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0 | Dual Recurrent Attention Units for Visual Question Answering | cs.AI | We propose an architecture for VQA which utilizes recurrent layers to
generate visual and textual attention. The memory characteristic of the
proposed recurrent attention units offers a rich joint embedding of visual and
textual features and enables the model to reason relations between several
parts of the image and q... | computer science |
1 | Sequential Short-Text Classification with Recurrent and Convolutional
Neural Networks | cs.CL | Recent approaches based on artificial neural networks (ANNs) have shown
promising results for short-text classification. However, many short texts
occur in sequences (e.g., sentences in a document or utterances in a dialog),
and most existing ANN-based systems do not leverage the preceding short texts
when classifying ... | computer science |
2 | Multiresolution Recurrent Neural Networks: An Application to Dialogue
Response Generation | cs.CL | We introduce the multiresolution recurrent neural network, which extends the
sequence-to-sequence framework to model natural language generation as two
parallel discrete stochastic processes: a sequence of high-level coarse tokens,
and a sequence of natural language tokens. There are many ways to estimate or
learn the ... | computer science |
3 | Learning what to share between loosely related tasks | stat.ML | Multi-task learning is motivated by the observation that humans bring to bear
what they know about related problems when solving new ones. Similarly, deep
neural networks can profit from related tasks by sharing parameters with other
networks. However, humans do not consciously decide to transfer knowledge
between task... | computer science |
4 | A Deep Reinforcement Learning Chatbot | cs.CL | We present MILABOT: a deep reinforcement learning chatbot developed by the
Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize
competition. MILABOT is capable of conversing with humans on popular small talk
topics through both speech and text. The system consists of an ensemble of
natural langu... | computer science |
5 | Generating Sentences by Editing Prototypes | cs.CL | We propose a new generative model of sentences that first samples a prototype
sentence from the training corpus and then edits it into a new sentence.
Compared to traditional models that generate from scratch either left-to-right
or by first sampling a latent sentence vector, our prototype-then-edit model
improves perp... | computer science |
6 | A Deep Reinforcement Learning Chatbot (Short Version) | cs.CL | We present MILABOT: a deep reinforcement learning chatbot developed by the
Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize
competition. MILABOT is capable of conversing with humans on popular small talk
topics through both speech and text. The system consists of an ensemble of
natural langu... | computer science |
7 | Document Image Coding and Clustering for Script Discrimination | cs.CV | The paper introduces a new method for discrimination of documents given in
different scripts. The document is mapped into a uniformly coded text of
numerical values. It is derived from the position of the letters in the text
line, based on their typographical characteristics. Each code is considered as
a gray level. Ac... | computer science |
8 | Tutorial on Answering Questions about Images with Deep Learning | cs.CV | Together with the development of more accurate methods in Computer Vision and
Natural Language Understanding, holistic architectures that answer on questions
about the content of real-world images have emerged. In this tutorial, we build
a neural-based approach to answer questions about images. We base our tutorial
on ... | computer science |
9 | pix2code: Generating Code from a Graphical User Interface Screenshot | cs.LG | Transforming a graphical user interface screenshot created by a designer into
computer code is a typical task conducted by a developer in order to build
customized software, websites, and mobile applications. In this paper, we show
that deep learning methods can be leveraged to train a model end-to-end to
automatically... | computer science |
10 | A Unified Deep Neural Network for Speaker and Language Recognition | cs.CL | Learned feature representations and sub-phoneme posteriors from Deep Neural
Networks (DNNs) have been used separately to produce significant performance
gains for speaker and language recognition tasks. In this work we show how
these gains are possible using a single DNN for both speaker and language
recognition. The u... | computer science |
11 | Efficient Neural Architecture Search via Parameter Sharing | cs.LG | We propose Efficient Neural Architecture Search (ENAS), a fast and
inexpensive approach for automatic model design. In ENAS, a controller learns
to discover neural network architectures by searching for an optimal subgraph
within a large computational graph. The controller is trained with policy
gradient to select a su... | computer science |
12 | Building Machines That Learn and Think Like People | cs.AI | Recent progress in artificial intelligence (AI) has renewed interest in
building systems that learn and think like people. Many advances have come from
using deep neural networks trained end-to-end in tasks such as object
recognition, video games, and board games, achieving performance that equals or
even beats humans ... | computer science |
13 | Towards Bayesian Deep Learning: A Survey | stat.ML | While perception tasks such as visual object recognition and text
understanding play an important role in human intelligence, the subsequent
tasks that involve inference, reasoning and planning require an even higher
level of intelligence. The past few years have seen major advances in many
perception tasks using deep ... | computer science |
14 | Hierarchical Deep Reinforcement Learning: Integrating Temporal
Abstraction and Intrinsic Motivation | cs.LG | Learning goal-directed behavior in environments with sparse feedback is a
major challenge for reinforcement learning algorithms. The primary difficulty
arises due to insufficient exploration, resulting in an agent being unable to
learn robust value functions. Intrinsically motivated agents can explore new
behavior for ... | computer science |
15 | Learning Features by Watching Objects Move | cs.CV | This paper presents a novel yet intuitive approach to unsupervised feature
learning. Inspired by the human visual system, we explore whether low-level
motion-based grouping cues can be used to learn an effective visual
representation. Specifically, we use unsupervised motion-based segmentation on
videos to obtain segme... | computer science |
16 | Domain Adaptive Neural Networks for Object Recognition | cs.CV | We propose a simple neural network model to deal with the domain adaptation
problem in object recognition. Our model incorporates the Maximum Mean
Discrepancy (MMD) measure as a regularization in the supervised learning to
reduce the distribution mismatch between the source and target domains in the
latent space. From ... | computer science |
17 | Beyond Temporal Pooling: Recurrence and Temporal Convolutions for
Gesture Recognition in Video | cs.CV | Recent studies have demonstrated the power of recurrent neural networks for
machine translation, image captioning and speech recognition. For the task of
capturing temporal structure in video, however, there still remain numerous
open research questions. Current research suggests using a simple temporal
feature pooling... | computer science |
18 | Telugu OCR Framework using Deep Learning | stat.ML | In this paper, we address the task of Optical Character Recognition(OCR) for
the Telugu script. We present an end-to-end framework that segments the text
image, classifies the characters and extracts lines using a language model. The
segmentation is based on mathematical morphology. The classification module,
which is ... | computer science |
19 | Adversarial Feature Learning | cs.LG | The ability of the Generative Adversarial Networks (GANs) framework to learn
generative models mapping from simple latent distributions to arbitrarily
complex data distributions has been demonstrated empirically, with compelling
results showing that the latent space of such generators captures semantic
variation in the... | computer science |
20 | The Mythos of Model Interpretability | cs.LG | Supervised machine learning models boast remarkable predictive capabilities.
But can you trust your model? Will it work in deployment? What else can it tell
you about the world? We want models to be not only good, but interpretable. And
yet the task of interpretation appears underspecified. Papers provide diverse
and s... | computer science |
21 | Neurogenesis-Inspired Dictionary Learning: Online Model Adaption in a
Changing World | cs.LG | In this paper, we focus on online representation learning in non-stationary
environments which may require continuous adaptation of model architecture. We
propose a novel online dictionary-learning (sparse-coding) framework which
incorporates the addition and deletion of hidden units (dictionary elements),
and is inspi... | computer science |
22 | Borrowing Treasures from the Wealthy: Deep Transfer Learning through
Selective Joint Fine-tuning | cs.CV | Deep neural networks require a large amount of labeled training data during
supervised learning. However, collecting and labeling so much data might be
infeasible in many cases. In this paper, we introduce a source-target selective
joint fine-tuning scheme for improving the performance of deep learning tasks
with insuf... | computer science |
23 | Aligned Image-Word Representations Improve Inductive Transfer Across
Vision-Language Tasks | cs.CV | An important goal of computer vision is to build systems that learn visual
representations over time that can be applied to many tasks. In this paper, we
investigate a vision-language embedding as a core representation and show that
it leads to better cross-task transfer than standard multi-task learning. In
particular... | computer science |
24 | Universal Adversarial Perturbations Against Semantic Image Segmentation | stat.ML | While deep learning is remarkably successful on perceptual tasks, it was also
shown to be vulnerable to adversarial perturbations of the input. These
perturbations denote noise added to the input that was generated specifically
to fool the system while being quasi-imperceptible for humans. More severely,
there even exi... | computer science |
25 | The loss surface of deep and wide neural networks | cs.LG | While the optimization problem behind deep neural networks is highly
non-convex, it is frequently observed in practice that training deep networks
seems possible without getting stuck in suboptimal points. It has been argued
that this is the case as all local minima are close to being globally optimal.
We show that thi... | computer science |
26 | Semantically Decomposing the Latent Spaces of Generative Adversarial
Networks | cs.LG | We propose a new algorithm for training generative adversarial networks that
jointly learns latent codes for both identities (e.g. individual humans) and
observations (e.g. specific photographs). By fixing the identity portion of the
latent codes, we can generate diverse images of the same subject, and by fixing
the ob... | computer science |
27 | Variants of RMSProp and Adagrad with Logarithmic Regret Bounds | cs.LG | Adaptive gradient methods have become recently very popular, in particular as
they have been shown to be useful in the training of deep neural networks. In
this paper we have analyzed RMSProp, originally proposed for the training of
deep neural networks, in the context of online convex optimization and show
$\sqrt{T}$-... | computer science |
28 | ALICE: Towards Understanding Adversarial Learning for Joint Distribution
Matching | stat.ML | We investigate the non-identifiability issues associated with bidirectional
adversarial training for joint distribution matching. Within a framework of
conditional entropy, we propose both adversarial and non-adversarial approaches
to learn desirable matched joint distributions for unsupervised and supervised
tasks. We... | computer science |
29 | A systematic study of the class imbalance problem in convolutional
neural networks | cs.CV | In this study, we systematically investigate the impact of class imbalance on
classification performance of convolutional neural networks (CNNs) and compare
frequently used methods to address the issue. Class imbalance is a common
problem that has been comprehensively studied in classical machine learning,
yet very lim... | computer science |
30 | Regularization for Deep Learning: A Taxonomy | cs.LG | Regularization is one of the crucial ingredients of deep learning, yet the
term regularization has various definitions, and regularization methods are
often studied separately from each other. In our work we present a systematic,
unifying taxonomy to categorize existing methods. We distinguish methods that
affect data,... | computer science |
31 | Clustering with Deep Learning: Taxonomy and New Methods | cs.LG | Clustering is a fundamental machine learning method. The quality of its
results is dependent on the data distribution. For this reason, deep neural
networks can be used for learning better representations of the data. In this
paper, we propose a systematic taxonomy for clustering with deep learning, in
addition to a re... | computer science |
32 | Coarse to fine non-rigid registration: a chain of scale-specific neural
networks for multimodal image alignment with application to remote sensing | cs.CV | We tackle here the problem of multimodal image non-rigid registration, which
is of prime importance in remote sensing and medical imaging. The difficulties
encountered by classical registration approaches include feature design and
slow optimization by gradient descent. By analyzing these methods, we note the
significa... | computer science |
33 | Describing Videos by Exploiting Temporal Structure | stat.ML | Recent progress in using recurrent neural networks (RNNs) for image
description has motivated the exploration of their application for video
description. However, while images are static, working with videos requires
modeling their dynamic temporal structure and then properly integrating that
information into a natural... | computer science |
34 | Collaborative Recurrent Autoencoder: Recommend while Learning to Fill in
the Blanks | cs.LG | Hybrid methods that utilize both content and rating information are commonly
used in many recommender systems. However, most of them use either handcrafted
features or the bag-of-words representation as a surrogate for the content
information but they are neither effective nor natural enough. To address this
problem, w... | computer science |
35 | Sentiment Classification using Images and Label Embeddings | cs.CL | In this project we analysed how much semantic information images carry, and
how much value image data can add to sentiment analysis of the text associated
with the images. To better understand the contribution from images, we compared
models which only made use of image data, models which only made use of text
data, an... | computer science |
36 | Natural-Parameter Networks: A Class of Probabilistic Neural Networks | cs.LG | Neural networks (NN) have achieved state-of-the-art performance in various
applications. Unfortunately in applications where training data is
insufficient, they are often prone to overfitting. One effective way to
alleviate this problem is to exploit the Bayesian approach by using Bayesian
neural networks (BNN). Anothe... | computer science |
37 | Learning to Perform Physics Experiments via Deep Reinforcement Learning | stat.ML | When encountering novel objects, humans are able to infer a wide range of
physical properties such as mass, friction and deformability by interacting
with them in a goal driven way. This process of active interaction is in the
same spirit as a scientist performing experiments to discover hidden facts.
Recent advances i... | computer science |
38 | A Network-based End-to-End Trainable Task-oriented Dialogue System | cs.CL | Teaching machines to accomplish tasks by conversing naturally with humans is
challenging. Currently, developing task-oriented dialogue systems requires
creating multiple components and typically this involves either a large amount
of handcrafting, or acquiring costly labelled datasets to solve a statistical
learning pr... | computer science |
39 | A Factorization Machine Framework for Testing Bigram Embeddings in
Knowledgebase Completion | cs.CL | Embedding-based Knowledge Base Completion models have so far mostly combined
distributed representations of individual entities or relations to compute
truth scores of missing links. Facts can however also be represented using
pairwise embeddings, i.e. embeddings for pairs of entities and relations. In
this paper we ex... | computer science |
40 | Neural Networks for Joint Sentence Classification in Medical Paper
Abstracts | cs.CL | Existing models based on artificial neural networks (ANNs) for sentence
classification often do not incorporate the context in which sentences appear,
and classify sentences individually. However, traditional sentence
classification approaches have been shown to greatly benefit from jointly
classifying subsequent sente... | computer science |
41 | De-identification of Patient Notes with Recurrent Neural Networks | cs.CL | Objective: Patient notes in electronic health records (EHRs) may contain
critical information for medical investigations. However, the vast majority of
medical investigators can only access de-identified notes, in order to protect
the confidentiality of patients. In the United States, the Health Insurance
Portability a... | computer science |
42 | Reasoning with Memory Augmented Neural Networks for Language
Comprehension | cs.CL | Hypothesis testing is an important cognitive process that supports human
reasoning. In this paper, we introduce a computational hypothesis testing
approach based on memory augmented neural networks. Our approach involves a
hypothesis testing loop that reconsiders and progressively refines a previously
formed hypothesis... | computer science |
43 | Automatic Rule Extraction from Long Short Term Memory Networks | cs.CL | Although deep learning models have proven effective at solving problems in
natural language processing, the mechanism by which they come to their
conclusions is often unclear. As a result, these models are generally treated
as black boxes, yielding no insight of the underlying learned patterns. In this
paper we conside... | computer science |
44 | Comparing Rule-Based and Deep Learning Models for Patient Phenotyping | cs.CL | Objective: We investigate whether deep learning techniques for natural
language processing (NLP) can be used efficiently for patient phenotyping.
Patient phenotyping is a classification task for determining whether a patient
has a medical condition, and is a crucial part of secondary analysis of
healthcare data. We ass... | computer science |
45 | MIT at SemEval-2017 Task 10: Relation Extraction with Convolutional
Neural Networks | cs.CL | Over 50 million scholarly articles have been published: they constitute a
unique repository of knowledge. In particular, one may infer from them
relations between scientific concepts, such as synonyms and hyponyms.
Artificial neural networks have been recently explored for relation extraction.
In this work, we continue... | computer science |
46 | Transfer Learning for Named-Entity Recognition with Neural Networks | cs.CL | Recent approaches based on artificial neural networks (ANNs) have shown
promising results for named-entity recognition (NER). In order to achieve high
performances, ANNs need to be trained on a large labeled dataset. However,
labels might be difficult to obtain for the dataset on which the user wants to
perform NER: la... | computer science |
47 | Adversarial Generation of Natural Language | cs.CL | Generative Adversarial Networks (GANs) have gathered a lot of attention from
the computer vision community, yielding impressive results for image
generation. Advances in the adversarial generation of natural language from
noise however are not commensurate with the progress made in generating images,
and still lag far ... | computer science |
48 | Explaining Recurrent Neural Network Predictions in Sentiment Analysis | cs.CL | Recently, a technique called Layer-wise Relevance Propagation (LRP) was shown
to deliver insightful explanations in the form of input space relevances for
understanding feed-forward neural network classification decisions. In the
present work, we extend the usage of LRP to recurrent neural networks. We
propose a specif... | computer science |
49 | Text Compression for Sentiment Analysis via Evolutionary Algorithms | cs.NE | Can textual data be compressed intelligently without losing accuracy in
evaluating sentiment? In this study, we propose a novel evolutionary
compression algorithm, PARSEC (PARts-of-Speech for sEntiment Compression),
which makes use of Parts-of-Speech tags to compress text in a way that
sacrifices minimal classification... | computer science |
50 | Building competitive direct acoustics-to-word models for English
conversational speech recognition | cs.CL | Direct acoustics-to-word (A2W) models in the end-to-end paradigm have
received increasing attention compared to conventional sub-word based automatic
speech recognition models using phones, characters, or context-dependent hidden
Markov model states. This is because A2W models recognize words from speech
without any de... | computer science |
51 | Ask, Attend and Answer: Exploring Question-Guided Spatial Attention for
Visual Question Answering | cs.CV | We address the problem of Visual Question Answering (VQA), which requires
joint image and language understanding to answer a question about a given
photograph. Recent approaches have applied deep image captioning methods based
on convolutional-recurrent networks to this problem, but have failed to model
spatial inferen... | computer science |
52 | Task-driven Visual Saliency and Attention-based Visual Question
Answering | cs.CV | Visual question answering (VQA) has witnessed great progress since May, 2015
as a classic problem unifying visual and textual data into a system. Many
enlightening VQA works explore deep into the image and question encodings and
fusing methods, of which attention is the most effective and infusive
mechanism. Current at... | computer science |
53 | Optimising The Input Window Alignment in CD-DNN Based Phoneme
Recognition for Low Latency Processing | cs.CL | We present a systematic analysis on the performance of a phonetic recogniser
when the window of input features is not symmetric with respect to the current
frame. The recogniser is based on Context Dependent Deep Neural Networks
(CD-DNNs) and Hidden Markov Models (HMMs). The objective is to reduce the
latency of the sy... | computer science |
54 | Bridging LSTM Architecture and the Neural Dynamics during Reading | cs.CL | Recently, the long short-term memory neural network (LSTM) has attracted wide
interest due to its success in many tasks. LSTM architecture consists of a
memory cell and three gates, which looks similar to the neuronal networks in
the brain. However, there still lacks the evidence of the cognitive
plausibility of LSTM a... | computer science |
55 | Feature Weight Tuning for Recursive Neural Networks | cs.NE | This paper addresses how a recursive neural network model can automatically
leave out useless information and emphasize important evidence, in other words,
to perform "weight tuning" for higher-level representation acquisition. We
propose two models, Weighted Neural Network (WNN) and Binary-Expectation Neural
Network (... | computer science |
56 | A New Data Representation Based on Training Data Characteristics to
Extract Drug Named-Entity in Medical Text | cs.CL | One essential task in information extraction from the medical corpus is drug
name recognition. Compared with text sources come from other domains, the
medical text is special and has unique characteristics. In addition, the
medical text mining poses more challenges, e.g., more unstructured text, the
fast growing of new... | computer science |
57 | DopeLearning: A Computational Approach to Rap Lyrics Generation | cs.LG | Writing rap lyrics requires both creativity to construct a meaningful,
interesting story and lyrical skills to produce complex rhyme patterns, which
form the cornerstone of good flow. We present a rap lyrics generation method
that captures both of these aspects. First, we develop a prediction model to
identify the next... | computer science |
58 | Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN | cs.CL | Semantic matching, which aims to determine the matching degree between two
texts, is a fundamental problem for many NLP applications. Recently, deep
learning approach has been applied to this problem and significant improvements
have been achieved. In this paper, we propose to view the generation of the
global interact... | computer science |
59 | Piecewise Latent Variables for Neural Variational Text Processing | cs.CL | Advances in neural variational inference have facilitated the learning of
powerful directed graphical models with continuous latent variables, such as
variational autoencoders. The hope is that such models will learn to represent
rich, multi-modal latent factors in real-world data, such as natural language
text. Howeve... | computer science |
60 | Recurrent Neural Networks with External Memory for Language
Understanding | cs.CL | Recurrent Neural Networks (RNNs) have become increasingly popular for the
task of language understanding. In this task, a semantic tagger is deployed to
associate a semantic label to each word in an input sequence. The success of
RNN may be attributed to its ability to memorize long-term dependence that
relates the cur... | computer science |
61 | A Neural Network Approach to Context-Sensitive Generation of
Conversational Responses | cs.CL | We present a novel response generation system that can be trained end to end
on large quantities of unstructured Twitter conversations. A neural network
architecture is used to address sparsity issues that arise when integrating
contextual information into classic statistical models, allowing the system to
take into ac... | computer science |
62 | The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured
Multi-Turn Dialogue Systems | cs.CL | This paper introduces the Ubuntu Dialogue Corpus, a dataset containing almost
1 million multi-turn dialogues, with a total of over 7 million utterances and
100 million words. This provides a unique resource for research into building
dialogue managers based on neural language models that can make use of large
amounts o... | computer science |
63 | Building End-To-End Dialogue Systems Using Generative Hierarchical
Neural Network Models | cs.CL | We investigate the task of building open domain, conversational dialogue
systems based on large dialogue corpora using generative models. Generative
models produce system responses that are autonomously generated word-by-word,
opening up the possibility for realistic, flexible interactions. In support of
this goal, we ... | computer science |
64 | End-to-End Attention-based Large Vocabulary Speech Recognition | cs.CL | Many of the current state-of-the-art Large Vocabulary Continuous Speech
Recognition Systems (LVCSR) are hybrids of neural networks and Hidden Markov
Models (HMMs). Most of these systems contain separate components that deal with
the acoustic modelling, language modelling and sequence decoding. We
investigate a more dir... | computer science |
65 | Towards Neural Network-based Reasoning | cs.AI | We propose Neural Reasoner, a framework for neural network-based reasoning
over natural language sentences. Given a question, Neural Reasoner can infer
over multiple supporting facts and find an answer to the question in specific
forms. Neural Reasoner has 1) a specific interaction-pooling mechanism,
allowing it to exa... | computer science |
66 | What to talk about and how? Selective Generation using LSTMs with
Coarse-to-Fine Alignment | cs.CL | We propose an end-to-end, domain-independent neural encoder-aligner-decoder
model for selective generation, i.e., the joint task of content selection and
surface realization. Our model first encodes a full set of over-determined
database event records via an LSTM-based recurrent neural network, then
utilizes a novel co... | computer science |
67 | Reasoning about Entailment with Neural Attention | cs.CL | While most approaches to automatically recognizing entailment relations have
used classifiers employing hand engineered features derived from complex
natural language processing pipelines, in practice their performance has been
only slightly better than bag-of-word pair classifiers using only lexical
similarity. The on... | computer science |
68 | Highway Long Short-Term Memory RNNs for Distant Speech Recognition | cs.NE | In this paper, we extend the deep long short-term memory (DLSTM) recurrent
neural networks by introducing gated direct connections between memory cells in
adjacent layers. These direct links, called highway connections, enable
unimpeded information flow across different layers and thus alleviate the
gradient vanishing ... | computer science |
69 | Neural Enquirer: Learning to Query Tables with Natural Language | cs.AI | We proposed Neural Enquirer as a neural network architecture to execute a
natural language (NL) query on a knowledge-base (KB) for answers. Basically,
Neural Enquirer finds the distributed representation of a query and then
executes it on knowledge-base tables to obtain the answer as one of the values
in the tables. Un... | computer science |
70 | Sentence Pair Scoring: Towards Unified Framework for Text Comprehension | cs.CL | We review the task of Sentence Pair Scoring, popular in the literature in
various forms - viewed as Answer Sentence Selection, Semantic Text Scoring,
Next Utterance Ranking, Recognizing Textual Entailment, Paraphrasing or e.g. a
component of Memory Networks.
We argue that all such tasks are similar from the model per... | computer science |
71 | Incorporating Copying Mechanism in Sequence-to-Sequence Learning | cs.CL | We address an important problem in sequence-to-sequence (Seq2Seq) learning
referred to as copying, in which certain segments in the input sequence are
selectively replicated in the output sequence. A similar phenomenon is
observable in human language communication. For example, humans tend to repeat
entity names or eve... | computer science |
72 | Generating Factoid Questions With Recurrent Neural Networks: The 30M
Factoid Question-Answer Corpus | cs.CL | Over the past decade, large-scale supervised learning corpora have enabled
machine learning researchers to make substantial advances. However, to this
date, there are no large-scale question-answer corpora available. In this paper
we present the 30M Factoid Question-Answer Corpus, an enormous question answer
pair corpu... | computer science |
73 | How NOT To Evaluate Your Dialogue System: An Empirical Study of
Unsupervised Evaluation Metrics for Dialogue Response Generation | cs.CL | We investigate evaluation metrics for dialogue response generation systems
where supervised labels, such as task completion, are not available. Recent
works in response generation have adopted metrics from machine translation to
compare a model's generated response to a single target response. We show that
these metric... | computer science |
74 | A Hierarchical Latent Variable Encoder-Decoder Model for Generating
Dialogues | cs.CL | Sequential data often possesses a hierarchical structure with complex
dependencies between subsequences, such as found between the utterances in a
dialogue. In an effort to model this kind of generative process, we propose a
neural network-based generative architecture, with latent stochastic variables
that span a vari... | computer science |
75 | Neural Associative Memory for Dual-Sequence Modeling | cs.NE | Many important NLP problems can be posed as dual-sequence or
sequence-to-sequence modeling tasks. Recent advances in building end-to-end
neural architectures have been highly successful in solving such tasks. In this
work we propose a new architecture for dual-sequence modeling that is based on
associative memory. We d... | computer science |
76 | Log-Linear RNNs: Towards Recurrent Neural Networks with Flexible Prior
Knowledge | cs.AI | We introduce LL-RNNs (Log-Linear RNNs), an extension of Recurrent Neural
Networks that replaces the softmax output layer by a log-linear output layer,
of which the softmax is a special case. This conceptually simple move has two
main advantages. First, it allows the learner to combat training data sparsity
by allowing ... | computer science |
77 | Embracing data abundance: BookTest Dataset for Reading Comprehension | cs.CL | There is a practically unlimited amount of natural language data available.
Still, recent work in text comprehension has focused on datasets which are
small relative to current computing possibilities. This article is making a
case for the community to move to larger data and as a step in that direction
it is proposing... | computer science |
78 | Quasi-Recurrent Neural Networks | cs.NE | Recurrent neural networks are a powerful tool for modeling sequential data,
but the dependence of each timestep's computation on the previous timestep's
output limits parallelism and makes RNNs unwieldy for very long sequences. We
introduce quasi-recurrent neural networks (QRNNs), an approach to neural
sequence modelin... | computer science |
79 | Input Switched Affine Networks: An RNN Architecture Designed for
Interpretability | cs.AI | There exist many problem domains where the interpretability of neural network
models is essential for deployment. Here we introduce a recurrent architecture
composed of input-switched affine transformations - in other words an RNN
without any explicit nonlinearities, but with input-dependent recurrent
weights. This sim... | computer science |
80 | Frustratingly Short Attention Spans in Neural Language Modeling | cs.CL | Neural language models predict the next token using a latent representation
of the immediate token history. Recently, various methods for augmenting neural
language models with an attention mechanism over a differentiable memory have
been proposed. For predicting the next token, these models query information
from a me... | computer science |
81 | A Structured Self-attentive Sentence Embedding | cs.CL | This paper proposes a new model for extracting an interpretable sentence
embedding by introducing self-attention. Instead of using a vector, we use a
2-D matrix to represent the embedding, with each row of the matrix attending on
a different part of the sentence. We also propose a self-attention mechanism
and a special... | computer science |
82 | A Recurrent Neural Model with Attention for the Recognition of Chinese
Implicit Discourse Relations | cs.CL | We introduce an attention-based Bi-LSTM for Chinese implicit discourse
relations and demonstrate that modeling argument pairs as a joint sequence can
outperform word order-agnostic approaches. Our model benefits from a partial
sampling scheme and is conceptually simple, yet achieves state-of-the-art
performance on the ... | computer science |
83 | Event Representations for Automated Story Generation with Deep Neural
Nets | cs.CL | Automated story generation is the problem of automatically selecting a
sequence of events, actions, or words that can be told as a story. We seek to
develop a system that can generate stories by learning everything it needs to
know from textual story corpora. To date, recurrent neural networks that learn
language model... | computer science |
84 | A Joint Model for Question Answering and Question Generation | cs.CL | We propose a generative machine comprehension model that learns jointly to
ask and answer questions based on documents. The proposed model uses a
sequence-to-sequence framework that encodes the document and generates a
question (answer) given an answer (question). Significant improvement in model
performance is observe... | computer science |
85 | Learning Intrinsic Sparse Structures within Long Short-Term Memory | cs.LG | Model compression is significant for the wide adoption of Recurrent Neural
Networks (RNNs) in both user devices possessing limited resources and business
clusters requiring quick responses to large-scale service requests. This work
aims to learn structurally-sparse Long Short-Term Memory (LSTM) by reducing the
sizes of... | computer science |
86 | Why PairDiff works? -- A Mathematical Analysis of Bilinear Relational
Compositional Operators for Analogy Detection | cs.CL | Representing the semantic relations that exist between two given words (or
entities) is an important first step in a wide-range of NLP applications such
as analogical reasoning, knowledge base completion and relational information
retrieval. A simple, yet surprisingly accurate method for representing a
relation between... | computer science |
87 | Object-oriented Neural Programming (OONP) for Document Understanding | cs.LG | We propose Object-oriented Neural Programming (OONP), a framework for
semantically parsing documents in specific domains. Basically, OONP reads a
document and parses it into a predesigned object-oriented data structure
(referred to as ontology in this paper) that reflects the domain-specific
semantics of the document. ... | computer science |
88 | A Neural Comprehensive Ranker (NCR) for Open-Domain Question Answering | cs.CL | This paper proposes a novel neural machine reading model for open-domain
question answering at scale. Existing machine comprehension models typically
assume that a short piece of relevant text containing answers is already
identified and given to the models, from which the models are designed to
extract answers. This a... | computer science |
89 | Improving speech recognition by revising gated recurrent units | cs.CL | Speech recognition is largely taking advantage of deep learning, showing that
substantial benefits can be obtained by modern Recurrent Neural Networks
(RNNs). The most popular RNNs are Long Short-Term Memory (LSTMs), which
typically reach state-of-the-art performance in many tasks thanks to their
ability to learn long-... | computer science |
90 | Integrating planning for task-completion dialogue policy learning | cs.CL | Training a task-completion dialogue agent with real users via reinforcement
learning (RL) could be prohibitively expensive, because it requires many
interactions with users. One alternative is to resort to a user simulator,
while the discrepancy of between simulated and real users makes the learned
policy unreliable in... | computer science |
91 | Building DNN Acoustic Models for Large Vocabulary Speech Recognition | cs.CL | Deep neural networks (DNNs) are now a central component of nearly all
state-of-the-art speech recognition systems. Building neural network acoustic
models requires several design decisions including network architecture, size,
and training loss function. This paper offers an empirical investigation on
which aspects of ... | computer science |
92 | Deep Recurrent Neural Networks for Acoustic Modelling | cs.LG | We present a novel deep Recurrent Neural Network (RNN) model for acoustic
modelling in Automatic Speech Recognition (ASR). We term our contribution as a
TC-DNN-BLSTM-DNN model, the model combines a Deep Neural Network (DNN) with
Time Convolution (TC), followed by a Bidirectional Long Short-Term Memory
(BLSTM), and a fi... | computer science |
93 | Regularizing RNNs by Stabilizing Activations | cs.NE | We stabilize the activations of Recurrent Neural Networks (RNNs) by
penalizing the squared distance between successive hidden states' norms.
This penalty term is an effective regularizer for RNNs including LSTMs and
IRNNs, improving performance on character-level language modeling and phoneme
recognition, and outperf... | computer science |
94 | Outrageously Large Neural Networks: The Sparsely-Gated
Mixture-of-Experts Layer | cs.LG | The capacity of a neural network to absorb information is limited by its
number of parameters. Conditional computation, where parts of the network are
active on a per-example basis, has been proposed in theory as a way of
dramatically increasing model capacity without a proportional increase in
computation. In practice... | computer science |
95 | Discourse-Based Objectives for Fast Unsupervised Sentence Representation
Learning | cs.CL | This work presents a novel objective function for the unsupervised training
of neural network sentence encoders. It exploits signals from paragraph-level
discourse coherence to train these models to understand text. Our objective is
purely discriminative, allowing us to train models many times faster than was
possible ... | computer science |
96 | Learning Convolutional Text Representations for Visual Question
Answering | cs.LG | Visual question answering is a recently proposed artificial intelligence task
that requires a deep understanding of both images and texts. In deep learning,
images are typically modeled through convolutional neural networks, and texts
are typically modeled through recurrent neural networks. While the requirement
for mo... | computer science |
97 | Learning Phrase Representations using RNN Encoder-Decoder for
Statistical Machine Translation | cs.CL | In this paper, we propose a novel neural network model called RNN
Encoder-Decoder that consists of two recurrent neural networks (RNN). One RNN
encodes a sequence of symbols into a fixed-length vector representation, and
the other decodes the representation into another sequence of symbols. The
encoder and decoder of t... | computer science |
98 | Recurrent Neural Network Training with Dark Knowledge Transfer | stat.ML | Recurrent neural networks (RNNs), particularly long short-term memory (LSTM),
have gained much attention in automatic speech recognition (ASR). Although some
successful stories have been reported, training RNNs remains highly
challenging, especially with limited training data. Recent research found that
a well-trained ... | computer science |
99 | Long Short-Term Memory Based Recurrent Neural Network Architectures for
Large Vocabulary Speech Recognition | cs.NE | Long Short-Term Memory (LSTM) is a recurrent neural network (RNN)
architecture that has been designed to address the vanishing and exploding
gradient problems of conventional RNNs. Unlike feedforward neural networks,
RNNs have cyclic connections making them powerful for modeling sequences. They
have been successfully u... | computer science |
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