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500 | Feature-Augmented Neural Networks for Patient Note De-identification | cs.CL | Patient notes contain a wealth of information of potentially great interest
to medical investigators. However, to protect patients' privacy, Protected
Health Information (PHI) must be removed from the patient notes before they can
be legally released, a process known as patient note de-identification. The
main objectiv... | computer science |
501 | Direct Acoustics-to-Word Models for English Conversational Speech
Recognition | cs.CL | Recent work on end-to-end automatic speech recognition (ASR) has shown that
the connectionist temporal classification (CTC) loss can be used to convert
acoustics to phone or character sequences. Such systems are used with a
dictionary and separately-trained Language Model (LM) to produce word
sequences. However, they a... | computer science |
502 | Factorization tricks for LSTM networks | cs.CL | We present two simple ways of reducing the number of parameters and
accelerating the training of large Long Short-Term Memory (LSTM) networks: the
first one is "matrix factorization by design" of LSTM matrix into the product
of two smaller matrices, and the second one is partitioning of LSTM matrix, its
inputs and stat... | computer science |
503 | NeuroNER: an easy-to-use program for named-entity recognition based on
neural networks | cs.CL | Named-entity recognition (NER) aims at identifying entities of interest in a
text. Artificial neural networks (ANNs) have recently been shown to outperform
existing NER systems. However, ANNs remain challenging to use for non-expert
users. In this paper, we present NeuroNER, an easy-to-use named-entity
recognition tool... | computer science |
504 | Syllable-aware Neural Language Models: A Failure to Beat Character-aware
Ones | cs.CL | Syllabification does not seem to improve word-level RNN language modeling
quality when compared to character-based segmentation. However, our best
syllable-aware language model, achieving performance comparable to the
competitive character-aware model, has 18%-33% fewer parameters and is trained
1.2-2.2 times faster. | computer science |
505 | A Benchmarking Environment for Reinforcement Learning Based Task
Oriented Dialogue Management | stat.ML | Dialogue assistants are rapidly becoming an indispensable daily aid. To avoid
the significant effort needed to hand-craft the required dialogue flow, the
Dialogue Management (DM) module can be cast as a continuous Markov Decision
Process (MDP) and trained through Reinforcement Learning (RL). Several RL
models have been... | computer science |
506 | Reusing Weights in Subword-aware Neural Language Models | cs.CL | We propose several ways of reusing subword embeddings and other weights in
subword-aware neural language models. The proposed techniques do not benefit a
competitive character-aware model, but some of them improve the performance of
syllable- and morpheme-aware models while showing significant reductions in
model sizes... | computer science |
507 | Multi-task Learning of Pairwise Sequence Classification Tasks Over
Disparate Label Spaces | cs.CL | We combine multi-task learning and semi-supervised learning by inducing a
joint embedding space between disparate label spaces and learning transfer
functions between label embeddings, enabling us to jointly leverage unlabelled
data and auxiliary, annotated datasets. We evaluate our approach on a variety
of sequence cl... | computer science |
508 | Dynamic Memory Networks for Visual and Textual Question Answering | cs.NE | Neural network architectures with memory and attention mechanisms exhibit
certain reasoning capabilities required for question answering. One such
architecture, the dynamic memory network (DMN), obtained high accuracy on a
variety of language tasks. However, it was not shown whether the architecture
achieves strong res... | computer science |
509 | Picture It In Your Mind: Generating High Level Visual Representations
From Textual Descriptions | cs.IR | In this paper we tackle the problem of image search when the query is a short
textual description of the image the user is looking for. We choose to
implement the actual search process as a similarity search in a visual feature
space, by learning to translate a textual query into a visual representation.
Searching in t... | computer science |
510 | Where to put the Image in an Image Caption Generator | cs.NE | When a recurrent neural network language model is used for caption
generation, the image information can be fed to the neural network either by
directly incorporating it in the RNN -- conditioning the language model by
`injecting' image features -- or in a layer following the RNN -- conditioning
the language model by `... | computer science |
511 | A Focused Dynamic Attention Model for Visual Question Answering | cs.CV | Visual Question and Answering (VQA) problems are attracting increasing
interest from multiple research disciplines. Solving VQA problems requires
techniques from both computer vision for understanding the visual contents of a
presented image or video, as well as the ones from natural language processing
for understandi... | computer science |
512 | Simple Image Description Generator via a Linear Phrase-Based Approach | cs.CL | Generating a novel textual description of an image is an interesting problem
that connects computer vision and natural language processing. In this paper,
we present a simple model that is able to generate descriptive sentences given
a sample image. This model has a strong focus on the syntax of the
descriptions. We tr... | computer science |
513 | Multimodal Convolutional Neural Networks for Matching Image and Sentence | cs.CV | In this paper, we propose multimodal convolutional neural networks (m-CNNs)
for matching image and sentence. Our m-CNN provides an end-to-end framework
with convolutional architectures to exploit image representation, word
composition, and the matching relations between the two modalities. More
specifically, it consist... | computer science |
514 | Learning to Compose Neural Networks for Question Answering | cs.CL | We describe a question answering model that applies to both images and
structured knowledge bases. The model uses natural language strings to
automatically assemble neural networks from a collection of composable modules.
Parameters for these modules are learned jointly with network-assembly
parameters via reinforcemen... | computer science |
515 | Signer-independent Fingerspelling Recognition with Deep Neural Network
Adaptation | cs.CL | We study the problem of recognition of fingerspelled letter sequences in
American Sign Language in a signer-independent setting. Fingerspelled sequences
are both challenging and important to recognize, as they are used for many
content words such as proper nouns and technical terms. Previous work has shown
that it is p... | computer science |
516 | Full-Network Embedding in a Multimodal Embedding Pipeline | cs.CV | The current state-of-the-art for image annotation and image retrieval tasks
is obtained through deep neural networks, which combine an image representation
and a text representation into a shared embedding space. In this paper we
evaluate the impact of using the Full-Network embedding in this setting,
replacing the ori... | computer science |
517 | What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption
Generator? | cs.CL | In neural image captioning systems, a recurrent neural network (RNN) is
typically viewed as the primary `generation' component. This view suggests that
the image features should be `injected' into the RNN. This is in fact the
dominant view in the literature. Alternatively, the RNN can instead be viewed
as only encoding... | computer science |
518 | A Fixed-Size Encoding Method for Variable-Length Sequences with its
Application to Neural Network Language Models | cs.NE | In this paper, we propose the new fixed-size ordinally-forgetting encoding
(FOFE) method, which can almost uniquely encode any variable-length sequence of
words into a fixed-size representation. FOFE can model the word order in a
sequence using a simple ordinally-forgetting mechanism according to the
positions of words... | computer science |
519 | Transition-Based Dependency Parsing with Stack Long Short-Term Memory | cs.CL | We propose a technique for learning representations of parser states in
transition-based dependency parsers. Our primary innovation is a new control
structure for sequence-to-sequence neural networks---the stack LSTM. Like the
conventional stack data structures used in transition-based parsing, elements
can be pushed t... | computer science |
520 | A Semisupervised Approach for Language Identification based on Ladder
Networks | cs.CL | In this study we address the problem of training a neuralnetwork for language
identification using both labeled and unlabeled speech samples in the form of
i-vectors. We propose a neural network architecture that can also handle
out-of-set languages. We utilize a modified version of the recently proposed
Ladder Network... | computer science |
521 | First-Pass Large Vocabulary Continuous Speech Recognition using
Bi-Directional Recurrent DNNs | cs.CL | We present a method to perform first-pass large vocabulary continuous speech
recognition using only a neural network and language model. Deep neural network
acoustic models are now commonplace in HMM-based speech recognition systems,
but building such systems is a complex, domain-specific task. Recent work
demonstrated... | computer science |
522 | Applying deep learning techniques on medical corpora from the World Wide
Web: a prototypical system and evaluation | cs.CL | BACKGROUND: The amount of biomedical literature is rapidly growing and it is
becoming increasingly difficult to keep manually curated knowledge bases and
ontologies up-to-date. In this study we applied the word2vec deep learning
toolkit to medical corpora to test its potential for identifying relationships
from unstruc... | computer science |
523 | Syntax-based Deep Matching of Short Texts | cs.CL | Many tasks in natural language processing, ranging from machine translation
to question answering, can be reduced to the problem of matching two sentences
or more generally two short texts. We propose a new approach to the problem,
called Deep Match Tree (DeepMatch$_{tree}$), under a general setting. The
approach consi... | computer science |
524 | Ensemble of Generative and Discriminative Techniques for Sentiment
Analysis of Movie Reviews | cs.CL | Sentiment analysis is a common task in natural language processing that aims
to detect polarity of a text document (typically a consumer review). In the
simplest settings, we discriminate only between positive and negative
sentiment, turning the task into a standard binary classification problem. We
compare several ma-... | computer science |
525 | Diverse Embedding Neural Network Language Models | cs.CL | We propose Diverse Embedding Neural Network (DENN), a novel architecture for
language models (LMs). A DENNLM projects the input word history vector onto
multiple diverse low-dimensional sub-spaces instead of a single
higher-dimensional sub-space as in conventional feed-forward neural network
LMs. We encourage these sub... | computer science |
526 | Learning linearly separable features for speech recognition using
convolutional neural networks | cs.LG | Automatic speech recognition systems usually rely on spectral-based features,
such as MFCC of PLP. These features are extracted based on prior knowledge such
as, speech perception or/and speech production. Recently, convolutional neural
networks have been shown to be able to estimate phoneme conditional
probabilities i... | computer science |
527 | Learning to Transduce with Unbounded Memory | cs.NE | Recently, strong results have been demonstrated by Deep Recurrent Neural
Networks on natural language transduction problems. In this paper we explore
the representational power of these models using synthetic grammars designed to
exhibit phenomena similar to those found in real transduction problems such as
machine tra... | computer science |
528 | Feedforward Sequential Memory Neural Networks without Recurrent Feedback | cs.NE | We introduce a new structure for memory neural networks, called feedforward
sequential memory networks (FSMN), which can learn long-term dependency without
using recurrent feedback. The proposed FSMN is a standard feedforward neural
networks equipped with learnable sequential memory blocks in the hidden layers.
In this... | computer science |
529 | Towards Structured Deep Neural Network for Automatic Speech Recognition | cs.CL | In this paper we propose the Structured Deep Neural Network (structured DNN)
as a structured and deep learning framework. This approach can learn to find
the best structured object (such as a label sequence) given a structured input
(such as a vector sequence) by globally considering the mapping relationships
between t... | computer science |
530 | Character-Level Incremental Speech Recognition with Recurrent Neural
Networks | cs.CL | In real-time speech recognition applications, the latency is an important
issue. We have developed a character-level incremental speech recognition (ISR)
system that responds quickly even during the speech, where the hypotheses are
gradually improved while the speaking proceeds. The algorithm employs a
speech-to-charac... | computer science |
531 | Globally Normalized Transition-Based Neural Networks | cs.CL | We introduce a globally normalized transition-based neural network model that
achieves state-of-the-art part-of-speech tagging, dependency parsing and
sentence compression results. Our model is a simple feed-forward neural network
that operates on a task-specific transition system, yet achieves comparable or
better acc... | computer science |
532 | Clinical Information Extraction via Convolutional Neural Network | cs.LG | We report an implementation of a clinical information extraction tool that
leverages deep neural network to annotate event spans and their attributes from
raw clinical notes and pathology reports. Our approach uses context words and
their part-of-speech tags and shape information as features. Then we hire
temporal (1D)... | computer science |
533 | Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations | cs.NE | We propose zoneout, a novel method for regularizing RNNs. At each timestep,
zoneout stochastically forces some hidden units to maintain their previous
values. Like dropout, zoneout uses random noise to train a pseudo-ensemble,
improving generalization. But by preserving instead of dropping hidden units,
gradient inform... | computer science |
534 | Stance Detection with Bidirectional Conditional Encoding | cs.CL | Stance detection is the task of classifying the attitude expressed in a text
towards a target such as Hillary Clinton to be "positive", negative" or
"neutral". Previous work has assumed that either the target is mentioned in the
text or that training data for every target is given. This paper considers the
more challen... | computer science |
535 | SMS Spam Filtering using Probabilistic Topic Modelling and Stacked
Denoising Autoencoder | cs.CL | In This paper we present a novel approach to spam filtering and demonstrate
its applicability with respect to SMS messages. Our approach requires minimum
features engineering and a small set of la- belled data samples. Features are
extracted using topic modelling based on latent Dirichlet allocation, and then
a compreh... | computer science |
536 | Bidirectional Recurrent Neural Networks for Medical Event Detection in
Electronic Health Records | cs.CL | Sequence labeling for extraction of medical events and their attributes from
unstructured text in Electronic Health Record (EHR) notes is a key step towards
semantic understanding of EHRs. It has important applications in health
informatics including pharmacovigilance and drug surveillance. The state of the
art supervi... | computer science |
537 | Sequence Training and Adaptation of Highway Deep Neural Networks | cs.CL | Highway deep neural network (HDNN) is a type of depth-gated feedforward
neural network, which has shown to be easier to train with more hidden layers
and also generalise better compared to conventional plain deep neural networks
(DNNs). Previously, we investigated a structured HDNN architecture for speech
recognition, ... | computer science |
538 | Recurrent Highway Networks | cs.LG | Many sequential processing tasks require complex nonlinear transition
functions from one step to the next. However, recurrent neural networks with
'deep' transition functions remain difficult to train, even when using Long
Short-Term Memory (LSTM) networks. We introduce a novel theoretical analysis of
recurrent network... | computer science |
539 | Towards cross-lingual distributed representations without parallel text
trained with adversarial autoencoders | cs.CL | Current approaches to learning vector representations of text that are
compatible between different languages usually require some amount of parallel
text, aligned at word, sentence or at least document level. We hypothesize
however, that different natural languages share enough semantic structure that
it should be pos... | computer science |
540 | Memory Visualization for Gated Recurrent Neural Networks in Speech
Recognition | cs.LG | Recurrent neural networks (RNNs) have shown clear superiority in sequence
modeling, particularly the ones with gated units, such as long short-term
memory (LSTM) and gated recurrent unit (GRU). However, the dynamic properties
behind the remarkable performance remain unclear in many applications, e.g.,
automatic speech ... | computer science |
541 | Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large
Vocabulary Speech Recognition | cs.CL | We present results that show it is possible to build a competitive, greatly
simplified, large vocabulary continuous speech recognition system with whole
words as acoustic units. We model the output vocabulary of about 100,000 words
directly using deep bi-directional LSTM RNNs with CTC loss. The model is
trained on 125,... | computer science |
542 | Unsupervised Pretraining for Sequence to Sequence Learning | cs.CL | This work presents a general unsupervised learning method to improve the
accuracy of sequence to sequence (seq2seq) models. In our method, the weights
of the encoder and decoder of a seq2seq model are initialized with the
pretrained weights of two language models and then fine-tuned with labeled
data. We apply this met... | computer science |
543 | Structured Attention Networks | cs.CL | Attention networks have proven to be an effective approach for embedding
categorical inference within a deep neural network. However, for many tasks we
may want to model richer structural dependencies without abandoning end-to-end
training. In this work, we experiment with incorporating richer structural
distributions,... | computer science |
544 | End-to-End Multi-View Networks for Text Classification | cs.CL | We propose a multi-view network for text classification. Our method
automatically creates various views of its input text, each taking the form of
soft attention weights that distribute the classifier's focus among a set of
base features. For a bag-of-words representation, each view focuses on a
different subset of the... | computer science |
545 | Differentiable Scheduled Sampling for Credit Assignment | cs.CL | We demonstrate that a continuous relaxation of the argmax operation can be
used to create a differentiable approximation to greedy decoding for
sequence-to-sequence (seq2seq) models. By incorporating this approximation into
the scheduled sampling training procedure (Bengio et al., 2015)--a well-known
technique for corr... | computer science |
546 | Phone-aware Neural Language Identification | cs.CL | Pure acoustic neural models, particularly the LSTM-RNN model, have shown
great potential in language identification (LID). However, the phonetic
information has been largely overlooked by most of existing neural LID models,
although this information has been used in the conventional phonetic LID
systems with a great su... | computer science |
547 | Detecting Off-topic Responses to Visual Prompts | cs.CL | Automated methods for essay scoring have made great progress in recent years,
achieving accuracies very close to human annotators. However, a known weakness
of such automated scorers is not taking into account the semantic relevance of
the submitted text. While there is existing work on detecting answer relevance
given... | computer science |
548 | Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation
Functions in Quasi-Recurrent Neural Networks | cs.CL | In this paper, we introduce a novel type of Rectified Linear Unit (ReLU),
called a Dual Rectified Linear Unit (DReLU). A DReLU, which comes with an
unbounded positive and negative image, can be used as a drop-in replacement for
a tanh activation function in the recurrent step of Quasi-Recurrent Neural
Networks (QRNNs) ... | computer science |
549 | Fidelity-Weighted Learning | cs.LG | Training deep neural networks requires many training samples, but in practice
training labels are expensive to obtain and may be of varying quality, as some
may be from trusted expert labelers while others might be from heuristics or
other sources of weak supervision such as crowd-sourcing. This creates a
fundamental q... | computer science |
550 | Feature Learning in Deep Neural Networks - Studies on Speech Recognition
Tasks | cs.LG | Recent studies have shown that deep neural networks (DNNs) perform
significantly better than shallow networks and Gaussian mixture models (GMMs)
on large vocabulary speech recognition tasks. In this paper, we argue that the
improved accuracy achieved by the DNNs is the result of their ability to
extract discriminative ... | computer science |
551 | Estimating Phoneme Class Conditional Probabilities from Raw Speech
Signal using Convolutional Neural Networks | cs.LG | In hybrid hidden Markov model/artificial neural networks (HMM/ANN) automatic
speech recognition (ASR) system, the phoneme class conditional probabilities
are estimated by first extracting acoustic features from the speech signal
based on prior knowledge such as, speech perception or/and speech production
knowledge, and... | computer science |
552 | Recursive Neural Networks Can Learn Logical Semantics | cs.CL | Tree-structured recursive neural networks (TreeRNNs) for sentence meaning
have been successful for many applications, but it remains an open question
whether the fixed-length representations that they learn can support tasks as
demanding as logical deduction. We pursue this question by evaluating whether
two such model... | computer science |
553 | A Re-ranking Model for Dependency Parser with Recursive Convolutional
Neural Network | cs.CL | In this work, we address the problem to model all the nodes (words or
phrases) in a dependency tree with the dense representations. We propose a
recursive convolutional neural network (RCNN) architecture to capture syntactic
and compositional-semantic representations of phrases and words in a dependency
tree. Different... | computer science |
554 | Deep Speaker Vectors for Semi Text-independent Speaker Verification | cs.CL | Recent research shows that deep neural networks (DNNs) can be used to extract
deep speaker vectors (d-vectors) that preserve speaker characteristics and can
be used in speaker verification. This new method has been tested on
text-dependent speaker verification tasks, and improvement was reported when
combined with the ... | computer science |
555 | Advances in Very Deep Convolutional Neural Networks for LVCSR | cs.CL | Very deep CNNs with small 3x3 kernels have recently been shown to achieve
very strong performance as acoustic models in hybrid NN-HMM speech recognition
systems. In this paper we investigate how to efficiently scale these models to
larger datasets. Specifically, we address the design choice of pooling and
padding along... | computer science |
556 | Learning Compact Recurrent Neural Networks | cs.LG | Recurrent neural networks (RNNs), including long short-term memory (LSTM)
RNNs, have produced state-of-the-art results on a variety of speech recognition
tasks. However, these models are often too large in size for deployment on
mobile devices with memory and latency constraints. In this work, we study
mechanisms for l... | computer science |
557 | Dependency Parsing with LSTMs: An Empirical Evaluation | cs.CL | We propose a transition-based dependency parser using Recurrent Neural
Networks with Long Short-Term Memory (LSTM) units. This extends the feedforward
neural network parser of Chen and Manning (2014) and enables modelling of
entire sequences of shift/reduce transition decisions. On the Google Web
Treebank, our LSTM par... | computer science |
558 | Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis
and Application to Information Retrieval | cs.CL | This paper develops a model that addresses sentence embedding, a hot topic in
current natural language processing research, using recurrent neural networks
with Long Short-Term Memory (LSTM) cells. Due to its ability to capture long
term memory, the LSTM-RNN accumulates increasingly richer information as it
goes throug... | computer science |
559 | Encoding Source Language with Convolutional Neural Network for Machine
Translation | cs.CL | The recently proposed neural network joint model (NNJM) (Devlin et al., 2014)
augments the n-gram target language model with a heuristically chosen source
context window, achieving state-of-the-art performance in SMT. In this paper,
we give a more systematic treatment by summarizing the relevant source
information thro... | computer science |
560 | Maximum a Posteriori Adaptation of Network Parameters in Deep Models | cs.LG | We present a Bayesian approach to adapting parameters of a well-trained
context-dependent, deep-neural-network, hidden Markov model (CD-DNN-HMM) to
improve automatic speech recognition performance. Given an abundance of DNN
parameters but with only a limited amount of data, the effectiveness of the
adapted DNN model ca... | computer science |
561 | Context-Dependent Translation Selection Using Convolutional Neural
Network | cs.CL | We propose a novel method for translation selection in statistical machine
translation, in which a convolutional neural network is employed to judge the
similarity between a phrase pair in two languages. The specifically designed
convolutional architecture encodes not only the semantic similarity of the
translation pai... | computer science |
562 | Convolutional Neural Network Architectures for Matching Natural Language
Sentences | cs.CL | Semantic matching is of central importance to many natural language tasks
\cite{bordes2014semantic,RetrievalQA}. A successful matching algorithm needs to
adequately model the internal structures of language objects and the
interaction between them. As a step toward this goal, we propose convolutional
neural network mod... | computer science |
563 | Long Short-Term Memory Over Tree Structures | cs.CL | The chain-structured long short-term memory (LSTM) has showed to be effective
in a wide range of problems such as speech recognition and machine translation.
In this paper, we propose to extend it to tree structures, in which a memory
cell can reflect the history memories of multiple child cells or multiple
descendant ... | computer science |
564 | Improving the Performance of Neural Machine Translation Involving
Morphologically Rich Languages | cs.CL | The advent of the attention mechanism in neural machine translation models
has improved the performance of machine translation systems by enabling
selective lookup into the source sentence. In this paper, the efficiencies of
translation using bidirectional encoder attention decoder models were studied
with respect to t... | computer science |
565 | A recurrent neural network without chaos | cs.NE | We introduce an exceptionally simple gated recurrent neural network (RNN)
that achieves performance comparable to well-known gated architectures, such as
LSTMs and GRUs, on the word-level language modeling task. We prove that our
model has simple, predicable and non-chaotic dynamics. This stands in stark
contrast to mo... | computer science |
566 | End-to-end Phoneme Sequence Recognition using Convolutional Neural
Networks | cs.LG | Most phoneme recognition state-of-the-art systems rely on a classical neural
network classifiers, fed with highly tuned features, such as MFCC or PLP
features. Recent advances in ``deep learning'' approaches questioned such
systems, but while some attempts were made with simpler features such as
spectrograms, state-of-... | computer science |
567 | A Deep Learning Approach to Data-driven Parameterizations for
Statistical Parametric Speech Synthesis | cs.CL | Nearly all Statistical Parametric Speech Synthesizers today use Mel Cepstral
coefficients as the vocal tract parameterization of the speech signal. Mel
Cepstral coefficients were never intended to work in a parametric speech
synthesis framework, but as yet, there has been little success in creating a
better parameteriz... | computer science |
568 | Addressing the Rare Word Problem in Neural Machine Translation | cs.CL | Neural Machine Translation (NMT) is a new approach to machine translation
that has shown promising results that are comparable to traditional approaches.
A significant weakness in conventional NMT systems is their inability to
correctly translate very rare words: end-to-end NMTs tend to have relatively
small vocabulari... | computer science |
569 | Investigating the Role of Prior Disambiguation in Deep-learning
Compositional Models of Meaning | cs.CL | This paper aims to explore the effect of prior disambiguation on neural
network- based compositional models, with the hope that better semantic
representations for text compounds can be produced. We disambiguate the input
word vectors before they are fed into a compositional deep net. A series of
evaluations shows the ... | computer science |
570 | Deep Speech: Scaling up end-to-end speech recognition | cs.CL | We present a state-of-the-art speech recognition system developed using
end-to-end deep learning. Our architecture is significantly simpler than
traditional speech systems, which rely on laboriously engineered processing
pipelines; these traditional systems also tend to perform poorly when used in
noisy environments. I... | computer science |
571 | Incremental Adaptation Strategies for Neural Network Language Models | cs.NE | It is today acknowledged that neural network language models outperform
backoff language models in applications like speech recognition or statistical
machine translation. However, training these models on large amounts of data
can take several days. We present efficient techniques to adapt a neural
network language mo... | computer science |
572 | Joint RNN-Based Greedy Parsing and Word Composition | cs.LG | This paper introduces a greedy parser based on neural networks, which
leverages a new compositional sub-tree representation. The greedy parser and
the compositional procedure are jointly trained, and tightly depends on
each-other. The composition procedure outputs a vector representation which
summarizes syntactically ... | computer science |
573 | Efficient Exact Gradient Update for training Deep Networks with Very
Large Sparse Targets | cs.NE | An important class of problems involves training deep neural networks with
sparse prediction targets of very high dimension D. These occur naturally in
e.g. neural language models or the learning of word-embeddings, often posed as
predicting the probability of next words among a vocabulary of size D (e.g. 200
000). Com... | computer science |
574 | Discriminative Neural Sentence Modeling by Tree-Based Convolution | cs.CL | This paper proposes a tree-based convolutional neural network (TBCNN) for
discriminative sentence modeling. Our models leverage either constituency trees
or dependency trees of sentences. The tree-based convolution process extracts
sentences' structural features, and these features are aggregated by max
pooling. Such a... | computer science |
575 | Self-Adaptive Hierarchical Sentence Model | cs.CL | The ability to accurately model a sentence at varying stages (e.g.,
word-phrase-sentence) plays a central role in natural language processing. As
an effort towards this goal we propose a self-adaptive hierarchical sentence
model (AdaSent). AdaSent effectively forms a hierarchy of representations from
words to phrases a... | computer science |
576 | Classifying Relations by Ranking with Convolutional Neural Networks | cs.CL | Relation classification is an important semantic processing task for which
state-ofthe-art systems still rely on costly handcrafted features. In this work
we tackle the relation classification task using a convolutional neural network
that performs classification by ranking (CR-CNN). We propose a new pairwise
ranking l... | computer science |
577 | Lexical Translation Model Using a Deep Neural Network Architecture | cs.CL | In this paper we combine the advantages of a model using global source
sentence contexts, the Discriminative Word Lexicon, and neural networks. By
using deep neural networks instead of the linear maximum entropy model in the
Discriminative Word Lexicon models, we are able to leverage dependencies
between different sour... | computer science |
578 | Visualizing and Understanding Recurrent Networks | cs.LG | Recurrent Neural Networks (RNNs), and specifically a variant with Long
Short-Term Memory (LSTM), are enjoying renewed interest as a result of
successful applications in a wide range of machine learning problems that
involve sequential data. However, while LSTMs provide exceptional results in
practice, the source of the... | computer science |
579 | A Multi-layered Acoustic Tokenizing Deep Neural Network (MAT-DNN) for
Unsupervised Discovery of Linguistic Units and Generation of High Quality
Features | cs.CL | This paper summarizes the work done by the authors for the Zero Resource
Speech Challenge organized in the technical program of Interspeech 2015. The
goal of the challenge is to discover linguistic units directly from unlabeled
speech data. The Multi-layered Acoustic Tokenizer (MAT) proposed in this work
automatically ... | computer science |
580 | Author Identification using Multi-headed Recurrent Neural Networks | cs.CL | Recurrent neural networks (RNNs) are very good at modelling the flow of text,
but typically need to be trained on a far larger corpus than is available for
the PAN 2015 Author Identification task. This paper describes a novel approach
where the output layer of a character-level RNN language model is split into
several ... | computer science |
581 | A Deep Memory-based Architecture for Sequence-to-Sequence Learning | cs.CL | We propose DEEPMEMORY, a novel deep architecture for sequence-to-sequence
learning, which performs the task through a series of nonlinear transformations
from the representation of the input sequence (e.g., a Chinese sentence) to the
final output sequence (e.g., translation to English). Inspired by the recently
propose... | computer science |
582 | Ask Me Anything: Dynamic Memory Networks for Natural Language Processing | cs.CL | Most tasks in natural language processing can be cast into question answering
(QA) problems over language input. We introduce the dynamic memory network
(DMN), a neural network architecture which processes input sequences and
questions, forms episodic memories, and generates relevant answers. Questions
trigger an itera... | computer science |
583 | Improved Deep Speaker Feature Learning for Text-Dependent Speaker
Recognition | cs.CL | A deep learning approach has been proposed recently to derive speaker
identifies (d-vector) by a deep neural network (DNN). This approach has been
applied to text-dependent speaker recognition tasks and shows reasonable
performance gains when combined with the conventional i-vector approach.
Although promising, the exi... | computer science |
584 | Grid Long Short-Term Memory | cs.NE | This paper introduces Grid Long Short-Term Memory, a network of LSTM cells
arranged in a multidimensional grid that can be applied to vectors, sequences
or higher dimensional data such as images. The network differs from existing
deep LSTM architectures in that the cells are connected between network layers
as well as ... | computer science |
585 | A Dependency-Based Neural Network for Relation Classification | cs.CL | Previous research on relation classification has verified the effectiveness
of using dependency shortest paths or subtrees. In this paper, we further
explore how to make full use of the combination of these dependency
information. We first propose a new structure, termed augmented dependency path
(ADP), which is compos... | computer science |
586 | PTE: Predictive Text Embedding through Large-scale Heterogeneous Text
Networks | cs.CL | Unsupervised text embedding methods, such as Skip-gram and Paragraph Vector,
have been attracting increasing attention due to their simplicity, scalability,
and effectiveness. However, comparing to sophisticated deep learning
architectures such as convolutional neural networks, these methods usually
yield inferior resu... | computer science |
587 | Relation Classification via Recurrent Neural Network | cs.CL | Deep learning has gained much success in sentence-level relation
classification. For example, convolutional neural networks (CNN) have delivered
competitive performance without much effort on feature engineering as the
conventional pattern-based methods. Thus a lot of works have been produced
based on CNN structures. H... | computer science |
588 | Learning from LDA using Deep Neural Networks | cs.LG | Latent Dirichlet Allocation (LDA) is a three-level hierarchical Bayesian
model for topic inference. In spite of its great success, inferring the latent
topic distribution with LDA is time-consuming. Motivated by the transfer
learning approach proposed by~\newcite{hinton2015distilling}, we present a
novel method that us... | computer science |
589 | Online Representation Learning in Recurrent Neural Language Models | cs.CL | We investigate an extension of continuous online learning in recurrent neural
network language models. The model keeps a separate vector representation of
the current unit of text being processed and adaptively adjusts it after each
prediction. The initial experiments give promising results, indicating that the
method ... | computer science |
590 | A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional
Neural Networks for Sentence Classification | cs.CL | Convolutional Neural Networks (CNNs) have recently achieved remarkably strong
performance on the practically important task of sentence classification (kim
2014, kalchbrenner 2014, johnson 2014). However, these models require
practitioners to specify an exact model architecture and set accompanying
hyperparameters, inc... | computer science |
591 | Prediction-Adaptation-Correction Recurrent Neural Networks for
Low-Resource Language Speech Recognition | cs.CL | In this paper, we investigate the use of prediction-adaptation-correction
recurrent neural networks (PAC-RNNs) for low-resource speech recognition. A
PAC-RNN is comprised of a pair of neural networks in which a {\it correction}
network uses auxiliary information given by a {\it prediction} network to help
estimate the ... | computer science |
592 | Generating Text with Deep Reinforcement Learning | cs.CL | We introduce a novel schema for sequence to sequence learning with a Deep
Q-Network (DQN), which decodes the output sequence iteratively. The aim here is
to enable the decoder to first tackle easier portions of the sequences, and
then turn to cope with difficult parts. Specifically, in each iteration, an
encoder-decode... | computer science |
593 | Detecting Interrogative Utterances with Recurrent Neural Networks | cs.CL | In this paper, we explore different neural network architectures that can
predict if a speaker of a given utterance is asking a question or making a
statement. We com- pare the outcomes of regularization methods that are
popularly used to train deep neural networks and study how different context
functions can affect t... | computer science |
594 | A Neural Transducer | cs.LG | Sequence-to-sequence models have achieved impressive results on various
tasks. However, they are unsuitable for tasks that require incremental
predictions to be made as more data arrives or tasks that have long input
sequences and output sequences. This is because they generate an output
sequence conditioned on an enti... | computer science |
595 | Skip-Thought Memory Networks | cs.NE | Question Answering (QA) is fundamental to natural language processing in that
most nlp problems can be phrased as QA (Kumar et al., 2015). Current weakly
supervised memory network models that have been proposed so far struggle at
answering questions that involve relations among multiple entities (such as
facebook's bAb... | computer science |
596 | Named Entity Recognition with Bidirectional LSTM-CNNs | cs.CL | Named entity recognition is a challenging task that has traditionally
required large amounts of knowledge in the form of feature engineering and
lexicons to achieve high performance. In this paper, we present a novel neural
network architecture that automatically detects word- and character-level
features using a hybri... | computer science |
597 | Generating News Headlines with Recurrent Neural Networks | cs.CL | We describe an application of an encoder-decoder recurrent neural network
with LSTM units and attention to generating headlines from the text of news
articles. We find that the model is quite effective at concisely paraphrasing
news articles. Furthermore, we study how the neural network decides which input
words to pay... | computer science |
598 | Words are not Equal: Graded Weighting Model for building Composite
Document Vectors | cs.CL | Despite the success of distributional semantics, composing phrases from word
vectors remains an important challenge. Several methods have been tried for
benchmark tasks such as sentiment classification, including word vector
averaging, matrix-vector approaches based on parsing, and on-the-fly learning
of paragraph vect... | computer science |
599 | Small-footprint Deep Neural Networks with Highway Connections for Speech
Recognition | cs.CL | For speech recognition, deep neural networks (DNNs) have significantly
improved the recognition accuracy in most of benchmark datasets and application
domains. However, compared to the conventional Gaussian mixture models,
DNN-based acoustic models usually have much larger number of model parameters,
making it challeng... | computer science |
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