations of
a pi... | computer science |
483 | Semi-supervised emotion lexicon expansion with label propagation and
specialized word embeddings | cs.CL | There exist two main approaches to automatically extract affective
orientation: lexicon-based and corpus-based. In this work, we argue that these
two methods are compatible and show that combining them can improve the
accuracy of emotion classifiers. In particular, we introduce a novel variant of
the Label Propagation ... | computer science |
484 | Modelling Protagonist Goals and Desires in First-Person Narrative | cs.AI | Many genres of natural language text are narratively structured, a testament
to our predilection for organizing our experiences as narratives. There is
broad consensus that understanding a narrative requires identifying and
tracking the goals and desires of the characters and their narrative outcomes.
However, to date,... | computer science |
485 | Understanding Grounded Language Learning Agents | cs.CL | Neural network-based systems can now learn to locate the referents of words
and phrases in images, answer questions about visual scenes, and even execute
symbolic instructions as first-person actors in partially-observable worlds. To
achieve this so-called grounded language learning, models must overcome certain
well-s... | computer science |
486 | Just ASK: Building an Architecture for Extensible Self-Service Spoken
Language Understanding | cs.CL | This paper presents the design of the machine learning architecture that
underlies the Alexa Skills Kit (ASK) a large scale Spoken Language
Understanding (SLU) Software Development Kit (SDK) that enables developers to
extend the capabilities of Amazon's virtual assistant, Alexa. At Amazon, the
infrastructure powers ove... | computer science |
487 | The NarrativeQA Reading Comprehension Challenge | cs.CL | Reading comprehension (RC)---in contrast to information retrieval---requires
integrating information and reasoning about events, entities, and their
relations across a full document. Question answering is conventionally used to
assess RC ability, in both artificial agents and children learning to read.
However, existin... | computer science |
488 | Cognitive Database: A Step towards Endowing Relational Databases with
Artificial Intelligence Capabilities | cs.DB | We propose Cognitive Databases, an approach for transparently enabling
Artificial Intelligence (AI) capabilities in relational databases. A novel
aspect of our design is to first view the structured data source as meaningful
unstructured text, and then use the text to build an unsupervised neural
network model using a ... | computer science |
489 | Feudal Reinforcement Learning for Dialogue Management in Large Domains | cs.CL | Reinforcement learning (RL) is a promising approach to solve dialogue policy
optimisation. Traditional RL algorithms, however, fail to scale to large
domains due to the curse of dimensionality. We propose a novel Dialogue
Management architecture, based on Feudal RL, which decomposes the decision into
two steps; a first... | computer science |
490 | An Analysis of Neural Language Modeling at Multiple Scales | cs.CL | Many of the leading approaches in language modeling introduce novel, complex
and specialized architectures. We take existing state-of-the-art word level
language models based on LSTMs and QRNNs and extend them to both larger
vocabularies as well as character-level granularity. When properly tuned, LSTMs
and QRNNs achie... | computer science |
491 | Spatial Diffuseness Features for DNN-Based Speech Recognition in Noisy
and Reverberant Environments | cs.CL | We propose a spatial diffuseness feature for deep neural network (DNN)-based
automatic speech recognition to improve recognition accuracy in reverberant and
noisy environments. The feature is computed in real-time from multiple
microphone signals without requiring knowledge or estimation of the direction
of arrival, an... | computer science |
492 | Character-Aware Neural Language Models | cs.CL | We describe a simple neural language model that relies only on
character-level inputs. Predictions are still made at the word-level. Our model
employs a convolutional neural network (CNN) and a highway network over
characters, whose output is given to a long short-term memory (LSTM) recurrent
neural network language mo... | computer science |
493 | Neural-based machine translation for medical text domain. Based on
European Medicines Agency leaflet texts | cs.CL | The quality of machine translation is rapidly evolving. Today one can find
several machine translation systems on the web that provide reasonable
translations, although the systems are not perfect. In some specific domains,
the quality may decrease. A recently proposed approach to this domain is neural
machine translat... | computer science |
494 | Conditional Generation and Snapshot Learning in Neural Dialogue Systems | cs.CL | Recently a variety of LSTM-based conditional language models (LM) have been
applied across a range of language generation tasks. In this work we study
various model architectures and different ways to represent and aggregate the
source information in an end-to-end neural dialogue system framework. A method
called snaps... | computer science |
495 | Dialog state tracking, a machine reading approach using Memory Network | cs.CL | In an end-to-end dialog system, the aim of dialog state tracking is to
accurately estimate a compact representation of the current dialog status from
a sequence of noisy observations produced by the speech recognition and the
natural language understanding modules. This paper introduces a novel method of
dialog state t... | computer science |
496 | A Physical Metaphor to Study Semantic Drift | cs.CL | In accessibility tests for digital preservation, over time we experience
drifts of localized and labelled content in statistical models of evolving
semantics represented as a vector field. This articulates the need to detect,
measure, interpret and model outcomes of knowledge dynamics. To this end we
employ a high-perf... | computer science |
497 | Optimizing Neural Network Hyperparameters with Gaussian Processes for
Dialog Act Classification | cs.CL | Systems based on artificial neural networks (ANNs) have achieved
state-of-the-art results in many natural language processing tasks. Although
ANNs do not require manually engineered features, ANNs have many
hyperparameters to be optimized. The choice of hyperparameters significantly
impacts models' performances. Howeve... | computer science |
498 | A Survey of Voice Translation Methodologies - Acoustic Dialect Decoder | cs.CL | Speech Translation has always been about giving source text or audio input
and waiting for system to give translated output in desired form. In this
paper, we present the Acoustic Dialect Decoder (ADD) - a voice to voice
ear-piece translation device. We introduce and survey the recent advances made
in the field of Spee... | computer science |
499 | Learning to Reason With Adaptive Computation | cs.CL | Multi-hop inference is necessary for machine learning systems to successfully
solve tasks such as Recognising Textual Entailment and Machine Reading. In this
work, we demonstrate the effectiveness of adaptive computation for learning the
number of inference steps required for examples of different complexity and
that l... | computer science |
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