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1,800 | Structural Correspondence Learning for Cross-lingual Sentiment
Classification with One-to-many Mappings | cs.LG | Structural correspondence learning (SCL) is an effective method for
cross-lingual sentiment classification. This approach uses unlabeled documents
along with a word translation oracle to automatically induce task specific,
cross-lingual correspondences. It transfers knowledge through identifying
important features, i.e... | computer science |
1,801 | Learning a Natural Language Interface with Neural Programmer | cs.CL | Learning a natural language interface for database tables is a challenging
task that involves deep language understanding and multi-step reasoning. The
task is often approached by mapping natural language queries to logical forms
or programs that provide the desired response when executed on the database. To
our knowle... | computer science |
1,802 | AutoMOS: Learning a non-intrusive assessor of naturalness-of-speech | cs.CL | Developers of text-to-speech synthesizers (TTS) often make use of human
raters to assess the quality of synthesized speech. We demonstrate that we can
model human raters' mean opinion scores (MOS) of synthesized speech using a
deep recurrent neural network whose inputs consist solely of a raw waveform.
Our best models ... | computer science |
1,803 | Identity-sensitive Word Embedding through Heterogeneous Networks | cs.CL | Most existing word embedding approaches do not distinguish the same words in
different contexts, therefore ignoring their contextual meanings. As a result,
the learned embeddings of these words are usually a mixture of multiple
meanings. In this paper, we acknowledge multiple identities of the same word in
different co... | computer science |
1,804 | Neural Probabilistic Model for Non-projective MST Parsing | cs.CL | In this paper, we propose a probabilistic parsing model, which defines a
proper conditional probability distribution over non-projective dependency
trees for a given sentence, using neural representations as inputs. The neural
network architecture is based on bi-directional LSTM-CNNs which benefits from
both word- and ... | computer science |
1,805 | Towards End-to-End Speech Recognition with Deep Convolutional Neural
Networks | cs.CL | Convolutional Neural Networks (CNNs) are effective models for reducing
spectral variations and modeling spectral correlations in acoustic features for
automatic speech recognition (ASR). Hybrid speech recognition systems
incorporating CNNs with Hidden Markov Models/Gaussian Mixture Models
(HMMs/GMMs) have achieved the ... | computer science |
1,806 | Kernel Approximation Methods for Speech Recognition | stat.ML | We study large-scale kernel methods for acoustic modeling in speech
recognition and compare their performance to deep neural networks (DNNs). We
perform experiments on four speech recognition datasets, including the TIMIT
and Broadcast News benchmark tasks, and compare these two types of models on
frame-level performan... | computer science |
1,807 | dna2vec: Consistent vector representations of variable-length k-mers | cs.CL | One of the ubiquitous representation of long DNA sequence is dividing it into
shorter k-mer components. Unfortunately, the straightforward vector encoding of
k-mer as a one-hot vector is vulnerable to the curse of dimensionality. Worse
yet, the distance between any pair of one-hot vectors is equidistant. This is
partic... | computer science |
1,808 | Predicting Auction Price of Vehicle License Plate with Deep Recurrent
Neural Network | cs.CL | In Chinese societies, superstition is of paramount importance, and vehicle
license plates with desirable numbers can fetch very high prices in auctions.
Unlike other valuable items, license plates are not allocated an estimated
price before auction. I propose that the task of predicting plate prices can be
viewed as a ... | computer science |
1,809 | Filtering Tweets for Social Unrest | cs.CL | Since the events of the Arab Spring, there has been increased interest in
using social media to anticipate social unrest. While efforts have been made
toward automated unrest prediction, we focus on filtering the vast volume of
tweets to identify tweets relevant to unrest, which can be provided to
downstream users for ... | computer science |
1,810 | Stability of Topic Modeling via Matrix Factorization | cs.IR | Topic models can provide us with an insight into the underlying latent
structure of a large corpus of documents. A range of methods have been proposed
in the literature, including probabilistic topic models and techniques based on
matrix factorization. However, in both cases, standard implementations rely on
stochastic... | computer science |
1,811 | Using Graphs of Classifiers to Impose Declarative Constraints on
Semi-supervised Learning | cs.LG | We propose a general approach to modeling semi-supervised learning (SSL)
algorithms. Specifically, we present a declarative language for modeling both
traditional supervised classification tasks and many SSL heuristics, including
both well-known heuristics such as co-training and novel domain-specific
heuristics. In ad... | computer science |
1,812 | Neural Machine Translation and Sequence-to-sequence Models: A Tutorial | cs.CL | This tutorial introduces a new and powerful set of techniques variously
called "neural machine translation" or "neural sequence-to-sequence models".
These techniques have been used in a number of tasks regarding the handling of
human language, and can be a powerful tool in the toolbox of anyone who wants
to model seque... | computer science |
1,813 | Generative and Discriminative Text Classification with Recurrent Neural
Networks | stat.ML | We empirically characterize the performance of discriminative and generative
LSTM models for text classification. We find that although RNN-based generative
models are more powerful than their bag-of-words ancestors (e.g., they account
for conditional dependencies across words in a document), they have higher
asymptoti... | computer science |
1,814 | Sequence-to-Sequence Models Can Directly Translate Foreign Speech | cs.CL | We present a recurrent encoder-decoder deep neural network architecture that
directly translates speech in one language into text in another. The model does
not explicitly transcribe the speech into text in the source language, nor does
it require supervision from the ground truth source language transcription
during t... | computer science |
1,815 | Word Embeddings via Tensor Factorization | stat.ML | Most popular word embedding techniques involve implicit or explicit
factorization of a word co-occurrence based matrix into low rank factors. In
this paper, we aim to generalize this trend by using numerical methods to
factor higher-order word co-occurrence based arrays, or \textit{tensors}. We
present four word embedd... | computer science |
1,816 | Persian Wordnet Construction using Supervised Learning | cs.CL | This paper presents an automated supervised method for Persian wordnet
construction. Using a Persian corpus and a bi-lingual dictionary, the initial
links between Persian words and Princeton WordNet synsets have been generated.
These links will be discriminated later as correct or incorrect by employing
seven features ... | computer science |
1,817 | Learning Latent Representations for Speech Generation and Transformation | cs.CL | An ability to model a generative process and learn a latent representation
for speech in an unsupervised fashion will be crucial to process vast
quantities of unlabelled speech data. Recently, deep probabilistic generative
models such as Variational Autoencoders (VAEs) have achieved tremendous success
in modeling natur... | computer science |
1,818 | Does Neural Machine Translation Benefit from Larger Context? | stat.ML | We propose a neural machine translation architecture that models the
surrounding text in addition to the source sentence. These models lead to
better performance, both in terms of general translation quality and pronoun
prediction, when trained on small corpora, although this improvement largely
disappears when trained... | computer science |
1,819 | Bandit Structured Prediction for Neural Sequence-to-Sequence Learning | stat.ML | Bandit structured prediction describes a stochastic optimization framework
where learning is performed from partial feedback. This feedback is received in
the form of a task loss evaluation to a predicted output structure, without
having access to gold standard structures. We advance this framework by lifting
linear ba... | computer science |
1,820 | Adversarial Neural Machine Translation | cs.CL | In this paper, we study a new learning paradigm for Neural Machine
Translation (NMT). Instead of maximizing the likelihood of the human
translation as in previous works, we minimize the distinction between human
translation and the translation given by an NMT model. To achieve this goal,
inspired by the recent success ... | computer science |
1,821 | Using Global Constraints and Reranking to Improve Cognates Detection | cs.CL | Global constraints and reranking have not been used in cognates detection
research to date. We propose methods for using global constraints by performing
rescoring of the score matrices produced by state of the art cognates detection
systems. Using global constraints to perform rescoring is complementary to
state of th... | computer science |
1,822 | Learning Representations of Emotional Speech with Deep Convolutional
Generative Adversarial Networks | cs.CL | Automatically assessing emotional valence in human speech has historically
been a difficult task for machine learning algorithms. The subtle changes in
the voice of the speaker that are indicative of positive or negative emotional
states are often "overshadowed" by voice characteristics relating to emotional
intensity ... | computer science |
1,823 | Max-Pooling Loss Training of Long Short-Term Memory Networks for
Small-Footprint Keyword Spotting | cs.CL | We propose a max-pooling based loss function for training Long Short-Term
Memory (LSTM) networks for small-footprint keyword spotting (KWS), with low
CPU, memory, and latency requirements. The max-pooling loss training can be
further guided by initializing with a cross-entropy loss trained network. A
posterior smoothin... | computer science |
1,824 | DeepDeath: Learning to Predict the Underlying Cause of Death with Big
Data | cs.CL | Multiple cause-of-death data provides a valuable source of information that
can be used to enhance health standards by predicting health related
trajectories in societies with large populations. These data are often
available in large quantities across U.S. states and require Big Data
techniques to uncover complex hidd... | computer science |
1,825 | Learning a bidirectional mapping between human whole-body motion and
natural language using deep recurrent neural networks | cs.LG | Linking human whole-body motion and natural language is of great interest for
the generation of semantic representations of observed human behaviors as well
as for the generation of robot behaviors based on natural language input. While
there has been a large body of research in this area, most approaches that
exist to... | computer science |
1,826 | Softmax Q-Distribution Estimation for Structured Prediction: A
Theoretical Interpretation for RAML | cs.LG | Reward augmented maximum likelihood (RAML), a simple and effective learning
framework to directly optimize towards the reward function in structured
prediction tasks, has led to a number of impressive empirical successes. RAML
incorporates task-specific reward by performing maximum-likelihood updates on
candidate outpu... | computer science |
1,827 | A Regularized Framework for Sparse and Structured Neural Attention | stat.ML | Modern neural networks are often augmented with an attention mechanism, which
tells the network where to focus within the input. We propose in this paper a
new framework for sparse and structured attention, building upon a smoothed max
operator. We show that the gradient of this operator defines a mapping from
real val... | computer science |
1,828 | On-the-fly Operation Batching in Dynamic Computation Graphs | cs.LG | Dynamic neural network toolkits such as PyTorch, DyNet, and Chainer offer
more flexibility for implementing models that cope with data of varying
dimensions and structure, relative to toolkits that operate on statically
declared computations (e.g., TensorFlow, CNTK, and Theano). However, existing
toolkits - both static... | computer science |
1,829 | Gated Recurrent Neural Tensor Network | cs.LG | Recurrent Neural Networks (RNNs), which are a powerful scheme for modeling
temporal and sequential data need to capture long-term dependencies on datasets
and represent them in hidden layers with a powerful model to capture more
information from inputs. For modeling long-term dependencies in a dataset, the
gating mecha... | computer science |
1,830 | Context encoders as a simple but powerful extension of word2vec | stat.ML | With a simple architecture and the ability to learn meaningful word
embeddings efficiently from texts containing billions of words, word2vec
remains one of the most popular neural language models used today. However, as
only a single embedding is learned for every word in the vocabulary, the model
fails to optimally re... | computer science |
1,831 | Adversarial Feature Matching for Text Generation | stat.ML | The Generative Adversarial Network (GAN) has achieved great success in
generating realistic (real-valued) synthetic data. However, convergence issues
and difficulties dealing with discrete data hinder the applicability of GAN to
text. We propose a framework for generating realistic text via adversarial
training. We emp... | computer science |
1,832 | Topic supervised non-negative matrix factorization | cs.CL | Topic models have been extensively used to organize and interpret the
contents of large, unstructured corpora of text documents. Although topic
models often perform well on traditional training vs. test set evaluations, it
is often the case that the results of a topic model do not align with human
interpretation. This ... | computer science |
1,833 | An online sequence-to-sequence model for noisy speech recognition | cs.CL | Generative models have long been the dominant approach for speech
recognition. The success of these models however relies on the use of
sophisticated recipes and complicated machinery that is not easily accessible
to non-practitioners. Recent innovations in Deep Learning have given rise to an
alternative - discriminati... | computer science |
1,834 | Grounded Language Learning in a Simulated 3D World | cs.CL | We are increasingly surrounded by artificially intelligent technology that
takes decisions and executes actions on our behalf. This creates a pressing
need for general means to communicate with, instruct and guide artificial
agents, with human language the most compelling means for such communication.
To achieve this i... | computer science |
1,835 | SAM: Semantic Attribute Modulation for Language Modeling and Style
Variation | cs.CL | This paper presents a Semantic Attribute Modulation (SAM) for language
modeling and style variation. The semantic attribute modulation includes
various document attributes, such as titles, authors, and document categories.
We consider two types of attributes, (title attributes and category
attributes), and a flexible a... | computer science |
1,836 | Efficient Correlated Topic Modeling with Topic Embedding | cs.LG | Correlated topic modeling has been limited to small model and problem sizes
due to their high computational cost and poor scaling. In this paper, we
propose a new model which learns compact topic embeddings and captures topic
correlations through the closeness between the topic vectors. Our method
enables efficient inf... | computer science |
1,837 | Language modeling with Neural trans-dimensional random fields | cs.CL | Trans-dimensional random field language models (TRF LMs) have recently been
introduced, where sentences are modeled as a collection of random fields. The
TRF approach has been shown to have the advantages of being computationally
more efficient in inference than LSTM LMs with close performance and being able
to flexibl... | computer science |
1,838 | Counterfactual Learning from Bandit Feedback under Deterministic
Logging: A Case Study in Statistical Machine Translation | stat.ML | The goal of counterfactual learning for statistical machine translation (SMT)
is to optimize a target SMT system from logged data that consist of user
feedback to translations that were predicted by another, historic SMT system. A
challenge arises by the fact that risk-averse commercial SMT systems
deterministically lo... | computer science |
1,839 | Bayesian Sparsification of Recurrent Neural Networks | stat.ML | Recurrent neural networks show state-of-the-art results in many text analysis
tasks but often require a lot of memory to store their weights. Recently
proposed Sparse Variational Dropout eliminates the majority of the weights in a
feed-forward neural network without significant loss of quality. We apply this
technique ... | computer science |
1,840 | Retrofitting Distributional Embeddings to Knowledge Graphs with
Functional Relations | stat.ML | Knowledge graphs are a versatile framework to encode richly structured data
relationships, but it not always apparent how to combine these with existing
entity representations. Methods for retrofitting pre-trained entity
representations to the structure of a knowledge graph typically assume that
entities are embedded i... | computer science |
1,841 | SenGen: Sentence Generating Neural Variational Topic Model | cs.CL | We present a new topic model that generates documents by sampling a topic for
one whole sentence at a time, and generating the words in the sentence using an
RNN decoder that is conditioned on the topic of the sentence. We argue that
this novel formalism will help us not only visualize and model the topical
discourse s... | computer science |
1,842 | Communication-Free Parallel Supervised Topic Models | cs.LG | Embarrassingly (communication-free) parallel Markov chain Monte Carlo (MCMC)
methods are commonly used in learning graphical models. However, MCMC cannot be
directly applied in learning topic models because of the quasi-ergodicity
problem caused by multimodal distribution of topics. In this paper, we develop
an embarra... | computer science |
1,843 | Sentiment Analysis by Joint Learning of Word Embeddings and Classifier | cs.CL | Word embeddings are representations of individual words of a text document in
a vector space and they are often use- ful for performing natural language pro-
cessing tasks. Current state of the art al- gorithms for learning word
embeddings learn vector representations from large corpora of text documents in
an unsu- pe... | computer science |
1,844 | Deconvolutional Paragraph Representation Learning | cs.CL | Learning latent representations from long text sequences is an important
first step in many natural language processing applications. Recurrent Neural
Networks (RNNs) have become a cornerstone for this challenging task. However,
the quality of sentences during RNN-based decoding (reconstruction) decreases
with the leng... | computer science |
1,845 | Leveraging Deep Neural Network Activation Entropy to cope with Unseen
Data in Speech Recognition | cs.LG | Unseen data conditions can inflict serious performance degradation on systems
relying on supervised machine learning algorithms. Because data can often be
unseen, and because traditional machine learning algorithms are trained in a
supervised manner, unsupervised adaptation techniques must be used to adapt the
model to... | computer science |
1,846 | Deconvolutional Latent-Variable Model for Text Sequence Matching | cs.CL | A latent-variable model is introduced for text matching, inferring sentence
representations by jointly optimizing generative and discriminative objectives.
To alleviate typical optimization challenges in latent-variable models for
text, we employ deconvolutional networks as the sequence decoder (generator),
providing l... | computer science |
1,847 | Adaptive Convolutional Filter Generation for Natural Language
Understanding | cs.CL | Convolutional neural networks (CNNs) have recently emerged as a popular
building block for natural language processing (NLP). Despite their success,
most existing CNN models employed in NLP are not expressive enough, in the
sense that all input sentences share the same learned (and static) set of
filters. Motivated by ... | computer science |
1,848 | Structured Embedding Models for Grouped Data | cs.CL | Word embeddings are a powerful approach for analyzing language, and
exponential family embeddings (EFE) extend them to other types of data. Here we
develop structured exponential family embeddings (S-EFE), a method for
discovering embeddings that vary across related groups of data. We study how
the word usage of U.S. C... | computer science |
1,849 | Wembedder: Wikidata entity embedding web service | stat.ML | I present a web service for querying an embedding of entities in the Wikidata
knowledge graph. The embedding is trained on the Wikidata dump using Gensim's
Word2Vec implementation and a simple graph walk. A REST API is implemented.
Together with the Wikidata API the web service exposes a multilingual resource
for over ... | computer science |
1,850 | Scaling Text with the Class Affinity Model | stat.ML | Probabilistic methods for classifying text form a rich tradition in machine
learning and natural language processing. For many important problems, however,
class prediction is uninteresting because the class is known, and instead the
focus shifts to estimating latent quantities related to the text, such as
affect or id... | computer science |
1,851 | Trace norm regularization and faster inference for embedded speech
recognition RNNs | cs.LG | We propose and evaluate new techniques for compressing and speeding up dense
matrix multiplications as found in the fully connected and recurrent layers of
neural networks for embedded large vocabulary continuous speech recognition
(LVCSR). For compression, we introduce and study a trace norm regularization
technique f... | computer science |
1,852 | Improving Negative Sampling for Word Representation using Self-embedded
Features | cs.LG | Although the word-popularity based negative sampler has shown superb
performance in the skip-gram model, the theoretical motivation behind
oversampling popular (non-observed) words as negative samples is still not well
understood. In this paper, we start from an investigation of the gradient
vanishing issue in the skip... | computer science |
1,853 | One-shot and few-shot learning of word embeddings | cs.CL | Standard deep learning systems require thousands or millions of examples to
learn a concept, and cannot integrate new concepts easily. By contrast, humans
have an incredible ability to do one-shot or few-shot learning. For instance,
from just hearing a word used in a sentence, humans can infer a great deal
about it, by... | computer science |
1,854 | Generalized End-to-End Loss for Speaker Verification | eess.AS | In this paper, we propose a new loss function called generalized end-to-end
(GE2E) loss, which makes the training of speaker verification models more
efficient than our previous tuple-based end-to-end (TE2E) loss function. Unlike
TE2E, the GE2E loss function updates the network in a way that emphasizes
examples that ar... | computer science |
1,855 | Reinforcement Learning of Speech Recognition System Based on Policy
Gradient and Hypothesis Selection | cs.CL | Speech recognition systems have achieved high recognition performance for
several tasks. However, the performance of such systems is dependent on the
tremendously costly development work of preparing vast amounts of task-matched
transcribed speech data for supervised training. The key problem here is the
cost of transc... | computer science |
1,856 | Bayesian Paragraph Vectors | cs.CL | Word2vec (Mikolov et al., 2013) has proven to be successful in natural
language processing by capturing the semantic relationships between different
words. Built on top of single-word embeddings, paragraph vectors (Le and
Mikolov, 2014) find fixed-length representations for pieces of text with
arbitrary lengths, such a... | computer science |
1,857 | Unsupervised Document Embedding With CNNs | cs.CL | We propose a new model for unsupervised document embedding. Leading existing
approaches either require complex inference or use recurrent neural networks
(RNN) that are difficult to parallelize. We take a different route and develop
a convolutional neural network (CNN) embedding model. Our CNN architecture is
fully par... | computer science |
1,858 | On Extending Neural Networks with Loss Ensembles for Text Classification | cs.CL | Ensemble techniques are powerful approaches that combine several weak
learners to build a stronger one. As a meta learning framework, ensemble
techniques can easily be applied to many machine learning techniques. In this
paper we propose a neural network extended with an ensemble loss function for
text classification. ... | computer science |
1,859 | Lattice Rescoring Strategies for Long Short Term Memory Language Models
in Speech Recognition | stat.ML | Recurrent neural network (RNN) language models (LMs) and Long Short Term
Memory (LSTM) LMs, a variant of RNN LMs, have been shown to outperform
traditional N-gram LMs on speech recognition tasks. However, these models are
computationally more expensive than N-gram LMs for decoding, and thus,
challenging to integrate in... | computer science |
1,860 | Counterfactual Learning for Machine Translation: Degeneracies and
Solutions | stat.ML | Counterfactual learning is a natural scenario to improve web-based machine
translation services by offline learning from feedback logged during user
interactions. In order to avoid the risk of showing inferior translations to
users, in such scenarios mostly exploration-free deterministic logging policies
are in place. ... | computer science |
1,861 | Continuous Semantic Topic Embedding Model Using Variational Autoencoder | stat.ML | This paper proposes the continuous semantic topic embedding model (CSTEM)
which finds latent topic variables in documents using continuous semantic
distance function between the topics and the words by means of the variational
autoencoder(VAE). The semantic distance could be represented by any symmetric
bell-shaped geo... | computer science |
1,862 | Neural Text Generation: A Practical Guide | cs.CL | Deep learning methods have recently achieved great empirical success on
machine translation, dialogue response generation, summarization, and other
text generation tasks. At a high level, the technique has been to train
end-to-end neural network models consisting of an encoder model to produce a
hidden representation o... | computer science |
1,863 | Generative Interest Estimation for Document Recommendations | cs.IR | Learning distributed representations of documents has pushed the
state-of-the-art in several natural language processing tasks and was
successfully applied to the field of recommender systems recently. In this
paper, we propose a novel content-based recommender system based on learned
representations and a generative m... | computer science |
1,864 | Sockeye: A Toolkit for Neural Machine Translation | cs.CL | We describe Sockeye (version 1.12), an open-source sequence-to-sequence
toolkit for Neural Machine Translation (NMT). Sockeye is a production-ready
framework for training and applying models as well as an experimental platform
for researchers. Written in Python and built on MXNet, the toolkit offers
scalable training a... | computer science |
1,865 | A Flexible Approach to Automated RNN Architecture Generation | cs.CL | The process of designing neural architectures requires expert knowledge and
extensive trial and error. While automated architecture search may simplify
these requirements, the recurrent neural network (RNN) architectures generated
by existing methods are limited in both flexibility and components. We propose
a domain-s... | computer science |
1,866 | Character-level Recurrent Neural Networks in Practice: Comparing
Training and Sampling Schemes | cs.LG | Recurrent neural networks are nowadays successfully used in an abundance of
applications, going from text, speech and image processing to recommender
systems. Backpropagation through time is the algorithm that is commonly used to
train these networks on specific tasks. Many deep learning frameworks have
their own imple... | computer science |
1,867 | Stochastic Learning of Nonstationary Kernels for Natural Language
Modeling | cs.CL | Natural language processing often involves computations with semantic or
syntactic graphs to facilitate sophisticated reasoning based on structural
relationships. While convolution kernels provide a powerful tool for comparing
graph structure based on node (word) level relationships, they are difficult to
customize and... | computer science |
1,868 | Predicting Movie Genres Based on Plot Summaries | cs.CL | This project explores several Machine Learning methods to predict movie
genres based on plot summaries. Naive Bayes, Word2Vec+XGBoost and Recurrent
Neural Networks are used for text classification, while K-binary
transformation, rank method and probabilistic classification with learned
probability threshold are employe... | computer science |
1,869 | Topic Modeling on Health Journals with Regularized Variational Inference | cs.CL | Topic modeling enables exploration and compact representation of a corpus.
The CaringBridge (CB) dataset is a massive collection of journals written by
patients and caregivers during a health crisis. Topic modeling on the CB
dataset, however, is challenging due to the asynchronous nature of multiple
authors writing abo... | computer science |
1,870 | Beyond Word Importance: Contextual Decomposition to Extract Interactions
from LSTMs | cs.CL | The driving force behind the recent success of LSTMs has been their ability
to learn complex and non-linear relationships. Consequently, our inability to
describe these relationships has led to LSTMs being characterized as black
boxes. To this end, we introduce contextual decomposition (CD), an
interpretation algorithm... | computer science |
1,871 | Fine-tuned Language Models for Text Classification | cs.CL | Transfer learning has revolutionized computer vision, but existing approaches
in NLP still require task-specific modifications and training from scratch. We
propose Fine-tuned Language Models (FitLaM), an effective transfer learning
method that can be applied to any task in NLP, and introduce techniques that
are key fo... | computer science |
1,872 | What Does a TextCNN Learn? | stat.ML | TextCNN, the convolutional neural network for text, is a useful deep learning
algorithm for sentence classification tasks such as sentiment analysis and
question classification. However, neural networks have long been known as black
boxes because interpreting them is a challenging task. Researchers have
developed sever... | computer science |
1,873 | Support Vector Machine Active Learning Algorithms with
Query-by-Committee versus Closest-to-Hyperplane Selection | cs.LG | This paper investigates and evaluates support vector machine active learning
algorithms for use with imbalanced datasets, which commonly arise in many
applications such as information extraction applications. Algorithms based on
closest-to-hyperplane selection and query-by-committee selection are combined
with methods ... | computer science |
1,874 | Deep Learning for Sentiment Analysis : A Survey | cs.CL | Deep learning has emerged as a powerful machine learning technique that
learns multiple layers of representations or features of the data and produces
state-of-the-art prediction results. Along with the success of deep learning in
many other application domains, deep learning is also popularly used in
sentiment analysi... | computer science |
1,875 | Impact of Batch Size on Stopping Active Learning for Text Classification | cs.LG | When using active learning, smaller batch sizes are typically more efficient
from a learning efficiency perspective. However, in practice due to speed and
human annotator considerations, the use of larger batch sizes is necessary.
While past work has shown that larger batch sizes decrease learning efficiency
from a lea... | computer science |
1,876 | Classifying medical notes into standard disease codes using Machine
Learning | cs.LG | We investigate the automatic classification of patient discharge notes into
standard disease labels. We find that Convolutional Neural Networks with
Attention outperform previous algorithms used in this task, and suggest further
areas for improvement. | computer science |
1,877 | Semi-Amortized Variational Autoencoders | stat.ML | Amortized variational inference (AVI) replaces instance-specific local
inference with a global inference network. While AVI has enabled efficient
training of deep generative models such as variational autoencoders (VAE),
recent empirical work suggests that inference networks can produce suboptimal
variational parameter... | computer science |
1,878 | SparseMAP: Differentiable Sparse Structured Inference | stat.ML | Structured prediction requires searching over a combinatorial number of
structures. To tackle it, we introduce SparseMAP, a new method for sparse
structured inference, together with corresponding loss functions. SparseMAP
inference is able to automatically select only a few global structures: it is
situated between MAP... | computer science |
1,879 | Multinomial Adversarial Networks for Multi-Domain Text Classification | cs.CL | Many text classification tasks are known to be highly domain-dependent.
Unfortunately, the availability of training data can vary drastically across
domains. Worse still, for some domains there may not be any annotated data at
all. In this work, we propose a multinomial adversarial network (MAN) to tackle
the text clas... | computer science |
1,880 | Explainable Prediction of Medical Codes from Clinical Text | cs.CL | Clinical notes are text documents that are created by clinicians for each
patient encounter. They are typically accompanied by medical codes, which
describe the diagnosis and treatment. Annotating these codes is labor intensive
and error prone; furthermore, the connection between the codes and the text is
not annotated... | computer science |
1,881 | Improving Mild Cognitive Impairment Prediction via Reinforcement
Learning and Dialogue Simulation | cs.LG | Mild cognitive impairment (MCI) is a prodromal phase in the progression from
normal aging to dementia, especially Alzheimers disease. Even though there is
mild cognitive decline in MCI patients, they have normal overall cognition and
thus is challenging to distinguish from normal aging. Using transcribed data
obtained ... | computer science |
1,882 | Learning Hidden Markov Models from Pairwise Co-occurrences with
Applications to Topic Modeling | cs.CL | We present a new algorithm for identifying the transition and emission
probabilities of a hidden Markov model (HMM) from the emitted data.
Expectation-maximization becomes computationally prohibitive for long
observation records, which are often required for identification. The new
algorithm is particularly suitable fo... | computer science |
1,883 | Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative
Refinement | cs.LG | We propose a conditional non-autoregressive neural sequence model based on
iterative refinement. The proposed model is designed based on the principles of
latent variable models and denoising autoencoders, and is generally applicable
to any sequence generation task. We extensively evaluate the proposed model on
machine... | computer science |
1,884 | One Single Deep Bidirectional LSTM Network for Word Sense Disambiguation
of Text Data | cs.LG | Due to recent technical and scientific advances, we have a wealth of
information hidden in unstructured text data such as offline/online narratives,
research articles, and clinical reports. To mine these data properly,
attributable to their innate ambiguity, a Word Sense Disambiguation (WSD)
algorithm can avoid numbers... | computer science |
1,885 | Gaussian meta-embeddings for efficient scoring of a heavy-tailed PLDA
model | stat.ML | Embeddings in machine learning are low-dimensional representations of complex
input patterns, with the property that simple geometric operations like
Euclidean distances and dot products can be used for classification and
comparison tasks. The proposed meta-embeddings are special embeddings that live
in more general in... | computer science |
1,886 | Learning Approximate Inference Networks for Structured Prediction | cs.CL | Structured prediction energy networks (SPENs; Belanger & McCallum 2016) use
neural network architectures to define energy functions that can capture
arbitrary dependencies among parts of structured outputs. Prior work used
gradient descent for inference, relaxing the structured output to a set of
continuous variables a... | computer science |
1,887 | Tensor2Tensor for Neural Machine Translation | cs.LG | Tensor2Tensor is a library for deep learning models that is well-suited for
neural machine translation and includes the reference implementation of the
state-of-the-art Transformer model. | computer science |
1,888 | Learning Eligibility in Clinical Cancer Trials using Deep Neural
Networks | cs.CL | Interventional clinical cancer trials are generally too restrictive and
cancer patients are often excluded from them on the basis of comorbidity, past
or concomitant treatments and the fact that they are over a certain age. The
efficacy and safety of new treatments for patients with these characteristics
are not, there... | computer science |
1,889 | Hello Edge: Keyword Spotting on Microcontrollers | cs.SD | Keyword spotting (KWS) is a critical component for enabling speech based user
interactions on smart devices. It requires real-time response and high accuracy
for good user experience. Recently, neural networks have become an attractive
choice for KWS architecture because of their superior accuracy compared to
tradition... | computer science |
1,890 | Deep Fragment Embeddings for Bidirectional Image Sentence Mapping | cs.CV | We introduce a model for bidirectional retrieval of images and sentences
through a multi-modal embedding of visual and natural language data. Unlike
previous models that directly map images or sentences into a common embedding
space, our model works on a finer level and embeds fragments of images
(objects) and fragment... | computer science |
1,891 | Are You Talking to a Machine? Dataset and Methods for Multilingual Image
Question Answering | cs.CV | In this paper, we present the mQA model, which is able to answer questions
about the content of an image. The answer can be a sentence, a phrase or a
single word. Our model contains four components: a Long Short-Term Memory
(LSTM) to extract the question representation, a Convolutional Neural Network
(CNN) to extract t... | computer science |
1,892 | Resolving Language and Vision Ambiguities Together: Joint Segmentation &
Prepositional Attachment Resolution in Captioned Scenes | cs.CV | We present an approach to simultaneously perform semantic segmentation and
prepositional phrase attachment resolution for captioned images. Some
ambiguities in language cannot be resolved without simultaneously reasoning
about an associated image. If we consider the sentence "I shot an elephant in
my pajamas", looking ... | computer science |
1,893 | Video Description using Bidirectional Recurrent Neural Networks | cs.CV | Although traditionally used in the machine translation field, the
encoder-decoder framework has been recently applied for the generation of video
and image descriptions. The combination of Convolutional and Recurrent Neural
Networks in these models has proven to outperform the previous state of the
art, obtaining more ... | computer science |
1,894 | CLEVR: A Diagnostic Dataset for Compositional Language and Elementary
Visual Reasoning | cs.CV | When building artificial intelligence systems that can reason and answer
questions about visual data, we need diagnostic tests to analyze our progress
and discover shortcomings. Existing benchmarks for visual question answering
can help, but have strong biases that models can exploit to correctly answer
questions witho... | computer science |
1,895 | Image-Text Multi-Modal Representation Learning by Adversarial
Backpropagation | cs.CV | We present novel method for image-text multi-modal representation learning.
In our knowledge, this work is the first approach of applying adversarial
learning concept to multi-modal learning and not exploiting image-text pair
information to learn multi-modal feature. We only use category information in
contrast with mo... | computer science |
1,896 | Speech Recognition Front End Without Information Loss | cs.CL | Speech representation and modelling in high-dimensional spaces of acoustic
waveforms, or a linear transformation thereof, is investigated with the aim of
improving the robustness of automatic speech recognition to additive noise. The
motivation behind this approach is twofold: (i) the information in acoustic
waveforms ... | computer science |
1,897 | Explain Images with Multimodal Recurrent Neural Networks | cs.CV | In this paper, we present a multimodal Recurrent Neural Network (m-RNN) model
for generating novel sentence descriptions to explain the content of images. It
directly models the probability distribution of generating a word given
previous words and the image. Image descriptions are generated by sampling from
this distr... | computer science |
1,898 | Unifying Visual-Semantic Embeddings with Multimodal Neural Language
Models | cs.LG | Inspired by recent advances in multimodal learning and machine translation,
we introduce an encoder-decoder pipeline that learns (a): a multimodal joint
embedding space with images and text and (b): a novel language model for
decoding distributed representations from our space. Our pipeline effectively
unifies joint im... | computer science |
1,899 | Definition of Visual Speech Element and Research on a Method of
Extracting Feature Vector for Korean Lip-Reading | cs.CL | In this paper, we defined the viseme (visual speech element) and described
about the method of extracting visual feature vector. We defined the 10 visemes
based on vowel by analyzing of Korean utterance and proposed the method of
extracting the 20-dimensional visual feature vector, combination of static
features and dy... | computer science |
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