Unnamed: 0.1 int64 0 41k | Unnamed: 0 int64 0 41k | author stringlengths 9 1.39k | id stringlengths 11 18 | summary stringlengths 25 3.66k | title stringlengths 4 258 | year int64 1.99k 2.02k | arxiv_url stringlengths 32 39 | info stringlengths 523 3.18k | embeddings stringlengths 16.9k 17.1k |
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500 | 500 | ['Ji Young Lee', 'Franck Dernoncourt', 'Ozlem Uzuner', 'Peter Szolovits'] | 1610.09704v1 | 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... | Feature-Augmented Neural Networks for Patient Note De-identification | 2,016 | http://arxiv.org/pdf/1610.09704v1 | Title FeatureAugmented Neural Networks Patient Note Deidentification Summary Patient note contain wealth information potentially great interest medical investigator However protect patient privacy Protected Health Information PHI must removed patient note legally released process known patient note deidentification mai... | [0.043702758848667145, 0.08156780898571014, -0.015921566635370255, -0.013011055067181587, -0.005612940061837435, 0.007986437529325485, 0.04966949298977852, 0.019367963075637817, 0.006645694375038147, 0.005578699987381697, 0.03567885980010033, -0.015435667708516121, 0.021586939692497253, 0.08970491588115692, 0.011171227... |
501 | 501 | ['Kartik Audhkhasi', 'Bhuvana Ramabhadran', 'George Saon', 'Michael Picheny', 'David Nahamoo'] | 1703.07754v1 | 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... | Direct Acoustics-to-Word Models for English Conversational Speech
Recognition | 2,017 | http://arxiv.org/pdf/1703.07754v1 | Title Direct AcousticstoWord Models English Conversational Speech Recognition Summary Recent work endtoend automatic speech recognition ASR shown connectionist temporal classification CTC loss used convert acoustic phone character sequence system used dictionary separatelytrained Language Model LM produce word sequence... | [0.02484346553683281, 0.033995211124420166, 0.01255255751311779, 0.0641455352306366, -0.03553905338048935, -0.00043097714660689235, 0.03510015085339546, 0.015079144388437271, -0.02754133567214012, -0.04431154206395149, -0.0484241284430027, -0.020511770620942116, 0.047198012471199036, 0.029331816360354424, -0.0065767988... |
502 | 502 | ['Oleksii Kuchaiev', 'Boris Ginsburg'] | 1703.10722v3 | 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... | Factorization tricks for LSTM networks | 2,017 | http://arxiv.org/pdf/1703.10722v3 | Title Factorization trick LSTM network Summary present two simple way reducing number parameter accelerating training large Long ShortTerm Memory LSTM network first one matrix factorization design LSTM matrix product two smaller matrix second one partitioning LSTM matrix input state independent group approach allow u t... | [-0.026453623548150063, 0.03137867525219917, 0.014112189412117004, 0.07220063358545303, 0.005589200649410486, 0.00911327637732029, 0.02947700396180153, 0.0013658213429152966, -0.03214815631508827, -0.04276707395911217, -0.046675652265548706, 0.0071130963042378426, 0.0020036331843584776, 0.03333934023976326, 0.021827960... |
503 | 503 | ['Franck Dernoncourt', 'Ji Young Lee', 'Peter Szolovits'] | 1705.05487v1 | 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... | NeuroNER: an easy-to-use program for named-entity recognition based on
neural networks | 2,017 | http://arxiv.org/pdf/1705.05487v1 | Title NeuroNER easytouse program namedentity recognition based neural network Summary Namedentity recognition NER aim identifying entity interest text Artificial neural network ANNs recently shown outperform existing NER system However ANNs remain challenging use nonexpert user paper present NeuroNER easytouse namedent... | [0.047105710953474045, 0.01603306084871292, 0.012159192003309727, 0.03783440217375755, 0.01361879613250494, 0.015137418173253536, 0.03210264444351196, 0.0026426741387695074, -0.007367223035544157, -0.03166850656270981, -0.0022698300890624523, 0.03770449012517929, 0.011607103981077671, 0.0037961245980113745, 0.023154465... |
504 | 504 | ['Zhenisbek Assylbekov', 'Rustem Takhanov', 'Bagdat Myrzakhmetov', 'Jonathan N. Washington'] | 1707.06480v1 | 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. | Syllable-aware Neural Language Models: A Failure to Beat Character-aware
Ones | 2,017 | http://arxiv.org/pdf/1707.06480v1 | Title Syllableaware Neural Language Models Failure Beat Characteraware Ones Summary Syllabification seem improve wordlevel RNN language modeling quality compared characterbased segmentation However best syllableaware language model achieving performance comparable competitive characteraware model 1833 fewer parameter t... | [0.017384110018610954, 0.08129464089870453, 0.02031603455543518, 0.02979023940861225, -0.022303177043795586, -0.01334854494780302, 0.07097753137350082, 0.008717306889593601, -0.08133973926305771, -0.004194943234324455, -0.033547624945640564, -0.07666829973459244, 0.06709268689155579, -0.05620016157627106, 0.06638380140... |
505 | 505 | ['Iñigo Casanueva', 'Paweł Budzianowski', 'Pei-Hao Su', 'Nikola Mrkšić', 'Tsung-Hsien Wen', 'Stefan Ultes', 'Lina Rojas-Barahona', 'Steve Young', 'Milica Gašić'] | 1711.11023v1 | 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... | A Benchmarking Environment for Reinforcement Learning Based Task
Oriented Dialogue Management | 2,017 | http://arxiv.org/pdf/1711.11023v1 | Title Benchmarking Environment Reinforcement Learning Based Task Oriented Dialogue Management Summary Dialogue assistant rapidly becoming indispensable daily aid avoid significant effort needed handcraft required dialogue flow Dialogue Management DM module cast continuous Markov Decision Process MDP trained Reinforceme... | [0.07061508297920227, 0.004467794205993414, -0.0014938859967514873, 0.006259709130972624, -0.043233588337898254, 0.005580393597483635, 0.024448717013001442, -0.03563292324542999, -0.012285783886909485, -0.01634962111711502, -0.03158413618803024, 0.0056253825314342976, -0.03055431693792343, 0.0707760900259018, -0.041852... |
506 | 506 | ['Zhenisbek Assylbekov', 'Rustem Takhanov'] | 1802.08375v1 | 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... | Reusing Weights in Subword-aware Neural Language Models | 2,018 | http://arxiv.org/pdf/1802.08375v1 | Title Reusing Weights Subwordaware Neural Language Models Summary propose several way reusing subword embeddings weight subwordaware neural language model proposed technique benefit competitive characteraware model improve performance syllable morphemeaware model showing significant reduction model size discover simple... | [0.0314902625977993, 0.06977414339780807, 0.009921452961862087, 0.05988321453332901, -0.03749609366059303, -0.014724734239280224, 0.026353269815444946, 0.018781455233693123, -0.03120853565633297, 0.010252118110656738, -0.02827058918774128, -0.05689524859189987, 0.03973189741373062, -0.0033906486351042986, 0.05414461717... |
507 | 507 | ['Isabelle Augenstein', 'Sebastian Ruder', 'Anders Søgaard'] | 1802.09913v1 | 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... | Multi-task Learning of Pairwise Sequence Classification Tasks Over
Disparate Label Spaces | 2,018 | http://arxiv.org/pdf/1802.09913v1 | Title Multitask Learning Pairwise Sequence Classification Tasks Disparate Label Spaces Summary combine multitask learning semisupervised learning inducing joint embedding space disparate label space learning transfer function label embeddings enabling u jointly leverage unlabelled data auxiliary annotated datasets eval... | [-0.0072976648807525635, 0.029084190726280212, -0.015074536204338074, 0.01611342467367649, -0.002867706585675478, 0.03876494988799095, 0.0400647297501564, -0.03494870290160179, 0.029712270945310593, -0.06404870003461838, -0.040871720761060715, -0.019969899207353592, -0.03229004889726639, 0.047194626182317734, -0.001758... |
508 | 508 | ['Caiming Xiong', 'Stephen Merity', 'Richard Socher'] | 1603.01417v1 | 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... | Dynamic Memory Networks for Visual and Textual Question Answering | 2,016 | http://arxiv.org/pdf/1603.01417v1 | Title Dynamic Memory Networks Visual Textual Question Answering Summary Neural network architecture memory attention mechanism exhibit certain reasoning capability required question answering One architecture dynamic memory network DMN obtained high accuracy variety language task However shown whether architecture achi... | [0.05473654344677925, -0.014790971763432026, -0.048675354570150375, 0.033531010150909424, 0.00014234644186217338, 0.024418624117970467, -0.011225576512515545, -0.001926077646203339, 0.0046080113388597965, -0.028277456760406494, 0.02012714184820652, -0.0230622086673975, -0.016158970072865486, 0.049191515892744064, 0.051... |
509 | 509 | ['Fabio Carrara', 'Andrea Esuli', 'Tiziano Fagni', 'Fabrizio Falchi', 'Alejandro Moreo Fernández'] | 1606.07287v1 | 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... | Picture It In Your Mind: Generating High Level Visual Representations
From Textual Descriptions | 2,016 | http://arxiv.org/pdf/1606.07287v1 | Title Picture Mind Generating High Level Visual Representations Textual Descriptions Summary paper tackle problem image search query short textual description image user looking choose implement actual search process similarity search visual feature space learning translate textual query visual representation Searching... | [0.03907022997736931, 0.039165105670690536, 0.004019522573798895, 0.06499049067497253, -0.020583219826221466, 0.005293701775372028, -0.0029849207494407892, 0.021723872050642967, -0.015071997418999672, -0.053929056972265244, -0.03275226429104805, 0.00982444453984499, -0.003892771666869521, 0.06876673549413681, 0.0166816... |
510 | 510 | ['Marc Tanti', 'Albert Gatt', 'Kenneth P. Camilleri'] | 1703.09137v2 | 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 `... | Where to put the Image in an Image Caption Generator | 2,017 | http://arxiv.org/pdf/1703.09137v2 | Title put Image Image Caption Generator Summary recurrent neural network language model used caption generation image information fed neural network either directly incorporating RNN conditioning language model injecting image feature layer following RNN conditioning language model merging image feature option attested... | [0.03209516778588295, 0.055982545018196106, -0.006379342172294855, 0.030374499037861824, -0.03485017269849777, -0.0006257386994548142, 0.035780780017375946, -0.0061155143193900585, -0.026076918467879295, -0.04781739413738251, -0.006454643327742815, -0.02906055562198162, 0.05437486618757248, 0.07614684104919434, 0.03335... |
511 | 511 | ['Ilija Ilievski', 'Shuicheng Yan', 'Jiashi Feng'] | 1604.01485v1 | 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... | A Focused Dynamic Attention Model for Visual Question Answering | 2,016 | http://arxiv.org/pdf/1604.01485v1 | Title Focused Dynamic Attention Model Visual Question Answering Summary Visual Question Answering VQA problem attracting increasing interest multiple research discipline Solving VQA problem requires technique computer vision understanding visual content presented image video well one natural language processing underst... | [0.06563566625118256, -0.00966651365160942, -0.027689313516020775, 0.04710132256150246, -0.010935337282717228, 0.02032623253762722, -0.0013326873304322362, 0.021615877747535706, 0.002249724231660366, -0.04811464250087738, 0.02061512880027294, -0.022769127041101456, -0.014294068329036236, 0.0656609833240509, 0.052788011... |
512 | 512 | ['Remi Lebret', 'Pedro O. Pinheiro', 'Ronan Collobert'] | 1412.8419v3 | 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... | Simple Image Description Generator via a Linear Phrase-Based Approach | 2,014 | http://arxiv.org/pdf/1412.8419v3 | Title Simple Image Description Generator via Linear PhraseBased Approach Summary Generating novel textual description image interesting problem connects computer vision natural language processing paper present simple model able generate descriptive sentence given sample image model strong focus syntax description trai... | [0.04166076332330704, 0.03131520375609398, 0.03395695239305496, 0.07672151923179626, -0.032109953463077545, 0.0065893144346773624, 0.007731662131845951, 0.014008475467562675, -0.014690528623759747, -0.08661362528800964, 0.016673613339662552, 0.01575004868209362, 0.006975891999900341, 0.06346464902162552, 0.040673106908... |
513 | 513 | ['Lin Ma', 'Zhengdong Lu', 'Lifeng Shang', 'Hang Li'] | 1504.06063v5 | 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... | Multimodal Convolutional Neural Networks for Matching Image and Sentence | 2,015 | http://arxiv.org/pdf/1504.06063v5 | Title Multimodal Convolutional Neural Networks Matching Image Sentence Summary paper propose multimodal convolutional neural network mCNNs matching image sentence mCNN provides endtoend framework convolutional architecture exploit image representation word composition matching relation two modality specifically consist... | [0.04177653044462204, 0.06474702805280685, -0.0027690697461366653, 0.08906528353691101, -0.05987587571144104, -0.008205855265259743, 0.002720879390835762, 0.026942765340209007, -0.05217777565121651, -0.05570228770375252, -0.0026668068021535873, 0.007719388231635094, -0.03881571441888809, 0.04712129756808281, 0.00684999... |
514 | 514 | ['Jacob Andreas', 'Marcus Rohrbach', 'Trevor Darrell', 'Dan Klein'] | 1601.01705v4 | 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... | Learning to Compose Neural Networks for Question Answering | 2,016 | http://arxiv.org/pdf/1601.01705v4 | Title Learning Compose Neural Networks Question Answering Summary describe question answering model applies image structured knowledge base model us natural language string automatically assemble neural network collection composable module Parameters module learned jointly networkassembly parameter via reinforcement le... | [0.0675688087940216, 0.008842003531754017, -0.012493891641497612, 0.025583632290363312, 3.815089803538285e-05, 0.02137421816587448, 0.0021996235009282827, 0.0015209135599434376, 0.00223616068251431, -0.03745744377374649, -0.006605775095522404, 0.005751275923103094, -0.02125655673444271, 0.06110196188092232, 0.015024741... |
515 | 515 | ['Taehwan Kim', 'Weiran Wang', 'Hao Tang', 'Karen Livescu'] | 1602.04278v1 | 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... | Signer-independent Fingerspelling Recognition with Deep Neural Network
Adaptation | 2,016 | http://arxiv.org/pdf/1602.04278v1 | Title Signerindependent Fingerspelling Recognition Deep Neural Network Adaptation Summary study problem recognition fingerspelled letter sequence American Sign Language signerindependent setting Fingerspelled sequence challenging important recognize used many content word proper noun technical term Previous work shown ... | [0.005663355812430382, 0.042473867535591125, 0.011422141455113888, 0.00483107753098011, -0.011012137867510319, 0.011493192985653877, 0.051666442304849625, -0.010203853249549866, 0.014243020676076412, -0.012097612023353577, -0.023107748478651047, -0.008776439353823662, 0.061435308307409286, 0.051392752677202225, 0.00125... |
516 | 516 | ['Armand Vilalta', 'Dario Garcia-Gasulla', 'Ferran Parés', 'Eduard Ayguadé', 'Jesus Labarta', 'Ulises Cortés', 'Toyotaro Suzumura'] | 1707.09872v2 | 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... | Full-Network Embedding in a Multimodal Embedding Pipeline | 2,017 | http://arxiv.org/pdf/1707.09872v2 | Title FullNetwork Embedding Multimodal Embedding Pipeline Summary current stateoftheart image annotation image retrieval task obtained deep neural network combine image representation text representation shared embedding space paper evaluate impact using FullNetwork embedding setting replacing original image representa... | [0.012527558021247387, 0.01966680958867073, -0.009844220243394375, 0.037345778197050095, -0.009412907995283604, 0.007965855300426483, 0.04769733548164368, 0.0024188843090087175, 0.0004167724691797048, -0.0063164448365569115, -0.008858357556164265, -0.02805659919977188, -0.03822551667690277, 0.03703112527728081, 0.04509... |
517 | 517 | ['Marc Tanti', 'Albert Gatt', 'Kenneth P. Camilleri'] | 1708.02043v2 | 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... | What is the Role of Recurrent Neural Networks (RNNs) in an Image Caption
Generator? | 2,017 | http://arxiv.org/pdf/1708.02043v2 | Title Role Recurrent Neural Networks RNNs Image Caption Generator Summary neural image captioning system recurrent neural network RNN typically viewed primary generation component view suggests image feature injected RNN fact dominant view literature Alternatively RNN instead viewed encoding previously generated word v... | [0.05952994152903557, 0.028533458709716797, 0.012885808013379574, 0.037569984793663025, -0.018395910039544106, 0.0034104178193956614, 0.02925185114145279, -0.025092288851737976, -0.025279764086008072, -0.05118748918175697, 0.006764248013496399, -0.03523680567741394, 0.025941558182239532, 0.06995707005262375, 0.02410381... |
518 | 518 | ['Shiliang Zhang', 'Hui Jiang', 'Mingbin Xu', 'Junfeng Hou', 'Lirong Dai'] | 1505.01504v2 | 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... | A Fixed-Size Encoding Method for Variable-Length Sequences with its
Application to Neural Network Language Models | 2,015 | http://arxiv.org/pdf/1505.01504v2 | Title FixedSize Encoding Method VariableLength Sequences Application Neural Network Language Models Summary paper propose new fixedsize ordinallyforgetting encoding FOFE method almost uniquely encode variablelength sequence word fixedsize representation FOFE model word order sequence using simple ordinallyforgetting me... | [0.020751405507326126, 0.08787958323955536, -0.0008128008921630681, 0.01903299055993557, -0.02275308594107628, -0.0176555123180151, 0.008528110571205616, 0.015340976417064667, -0.04496227949857712, -0.02266302891075611, 0.03872222453355789, -0.06334277987480164, 0.0518856905400753, 0.02693445049226284, 0.04259736463427... |
519 | 519 | ['Chris Dyer', 'Miguel Ballesteros', 'Wang Ling', 'Austin Matthews', 'Noah A. Smith'] | 1505.08075v1 | 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... | Transition-Based Dependency Parsing with Stack Long Short-Term Memory | 2,015 | http://arxiv.org/pdf/1505.08075v1 | Title TransitionBased Dependency Parsing Stack Long ShortTerm Memory Summary propose technique learning representation parser state transitionbased dependency parser primary innovation new control structure sequencetosequence neural networksthe stack LSTM Like conventional stack data structure used transitionbased pars... | [0.046386729925870895, 0.04305907338857651, -0.0023133051581680775, 0.036666374653577805, -0.03569711372256279, -0.025922877714037895, -0.02053554356098175, -0.004047680180519819, -0.011525302194058895, -0.0806916207075119, 0.03160813823342323, 0.01739772967994213, -0.00100998068228364, 0.08142969012260437, -0.02886826... |
520 | 520 | ['Ehud Ben-Reuven', 'Jacob Goldberger'] | 1604.00317v1 | 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... | A Semisupervised Approach for Language Identification based on Ladder
Networks | 2,016 | http://arxiv.org/pdf/1604.00317v1 | Title Semisupervised Approach Language Identification based Ladder Networks Summary study address problem training neuralnetwork language identification using labeled unlabeled speech sample form ivectors propose neural network architecture also handle outofset language utilize modified version recently proposed Ladder... | [-0.007939611561596394, 0.06225118413567543, 0.018980644643306732, 0.03386964648962021, -0.001068435492925346, 0.006218391470611095, 0.03935648873448372, -0.029958978295326233, 0.032825157046318054, 0.0208926722407341, -0.0561082661151886, -0.012975585646927357, 0.03465845808386803, 0.012202487327158451, 0.013990128412... |
521 | 521 | ['Awni Y. Hannun', 'Andrew L. Maas', 'Daniel Jurafsky', 'Andrew Y. Ng'] | 1408.2873v2 | 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... | First-Pass Large Vocabulary Continuous Speech Recognition using
Bi-Directional Recurrent DNNs | 2,014 | http://arxiv.org/pdf/1408.2873v2 | Title FirstPass Large Vocabulary Continuous Speech Recognition using BiDirectional Recurrent DNNs Summary present method perform firstpass large vocabulary continuous speech recognition using neural network language model Deep neural network acoustic model commonplace HMMbased speech recognition system building system ... | [-0.03477975353598595, 0.03933965042233467, 0.013139639981091022, 0.0683111846446991, -0.027946796268224716, -0.005735600367188454, 0.019245104864239693, 0.008267185650765896, -0.013944591395556927, -0.0594002865254879, -0.0265803225338459, -0.041861556470394135, 0.01546946819871664, 0.056132446974515915, -0.0185661390... |
522 | 522 | ['Jose Antonio Miñarro-Giménez', 'Oscar Marín-Alonso', 'Matthias Samwald'] | 1502.03682v1 | 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... | Applying deep learning techniques on medical corpora from the World Wide
Web: a prototypical system and evaluation | 2,015 | http://arxiv.org/pdf/1502.03682v1 | Title Applying deep learning technique medical corpus World Wide Web prototypical system evaluation Summary BACKGROUND amount biomedical literature rapidly growing becoming increasingly difficult keep manually curated knowledge base ontology uptodate study applied word2vec deep learning toolkit medical corpus test pote... | [0.04419950023293495, 0.03953353315591812, 0.009901393204927444, -0.014953557401895523, -0.00039755282341502607, 0.03156756982207298, 0.005024713464081287, 0.053299590945243835, -0.008856860920786858, -0.023115988820791245, 0.029011059552431107, -0.04097963497042656, 0.029397210106253624, 0.05877178534865379, -0.009140... |
523 | 523 | ['Mingxuan Wang', 'Zhengdong Lu', 'Hang Li', 'Qun Liu'] | 1503.02427v6 | 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... | Syntax-based Deep Matching of Short Texts | 2,015 | http://arxiv.org/pdf/1503.02427v6 | Title Syntaxbased Deep Matching Short Texts Summary Many task natural language processing ranging machine translation question answering reduced problem matching two sentence generally two short text propose new approach problem called Deep Match Tree DeepMatchtree general setting approach consists two component 1 mini... | [0.051162995398044586, 0.018305283039808273, -0.01855611428618431, 0.07697848230600357, -0.08832608908414841, -0.0020218202844262123, -0.034851547330617905, 0.02787567302584648, 0.000578019767999649, -0.05271303653717041, 0.005947253666818142, -0.01473609171807766, 0.016358161345124245, 0.0718144103884697, -0.003472416... |
524 | 524 | ['Grégoire Mesnil', 'Tomas Mikolov', "Marc'Aurelio Ranzato", 'Yoshua Bengio'] | 1412.5335v7 | 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-... | Ensemble of Generative and Discriminative Techniques for Sentiment
Analysis of Movie Reviews | 2,014 | http://arxiv.org/pdf/1412.5335v7 | Title Ensemble Generative Discriminative Techniques Sentiment Analysis Movie Reviews Summary Sentiment analysis common task natural language processing aim detect polarity text document typically consumer review simplest setting discriminate positive negative sentiment turning task standard binary classification proble... | [0.040673088282346725, 0.06416871398687363, -0.0010957929771393538, 0.022742290049791336, -0.02854306995868683, 0.02009245753288269, 0.01908349059522152, 0.005958822555840015, -0.006910823751240969, -0.06278390437364578, -0.00593555485829711, -0.03153858333826065, 0.014895033091306686, 0.03587951511144638, -0.027388690... |
525 | 525 | ['Kartik Audhkhasi', 'Abhinav Sethy', 'Bhuvana Ramabhadran'] | 1412.7063v5 | 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... | Diverse Embedding Neural Network Language Models | 2,014 | http://arxiv.org/pdf/1412.7063v5 | Title Diverse Embedding Neural Network Language Models Summary propose Diverse Embedding Neural Network DENN novel architecture language model LMs DENNLM project input word history vector onto multiple diverse lowdimensional subspace instead single higherdimensional subspace conventional feedforward neural network LMs ... | [0.02236238308250904, 0.04809650033712387, -0.011370988562703133, 0.04160626605153084, -0.0006663855165243149, 0.004996190778911114, 0.03278935328125954, -0.01274781208485365, 0.010790622793138027, -0.03399166464805603, -0.0007355916313827038, -0.057956546545028687, 0.020329143851995468, 0.041717637330293655, 0.0400969... |
526 | 526 | ['Dimitri Palaz', 'Mathew Magimai Doss', 'Ronan Collobert'] | 1412.7110v6 | 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... | Learning linearly separable features for speech recognition using
convolutional neural networks | 2,014 | http://arxiv.org/pdf/1412.7110v6 | Title Learning linearly separable feature speech recognition using convolutional neural network Summary Automatic speech recognition system usually rely spectralbased feature MFCC PLP feature extracted based prior knowledge speech perception orand speech production Recently convolutional neural network shown able estim... | [0.00563886109739542, 0.029790548607707024, 0.021793151274323463, 0.07984942197799683, 0.009085021913051605, -0.007258158177137375, 0.05035756155848503, 0.01637815125286579, 0.012337390333414078, -0.014996013604104519, -0.040835052728652954, 0.02297447808086872, -0.0020683889742940664, 0.07877500355243683, 0.0013255557... |
527 | 527 | ['Edward Grefenstette', 'Karl Moritz Hermann', 'Mustafa Suleyman', 'Phil Blunsom'] | 1506.02516v3 | 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... | Learning to Transduce with Unbounded Memory | 2,015 | http://arxiv.org/pdf/1506.02516v3 | Title Learning Transduce Unbounded Memory Summary Recently strong result demonstrated Deep Recurrent Neural Networks natural language transduction problem paper explore representational power model using synthetic grammar designed exhibit phenomenon similar found real transduction problem machine translation experiment... | [0.020350556820631027, 0.008127058856189251, -0.015597475692629814, 0.05192788690328598, -0.00637725880369544, 0.00024154184211511165, -0.026283787563443184, -0.016088377684354782, -0.04758871719241142, -0.03196248784661293, 0.002205190947279334, -0.05149921402335167, -0.00998506136238575, 0.036861661821603775, 0.02149... |
528 | 528 | ['ShiLiang Zhang', 'Hui Jiang', 'Si Wei', 'LiRong Dai'] | 1510.02693v1 | 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... | Feedforward Sequential Memory Neural Networks without Recurrent Feedback | 2,015 | http://arxiv.org/pdf/1510.02693v1 | Title Feedforward Sequential Memory Neural Networks without Recurrent Feedback Summary introduce new structure memory neural network called feedforward sequential memory network FSMN learn longterm dependency without using recurrent feedback proposed FSMN standard feedforward neural network equipped learnable sequentia... | [0.0170398261398077, 0.02921907790005207, 0.0004469997074920684, 0.053692590445280075, -0.015155096538364887, -0.004730031359940767, 0.001771857962012291, -0.0001782434555934742, 0.039970770478248596, -0.026382112875580788, -0.013815529644489288, -0.04723210632801056, 0.022966090589761734, 0.041136160492897034, 0.00424... |
529 | 529 | ['Yi-Hsiu Liao', 'Hung-yi Lee', 'Lin-shan Lee'] | 1511.02506v1 | 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... | Towards Structured Deep Neural Network for Automatic Speech Recognition | 2,015 | http://arxiv.org/pdf/1511.02506v1 | Title Towards Structured Deep Neural Network Automatic Speech Recognition Summary paper propose Structured Deep Neural Network structured DNN structured deep learning framework approach learn find best structured object label sequence given structured input vector sequence globally considering mapping relationship stru... | [-0.020786505192518234, 0.031145649030804634, 0.023787403479218483, 0.03606473654508591, 0.007402504328638315, -0.011239790357649326, 0.030399184674024582, -0.007328341715037823, 0.006598798092454672, -0.012059276923537254, -0.017939789220690727, -0.0021720367949455976, 0.022716106846928596, 0.07752376049757004, -0.006... |
530 | 530 | ['Kyuyeon Hwang', 'Wonyong Sung'] | 1601.06581v2 | 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... | Character-Level Incremental Speech Recognition with Recurrent Neural
Networks | 2,016 | http://arxiv.org/pdf/1601.06581v2 | Title CharacterLevel Incremental Speech Recognition Recurrent Neural Networks Summary realtime speech recognition application latency important issue developed characterlevel incremental speech recognition ISR system responds quickly even speech hypothesis gradually improved speaking proceeds algorithm employ speechtoc... | [0.0044223531149327755, 0.05571771040558815, 0.03744976967573166, 0.038522932678461075, -0.008043334819376469, -0.006230231840163469, 0.005893508903682232, -0.0026893592439591885, -0.051229141652584076, -0.003056955523788929, -0.018325883895158768, -0.04175221920013428, 0.06111201271414757, 0.07964888960123062, -0.0042... |
531 | 531 | ['Daniel Andor', 'Chris Alberti', 'David Weiss', 'Aliaksei Severyn', 'Alessandro Presta', 'Kuzman Ganchev', 'Slav Petrov', 'Michael Collins'] | 1603.06042v2 | 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... | Globally Normalized Transition-Based Neural Networks | 2,016 | http://arxiv.org/pdf/1603.06042v2 | Title Globally Normalized TransitionBased Neural Networks Summary introduce globally normalized transitionbased neural network model achieves stateoftheart partofspeech tagging dependency parsing sentence compression result model simple feedforward neural network operates taskspecific transition system yet achieves com... | [-0.0018425033194944263, 0.03360210731625557, 0.004456736147403717, 6.53143142699264e-05, -0.022016210481524467, -0.017721550539135933, 0.009323908016085625, -0.012625712901353836, -0.015643484890460968, 0.0013822111068293452, -0.0024533974938094616, 0.024322573095560074, -0.011615287512540817, 0.07487696409225464, -0.... |
532 | 532 | ['Peng Li', 'Heng Huang'] | 1603.09381v1 | 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)... | Clinical Information Extraction via Convolutional Neural Network | 2,016 | http://arxiv.org/pdf/1603.09381v1 | Title Clinical Information Extraction via Convolutional Neural Network Summary report implementation clinical information extraction tool leverage deep neural network annotate event span attribute raw clinical note pathology report approach us context word partofspeech tag shape information feature hire temporal 1D con... | [0.029269743710756302, 0.0399782657623291, 0.013260511681437492, 0.0400710366666317, -0.011499225161969662, 0.03400396928191185, -0.0003108444798272103, 0.03770548105239868, -0.002720797434449196, 0.00375669589266181, -0.0022862101905047894, -0.023347411304712296, -0.009924249723553658, 0.09361836314201355, -0.00325862... |
533 | 533 | ['David Krueger', 'Tegan Maharaj', 'János Kramár', 'Mohammad Pezeshki', 'Nicolas Ballas', 'Nan Rosemary Ke', 'Anirudh Goyal', 'Yoshua Bengio', 'Aaron Courville', 'Chris Pal'] | 1606.01305v4 | 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... | Zoneout: Regularizing RNNs by Randomly Preserving Hidden Activations | 2,016 | http://arxiv.org/pdf/1606.01305v4 | Title Zoneout Regularizing RNNs Randomly Preserving Hidden Activations Summary propose zoneout novel method regularizing RNNs timestep zoneout stochastically force hidden unit maintain previous value Like dropout zoneout us random noise train pseudoensemble improving generalization preserving instead dropping hidden un... | [0.019840899854898453, 0.05786997452378273, 0.011844219639897346, 0.013934220187366009, -0.03447861969470978, -0.015363059937953949, 0.04581072926521301, 0.005135738290846348, -0.029061265289783478, 0.005191651172935963, -0.04723426699638367, -0.0017743544885888696, 0.02767947129905224, 0.02805199846625328, 0.005984567... |
534 | 534 | ['Isabelle Augenstein', 'Tim Rocktäschel', 'Andreas Vlachos', 'Kalina Bontcheva'] | 1606.05464v2 | 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... | Stance Detection with Bidirectional Conditional Encoding | 2,016 | http://arxiv.org/pdf/1606.05464v2 | Title Stance Detection Bidirectional Conditional Encoding Summary Stance detection task classifying attitude expressed text towards target Hillary Clinton positive negative neutral Previous work assumed either target mentioned text training data every target given paper considers challenging version task target always ... | [0.024833083152770996, 0.0354040190577507, 0.015504857525229454, 0.05491749569773674, -0.017649494111537933, 0.036230795085430145, -0.014465161599218845, 0.01846902258694172, -0.0023728450760245323, -0.08561266958713531, -0.0020045368000864983, -0.037640057504177094, -0.049539320170879364, 0.03160585090517998, -0.01427... |
535 | 535 | ['Noura Al Moubayed', 'Toby Breckon', 'Peter Matthews', 'A. Stephen McGough'] | 1606.05554v1 | 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... | SMS Spam Filtering using Probabilistic Topic Modelling and Stacked
Denoising Autoencoder | 2,016 | http://arxiv.org/pdf/1606.05554v1 | Title SMS Spam Filtering using Probabilistic Topic Modelling Stacked Denoising Autoencoder Summary paper present novel approach spam filtering demonstrate applicability respect SMS message approach requires minimum feature engineering small set la belled data sample Features extracted using topic modelling based latent... | [0.07865723967552185, -0.014085316099226475, -0.03809920698404312, 0.018198154866695404, -0.04089958220720291, 0.0033303394448012114, 0.027079053223133087, -0.013853617012500763, -0.0011168353958055377, -0.06289788335561752, 0.018436282873153687, 0.0021972579415887594, -0.010859817266464233, 0.07428963482379913, -0.055... |
536 | 536 | ['Abhyuday Jagannatha', 'Hong Yu'] | 1606.07953v2 | 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... | Bidirectional Recurrent Neural Networks for Medical Event Detection in
Electronic Health Records | 2,016 | http://arxiv.org/pdf/1606.07953v2 | Title Bidirectional Recurrent Neural Networks Medical Event Detection Electronic Health Records Summary Sequence labeling extraction medical event attribute unstructured text Electronic Health Record EHR note key step towards semantic understanding EHRs important application health informatics including pharmacovigilan... | [0.028854914009571075, 0.021569009870290756, 0.02300029993057251, 0.0031422856263816357, -0.017468098551034927, 0.02254670299589634, -0.015461267903447151, 0.02820962853729725, 0.019318534061312675, -0.03862966224551201, 0.07021252065896988, -0.047684554010629654, -0.005900430493056774, 0.0902818888425827, -0.012088811... |
537 | 537 | ['Liang Lu'] | 1607.01963v5 | 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, ... | Sequence Training and Adaptation of Highway Deep Neural Networks | 2,016 | http://arxiv.org/pdf/1607.01963v5 | Title Sequence Training Adaptation Highway Deep Neural Networks Summary Highway deep neural network HDNN type depthgated feedforward neural network shown easier train hidden layer also generalise better compared conventional plain deep neural network DNNs Previously investigated structured HDNN architecture speech reco... | [0.008032679557800293, 0.06332380324602127, -0.0070065963082015514, 0.0451037734746933, 0.0027463044971227646, 0.0019146653357893229, 0.04481321945786476, 0.0075764842331409454, 0.0069970544427633286, -0.04749356210231781, -0.031650710850954056, -0.0027913234662264585, 0.024920636788010597, 0.032488442957401276, 0.0068... |
538 | 538 | ['Julian Georg Zilly', 'Rupesh Kumar Srivastava', 'Jan Koutník', 'Jürgen Schmidhuber'] | 1607.03474v5 | 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... | Recurrent Highway Networks | 2,016 | http://arxiv.org/pdf/1607.03474v5 | Title Recurrent Highway Networks Summary Many sequential processing task require complex nonlinear transition function one step next However recurrent neural network deep transition function remain difficult train even using Long ShortTerm Memory LSTM network introduce novel theoretical analysis recurrent network based... | [0.009730732999742031, 0.05000156909227371, -0.009580492973327637, 0.02807185985147953, -0.009045728482306004, 0.009133345447480679, -0.005226182285696268, 0.01703661121428013, -0.022015279158949852, -0.00795978307723999, 0.033901527523994446, -0.0669899731874466, 0.029679475352168083, 0.04942607879638672, -0.007268294... |
539 | 539 | ['Antonio Valerio Miceli Barone'] | 1608.02996v1 | 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... | Towards cross-lingual distributed representations without parallel text
trained with adversarial autoencoders | 2,016 | http://arxiv.org/pdf/1608.02996v1 | Title Towards crosslingual distributed representation without parallel text trained adversarial autoencoders Summary Current approach learning vector representation text compatible different language usually require amount parallel text aligned word sentence least document level hypothesize however different natural la... | [-0.002654540818184614, 0.029592353850603104, -0.020501552149653435, 0.08953430503606796, -0.05177861452102661, 0.015171200037002563, 0.030116500332951546, -0.03203490003943443, 0.01959400065243244, -0.04761359840631485, -0.051239270716905594, -0.00202724477276206, 0.045413024723529816, -0.007184742484241724, 0.0145744... |
540 | 540 | ['Zhiyuan Tang', 'Ying Shi', 'Dong Wang', 'Yang Feng', 'Shiyue Zhang'] | 1609.08789v3 | 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 ... | Memory Visualization for Gated Recurrent Neural Networks in Speech
Recognition | 2,016 | http://arxiv.org/pdf/1609.08789v3 | Title Memory Visualization Gated Recurrent Neural Networks Speech Recognition Summary Recurrent neural network RNNs shown clear superiority sequence modeling particularly one gated unit long shortterm memory LSTM gated recurrent unit GRU However dynamic property behind remarkable performance remain unclear many applica... | [-0.006197511218488216, 0.0016232029302045703, -0.010341375134885311, 0.04599903151392937, -0.024055778980255127, -0.005096917040646076, 0.060437288135290146, -0.02678726799786091, -0.015349045395851135, 0.0017848003190010786, -0.032852910459041595, -0.04186113923788071, 0.05244435369968414, 0.05691493675112724, 0.0358... |
541 | 541 | ['Hagen Soltau', 'Hank Liao', 'Hasim Sak'] | 1610.09975v1 | 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,... | Neural Speech Recognizer: Acoustic-to-Word LSTM Model for Large
Vocabulary Speech Recognition | 2,016 | http://arxiv.org/pdf/1610.09975v1 | Title Neural Speech Recognizer AcoustictoWord LSTM Model Large Vocabulary Speech Recognition Summary present result show possible build competitive greatly simplified large vocabulary continuous speech recognition system whole word acoustic unit model output vocabulary 100000 word directly using deep bidirectional LSTM... | [-0.004185554105788469, 0.043656352907419205, 0.04244375228881836, 0.09234080463647842, -0.003420830937102437, 0.00681381905451417, 0.03762492164969444, 0.013959381729364395, -0.03840254992246628, -0.021074889227747917, -0.0640619620680809, -0.03905227780342102, 0.03335243836045265, 0.04141192510724068, 0.0106396749615... |
542 | 542 | ['Prajit Ramachandran', 'Peter J. Liu', 'Quoc V. Le'] | 1611.02683v2 | 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... | Unsupervised Pretraining for Sequence to Sequence Learning | 2,016 | http://arxiv.org/pdf/1611.02683v2 | Title Unsupervised Pretraining Sequence Sequence Learning Summary work present general unsupervised learning method improve accuracy sequence sequence seq2seq model method weight encoder decoder seq2seq model initialized pretrained weight two language model finetuned labeled data apply method challenging benchmark mach... | [0.002003306522965431, 0.06992283463478088, -0.011045689694583416, 0.01799277774989605, -0.0049300226382911205, 0.02622196078300476, 0.021231332793831825, -0.005262626335024834, -0.029905002564191818, -0.045856647193431854, -0.029438117519021034, -0.013366500847041607, 0.030526725575327873, 0.03710099309682846, 0.01927... |
543 | 543 | ['Yoon Kim', 'Carl Denton', 'Luong Hoang', 'Alexander M. Rush'] | 1702.00887v3 | 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,... | Structured Attention Networks | 2,017 | http://arxiv.org/pdf/1702.00887v3 | Title Structured Attention Networks Summary Attention network proven effective approach embedding categorical inference within deep neural network However many task may want model richer structural dependency without abandoning endtoend training work experiment incorporating richer structural distribution encoded using... | [0.026088450103998184, 0.0376410074532032, -0.01531126070767641, 0.04309573397040367, -0.001533530536107719, -0.003978844732046127, -0.0019318856066092849, -0.026550261303782463, -0.006426883861422539, -0.05096250772476196, -0.009327799081802368, 0.032912496477365494, -0.0024358744267374277, 0.05665376037359238, 0.0270... |
544 | 544 | ['Hongyu Guo', 'Colin Cherry', 'Jiang Su'] | 1704.05907v1 | 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... | End-to-End Multi-View Networks for Text Classification | 2,017 | http://arxiv.org/pdf/1704.05907v1 | Title EndtoEnd MultiView Networks Text Classification Summary propose multiview network text classification method automatically creates various view input text taking form soft attention weight distribute classifier focus among set base feature bagofwords representation view focus different subset text word Aggregatin... | [0.025872070342302322, -0.006213345564901829, 0.024412499740719795, 0.04621824249625206, -0.030140504240989685, 0.004059010650962591, 0.03245772048830986, 0.012296872213482857, 0.04079432040452957, -0.06887512654066086, -0.033552028238773346, -0.011995146051049232, 0.011675628833472729, 0.04331379756331444, 0.003311428... |
545 | 545 | ['Kartik Goyal', 'Chris Dyer', 'Taylor Berg-Kirkpatrick'] | 1704.06970v1 | 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... | Differentiable Scheduled Sampling for Credit Assignment | 2,017 | http://arxiv.org/pdf/1704.06970v1 | Title Differentiable Scheduled Sampling Credit Assignment Summary demonstrate continuous relaxation argmax operation used create differentiable approximation greedy decoding sequencetosequence seq2seq model incorporating approximation scheduled sampling training procedure Bengio et al 2015a wellknown technique correcti... | [0.04359620064496994, 0.042325232177972794, 0.006403833162039518, 0.015020525082945824, 0.009850161150097847, 0.05608697980642319, -0.016278481110930443, 0.005217379424721003, -0.014566252939403057, -0.017947543412446976, -0.022926131263375282, -0.0151705052703619, 0.018968705087900162, -0.028447363525629044, 0.0015770... |
546 | 546 | ['Zhiyuan Tang', 'Dong Wang', 'Yixiang Chen', 'Ying Shi', 'Lantian Li'] | 1705.03152v2 | 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... | Phone-aware Neural Language Identification | 2,017 | http://arxiv.org/pdf/1705.03152v2 | Title Phoneaware Neural Language Identification Summary Pure acoustic neural model particularly LSTMRNN model shown great potential language identification LID However phonetic information largely overlooked existing neural LID model although information used conventional phonetic LID system great success present phone... | [0.01298956386744976, 0.08186398446559906, 0.015031697228550911, 0.04583071172237396, -0.013382386416196823, 0.01289944164454937, 0.055056098848581314, -0.01047565694898367, -0.016969528049230576, -0.0007712991209700704, -0.06293878704309464, -0.04870281741023064, 0.05759785696864128, 0.0004911783616989851, 0.043796192... |
547 | 547 | ['Marek Rei'] | 1707.05233v1 | 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... | Detecting Off-topic Responses to Visual Prompts | 2,017 | http://arxiv.org/pdf/1707.05233v1 | Title Detecting Offtopic Responses Visual Prompts Summary Automated method essay scoring made great progress recent year achieving accuracy close human annotator However known weakness automated scorer taking account semantic relevance submitted text existing work detecting answer relevance given textual prompt little ... | [0.10225941240787506, -0.0015375548973679543, -0.01723756641149521, 0.03070058673620224, -0.0012213430600240827, 0.011814624071121216, 0.002692685928195715, 0.06981052458286285, 0.017407312989234924, -0.04312202334403992, 0.012537005357444286, 0.007268738467246294, -0.010276996530592442, 0.06191529333591461, -0.0303712... |
548 | 548 | ['Fréderic Godin', 'Jonas Degrave', 'Joni Dambre', 'Wesley De Neve'] | 1707.08214v2 | 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) ... | Dual Rectified Linear Units (DReLUs): A Replacement for Tanh Activation
Functions in Quasi-Recurrent Neural Networks | 2,017 | http://arxiv.org/pdf/1707.08214v2 | Title Dual Rectified Linear Units DReLUs Replacement Tanh Activation Functions QuasiRecurrent Neural Networks Summary paper introduce novel type Rectified Linear Unit ReLU called Dual Rectified Linear Unit DReLU DReLU come unbounded positive negative image used dropin replacement tanh activation function recurrent step... | [0.0021918551065027714, 0.008251101709902287, 0.01522910874336958, 0.04784897714853287, -0.017525488510727882, 0.005926362704485655, 0.02409275807440281, -0.007333395536988974, -0.02322854846715927, -0.036688949912786484, -0.053567834198474884, -0.06477624177932739, 0.007544497959315777, 0.07392292469739914, -0.0106450... |
549 | 549 | ['Mostafa Dehghani', 'Arash Mehrjou', 'Stephan Gouws', 'Jaap Kamps', 'Bernhard Schölkopf'] | 1711.02799v1 | 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... | Fidelity-Weighted Learning | 2,017 | http://arxiv.org/pdf/1711.02799v1 | Title FidelityWeighted Learning Summary Training deep neural network requires many training sample practice training label expensive obtain may varying quality may trusted expert labelers others might heuristic source weak supervision crowdsourcing creates fundamental quality versusquantity tradeoff learning process le... | [0.05056807026267052, 0.05870668217539787, -0.02262929268181324, 0.04009771719574928, 0.021441899240016937, -0.03986431658267975, 0.03014550544321537, -0.03484506532549858, -0.014632077887654305, -0.01139342412352562, -0.04413081333041191, -0.0247972309589386, 0.007972529157996178, 0.057504575699567795, 0.0095823891460... |
550 | 550 | ['Dong Yu', 'Michael L. Seltzer', 'Jinyu Li', 'Jui-Ting Huang', 'Frank Seide'] | 1301.3605v3 | 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 ... | Feature Learning in Deep Neural Networks - Studies on Speech Recognition
Tasks | 2,013 | http://arxiv.org/pdf/1301.3605v3 | Title Feature Learning Deep Neural Networks Studies Speech Recognition Tasks Summary Recent study shown deep neural network DNNs perform significantly better shallow network Gaussian mixture model GMMs large vocabulary speech recognition task paper argue improved accuracy achieved DNNs result ability extract discrimina... | [-0.017452381551265717, -0.002058636164292693, 0.0036066260654479265, 0.03968363255262375, -0.007213184610009193, -0.017433112487196922, 0.05981472134590149, -0.017009399831295013, -0.04588581249117851, 0.01918136700987816, -0.041604217141866684, 0.01103188656270504, 0.04672212898731232, 0.03411168232560158, 0.01186550... |
551 | 551 | ['Dimitri Palaz', 'Ronan Collobert', 'Mathew Magimai. -Doss'] | 1304.1018v2 | 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... | Estimating Phoneme Class Conditional Probabilities from Raw Speech
Signal using Convolutional Neural Networks | 2,013 | http://arxiv.org/pdf/1304.1018v2 | Title Estimating Phoneme Class Conditional Probabilities Raw Speech Signal using Convolutional Neural Networks Summary hybrid hidden Markov modelartificial neural network HMMANN automatic speech recognition ASR system phoneme class conditional probability estimated first extracting acoustic feature speech signal based ... | [0.013593069277703762, 0.04590294510126114, 0.017292046919465065, 0.0597132183611393, 0.004294393118470907, -0.019776178523898125, 0.04187477007508278, 0.006013117264956236, -0.034289129078388214, -0.03814668580889702, -0.0364546962082386, -0.013362758792936802, 0.01716701127588749, 0.03846025466918945, 0.0041391011327... |
552 | 552 | ['Samuel R. Bowman', 'Christopher Potts', 'Christopher D. Manning'] | 1406.1827v4 | 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... | Recursive Neural Networks Can Learn Logical Semantics | 2,014 | http://arxiv.org/pdf/1406.1827v4 | Title Recursive Neural Networks Learn Logical Semantics Summary Treestructured recursive neural network TreeRNNs sentence meaning successful many application remains open question whether fixedlength representation learn support task demanding logical deduction pursue question evaluating whether two modelsplain TreeRNN... | [0.04599861800670624, 0.04760322719812393, -0.021887384355068207, 0.05416524410247803, -0.05406181514263153, 0.007405961398035288, -0.010208075866103172, -0.01070459932088852, 0.0029791484121233225, -0.04488600790500641, 0.039833322167396545, 0.03173527494072914, 0.006863091140985489, 0.051395632326602936, 0.0032243358... |
553 | 553 | ['Chenxi Zhu', 'Xipeng Qiu', 'Xinchi Chen', 'Xuanjing Huang'] | 1505.05667v1 | 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... | A Re-ranking Model for Dependency Parser with Recursive Convolutional
Neural Network | 2,015 | http://arxiv.org/pdf/1505.05667v1 | Title Reranking Model Dependency Parser Recursive Convolutional Neural Network Summary work address problem model node word phrase dependency tree dense representation propose recursive convolutional neural network RCNN architecture capture syntactic compositionalsemantic representation phrase word dependency tree Diff... | [0.058216795325279236, 0.036891523748636246, 0.007242720574140549, 0.09531322866678238, -0.04551072418689728, -2.6305309802410193e-05, -0.04743984714150429, -0.008480262942612171, -0.007531933486461639, -0.03914311155676842, 0.0289884265512228, 0.016147201880812645, 0.030944496393203735, 0.022281188517808914, -0.046962... |
554 | 554 | ['Lantian Li', 'Dong Wang', 'Zhiyong Zhang', 'Thomas Fang Zheng'] | 1505.06427v1 | 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 ... | Deep Speaker Vectors for Semi Text-independent Speaker Verification | 2,015 | http://arxiv.org/pdf/1505.06427v1 | Title Deep Speaker Vectors Semi Textindependent Speaker Verification Summary Recent research show deep neural network DNNs used extract deep speaker vector dvectors preserve speaker characteristic used speaker verification new method tested textdependent speaker verification task improvement reported combined conventio... | [-0.004094146192073822, 0.025912322103977203, -0.00012574568972922862, 0.06336522102355957, -0.013480176217854023, 0.013753141276538372, 0.05886764079332352, -0.011309025809168816, -0.015574604272842407, -0.005354780238121748, -0.0016111833974719048, -0.03165379539132118, 0.02726726606488228, -0.008409736678004265, 0.0... |
555 | 555 | ['Tom Sercu', 'Vaibhava Goel'] | 1604.01792v2 | 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... | Advances in Very Deep Convolutional Neural Networks for LVCSR | 2,016 | http://arxiv.org/pdf/1604.01792v2 | Title Advances Deep Convolutional Neural Networks LVCSR Summary deep CNNs small 3x3 kernel recently shown achieve strong performance acoustic model hybrid NNHMM speech recognition system paper investigate efficiently scale model larger datasets Specifically address design choice pooling padding along time dimension ren... | [-0.0027890349738299847, 0.029791463166475296, 0.027223581448197365, 0.0857696682214737, 0.010207554325461388, -0.03195658326148987, 0.05236981064081192, -0.02051599696278572, -0.027545113116502762, 0.01005551777780056, -0.06834647059440613, -0.022901779040694237, 0.012852547690272331, 0.035293564200401306, 0.023511055... |
556 | 556 | ['Zhiyun Lu', 'Vikas Sindhwani', 'Tara N. Sainath'] | 1604.02594v1 | 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... | Learning Compact Recurrent Neural Networks | 2,016 | http://arxiv.org/pdf/1604.02594v1 | Title Learning Compact Recurrent Neural Networks Summary Recurrent neural network RNNs including long shortterm memory LSTM RNNs produced stateoftheart result variety speech recognition task However model often large size deployment mobile device memory latency constraint work study mechanism learning compact RNNs LSTM... | [-0.0004871900600846857, -0.004935301840305328, 0.0123648876324296, 0.05660497397184372, 0.00821257196366787, -0.031614307314157486, 0.04056230187416077, -0.0007642898708581924, -0.02976626344025135, -0.010106392204761505, -0.04787039756774902, -0.03563445433974266, 0.03354519233107567, -0.0073138996958732605, -0.01384... |
557 | 557 | ['Adhiguna Kuncoro', 'Yuichiro Sawai', 'Kevin Duh', 'Yuji Matsumoto'] | 1604.06529v2 | 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... | Dependency Parsing with LSTMs: An Empirical Evaluation | 2,016 | http://arxiv.org/pdf/1604.06529v2 | Title Dependency Parsing LSTMs Empirical Evaluation Summary propose transitionbased dependency parser using Recurrent Neural Networks Long ShortTerm Memory LSTM unit extends feedforward neural network parser Chen Manning 2014 enables modelling entire sequence shiftreduce transition decision Google Web Treebank LSTM par... | [0.029366113245487213, 0.05464070290327072, -0.003604850498959422, 0.047632377594709396, -0.03991984203457832, -0.0028011994436383247, -0.025996096432209015, -0.013133931905031204, -0.025421075522899628, -0.042501360177993774, 0.03368372842669487, -0.010501038283109665, 0.012196731753647327, 0.03819626569747925, -0.043... |
558 | 558 | ['Hamid Palangi', 'Li Deng', 'Yelong Shen', 'Jianfeng Gao', 'Xiaodong He', 'Jianshu Chen', 'Xinying Song', 'Rabab Ward'] | 1502.06922v3 | 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... | Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis
and Application to Information Retrieval | 2,015 | http://arxiv.org/pdf/1502.06922v3 | Title Deep Sentence Embedding Using Long ShortTerm Memory Networks Analysis Application Information Retrieval Summary paper develops model address sentence embedding hot topic current natural language processing research using recurrent neural network Long ShortTerm Memory LSTM cell Due ability capture long term memory... | [0.03156965970993042, -0.04089318588376045, -0.008375040255486965, 0.05650141090154648, -0.025587432086467743, 0.006287709344178438, 0.002414876129478216, -0.021042458713054657, 0.010764251463115215, -0.06723520904779434, -0.004185568541288376, 0.003493493190035224, -0.010016089305281639, 0.012699143029749393, 0.018240... |
559 | 559 | ['Fandong Meng', 'Zhengdong Lu', 'Mingxuan Wang', 'Hang Li', 'Wenbin Jiang', 'Qun Liu'] | 1503.01838v5 | 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... | Encoding Source Language with Convolutional Neural Network for Machine
Translation | 2,015 | http://arxiv.org/pdf/1503.01838v5 | Title Encoding Source Language Convolutional Neural Network Machine Translation Summary recently proposed neural network joint model NNJM Devlin et al 2014 augments ngram target language model heuristically chosen source context window achieving stateoftheart performance SMT paper give systematic treatment summarizing ... | [0.05548024922609329, 0.047425027936697006, 0.014198657125234604, 0.07506497204303741, -0.03541373834013939, 0.012434015981853008, 0.039030659943819046, 0.004686171654611826, -0.07581836730241776, -0.029282188042998314, -0.05318759009242058, -0.02025393396615982, 0.005503562744706869, 0.04283221811056137, 0.02397718653... |
560 | 560 | ['Zhen Huang', 'Sabato Marco Siniscalchi', 'I-Fan Chen', 'Jiadong Wu', 'Chin-Hui Lee'] | 1503.02108v2 | 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... | Maximum a Posteriori Adaptation of Network Parameters in Deep Models | 2,015 | http://arxiv.org/pdf/1503.02108v2 | Title Maximum Posteriori Adaptation Network Parameters Deep Models Summary present Bayesian approach adapting parameter welltrained contextdependent deepneuralnetwork hidden Markov model CDDNNHMM improve automatic speech recognition performance Given abundance DNN parameter limited amount data effectiveness adapted DNN... | [0.016376910731196404, 0.034700606018304825, -0.0065205697901546955, 0.003025778802111745, 0.021287813782691956, 0.020427532494068146, 0.04004742205142975, -0.012555258348584175, -0.020050641149282455, -0.05148341506719589, -0.06654325127601624, 0.03062780201435089, 0.030081646516919136, 0.010403763502836227, 0.0018954... |
561 | 561 | ['Zhaopeng Tu', 'Baotian Hu', 'Zhengdong Lu', 'Hang Li'] | 1503.02357v2 | 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... | Context-Dependent Translation Selection Using Convolutional Neural
Network | 2,015 | http://arxiv.org/pdf/1503.02357v2 | Title ContextDependent Translation Selection Using Convolutional Neural Network Summary propose novel method translation selection statistical machine translation convolutional neural network employed judge similarity phrase pair two language specifically designed convolutional architecture encodes semantic similarity ... | [0.057121142745018005, 0.03913121297955513, -0.013562625274062157, 0.09125379472970963, -0.055765941739082336, 0.009166888892650604, 0.0394693985581398, 0.009660053066909313, -0.05678289011120796, -0.04626738280057907, -0.053807344287633896, 0.005128434393554926, 0.017527494579553604, -0.03251349925994873, 0.0097431940... |
562 | 562 | ['Baotian Hu', 'Zhengdong Lu', 'Hang Li', 'Qingcai Chen'] | 1503.03244v1 | 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... | Convolutional Neural Network Architectures for Matching Natural Language
Sentences | 2,015 | http://arxiv.org/pdf/1503.03244v1 | Title Convolutional Neural Network Architectures Matching Natural Language Sentences Summary Semantic matching central importance many natural language task citebordes2014semanticRetrievalQA successful matching algorithm need adequately model internal structure language object interaction step toward goal propose convo... | [0.07127247750759125, 0.04071514308452606, 7.114599429769441e-05, 0.10387472808361053, -0.0757235735654831, -0.0007980315713211894, -0.02864399179816246, 0.0014175277901813388, -0.009861437603831291, -0.039536718279123306, 0.001107489806599915, 0.0004916680045425892, 0.007699381094425917, 0.038788337260484695, 0.008469... |
563 | 563 | ['Xiaodan Zhu', 'Parinaz Sobhani', 'Hongyu Guo'] | 1503.04881v1 | 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 ... | Long Short-Term Memory Over Tree Structures | 2,015 | http://arxiv.org/pdf/1503.04881v1 | Title Long ShortTerm Memory Tree Structures Summary chainstructured long shortterm memory LSTM showed effective wide range problem speech recognition machine translation paper propose extend tree structure memory cell reflect history memory multiple child cell multiple descendant cell recursive process call model SLSTM... | [0.021890904754400253, -0.0186021588742733, 0.0105922631919384, 0.08318793773651123, -0.003978133667260408, 0.0044142757542431355, -6.259512883843854e-05, -0.010849968530237675, -0.04144003242254257, -0.06341680139303207, -0.040655747056007385, -0.06472526490688324, 0.057167090475559235, 0.030477045103907585, -0.018740... |
564 | 564 | ['Krupakar Hans', 'R S Milton'] | 1612.02482v2 | 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... | Improving the Performance of Neural Machine Translation Involving
Morphologically Rich Languages | 2,016 | http://arxiv.org/pdf/1612.02482v2 | Title Improving Performance Neural Machine Translation Involving Morphologically Rich Languages Summary advent attention mechanism neural machine translation model improved performance machine translation system enabling selective lookup source sentence paper efficiency translation using bidirectional encoder attention... | [0.06208235025405884, 0.00661317678168416, -0.010848728008568287, 0.06465115398168564, -0.039646659046411514, -0.007100379094481468, -0.01967349275946617, 0.021981865167617798, -0.09096962958574295, -0.04573608934879303, -0.03098827600479126, -0.046243924647569656, 0.05132283642888069, 0.01270466111600399, 0.0617354027... |
565 | 565 | ['Thomas Laurent', 'James von Brecht'] | 1612.06212v1 | 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... | A recurrent neural network without chaos | 2,016 | http://arxiv.org/pdf/1612.06212v1 | Title recurrent neural network without chaos Summary introduce exceptionally simple gated recurrent neural network RNN achieves performance comparable wellknown gated architecture LSTMs GRUs wordlevel language modeling task prove model simple predicable nonchaotic dynamic stand stark contrast standard gated architectur... | [-0.014985080808401108, 0.029927542433142662, -0.028112288564443588, 0.030175624415278435, -0.020678887143731117, -0.00834645051509142, -0.011060236021876335, -0.013258378021419048, -0.03712480887770653, -0.0036131013184785843, 0.022630540654063225, -0.04988980293273926, -0.00441202474758029, 0.07946450263261795, 0.018... |
566 | 566 | ['Dimitri Palaz', 'Ronan Collobert', 'Mathew Magimai. -Doss'] | 1312.2137v1 | 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-... | End-to-end Phoneme Sequence Recognition using Convolutional Neural
Networks | 2,013 | http://arxiv.org/pdf/1312.2137v1 | Title Endtoend Phoneme Sequence Recognition using Convolutional Neural Networks Summary phoneme recognition stateoftheart system rely classical neural network classifier fed highly tuned feature MFCC PLP feature Recent advance deep learning approach questioned system attempt made simpler feature spectrogram stateofthea... | [0.026019861921668053, 0.06793640553951263, 0.024221213534474373, 0.06060829758644104, 0.027038084343075752, 0.00023603998124599457, 0.04196571558713913, 0.021477218717336655, -0.02096324786543846, 0.010637312196195126, -0.028192345052957535, -0.03161724656820297, 0.03874414414167404, 0.05420498922467232, 0.01417389232... |
567 | 567 | ['Prasanna Kumar Muthukumar', 'Alan W. Black'] | 1409.8558v1 | 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... | A Deep Learning Approach to Data-driven Parameterizations for
Statistical Parametric Speech Synthesis | 2,014 | http://arxiv.org/pdf/1409.8558v1 | Title Deep Learning Approach Datadriven Parameterizations Statistical Parametric Speech Synthesis Summary Nearly Statistical Parametric Speech Synthesizers today use Mel Cepstral coefficient vocal tract parameterization speech signal Mel Cepstral coefficient never intended work parametric speech synthesis framework yet... | [-0.026742789894342422, 0.026720818132162094, 0.012580730952322483, -0.01615588739514351, 0.0337187722325325, -0.043105125427246094, 0.045954830944538116, -0.02139735035598278, -0.08642750978469849, 0.004690163303166628, -0.029437372460961342, -0.00020457124628592283, 0.02073962427675724, 0.057533204555511475, 0.040730... |
568 | 568 | ['Minh-Thang Luong', 'Ilya Sutskever', 'Quoc V. Le', 'Oriol Vinyals', 'Wojciech Zaremba'] | 1410.8206v4 | 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... | Addressing the Rare Word Problem in Neural Machine Translation | 2,014 | http://arxiv.org/pdf/1410.8206v4 | Title Addressing Rare Word Problem Neural Machine Translation Summary Neural Machine Translation NMT new approach machine translation shown promising result comparable traditional approach significant weakness conventional NMT system inability correctly translate rare word endtoend NMTs tend relatively small vocabulary... | [0.022855162620544434, 0.029289616271853447, 0.02399652637541294, 0.04400232434272766, -0.05045294761657715, -0.023506755009293556, -0.010444186627864838, 0.032771993428468704, -0.04892449453473091, -0.015761377289891243, 0.039404843002557755, -0.015806181356310844, 0.03838327154517174, 0.025523344054818153, 0.03211229... |
569 | 569 | ['Jianpeng Cheng', 'Dimitri Kartsaklis', 'Edward Grefenstette'] | 1411.4116v1 | 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 ... | Investigating the Role of Prior Disambiguation in Deep-learning
Compositional Models of Meaning | 2,014 | http://arxiv.org/pdf/1411.4116v1 | Title Investigating Role Prior Disambiguation Deeplearning Compositional Models Meaning Summary paper aim explore effect prior disambiguation neural network based compositional model hope better semantic representation text compound produced disambiguate input word vector fed compositional deep net series evaluation sh... | [0.05237410217523575, -0.0034892878029495478, -0.017736071720719337, 0.020880402997136116, -0.019608888775110245, 0.031066931784152985, 0.004911269526928663, -0.014840880408883095, -0.01773572526872158, -0.04323531314730644, 0.010033187456429005, -0.01480183657258749, 0.031577616930007935, -0.017169419676065445, 0.0334... |
570 | 570 | ['Awni Hannun', 'Carl Case', 'Jared Casper', 'Bryan Catanzaro', 'Greg Diamos', 'Erich Elsen', 'Ryan Prenger', 'Sanjeev Satheesh', 'Shubho Sengupta', 'Adam Coates', 'Andrew Y. Ng'] | 1412.5567v2 | 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... | Deep Speech: Scaling up end-to-end speech recognition | 2,014 | http://arxiv.org/pdf/1412.5567v2 | Title Deep Speech Scaling endtoend speech recognition Summary present stateoftheart speech recognition system developed using endtoend deep learning architecture significantly simpler traditional speech system rely laboriously engineered processing pipeline traditional system also tend perform poorly used noisy environ... | [-0.027821414172649384, -0.007224636618047953, 0.012885487638413906, 0.011362634599208832, 0.05090915039181709, -0.010874909348785877, 0.040681254118680954, 0.012056133709847927, -0.026008332148194313, 0.02655702270567417, -0.055612713098526, -0.03361864015460014, 0.03383137658238411, 0.07654588669538498, 0.01008385419... |
571 | 571 | ['Aram Ter-Sarkisov', 'Holger Schwenk', 'Loic Barrault', 'Fethi Bougares'] | 1412.6650v4 | 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... | Incremental Adaptation Strategies for Neural Network Language Models | 2,014 | http://arxiv.org/pdf/1412.6650v4 | Title Incremental Adaptation Strategies Neural Network Language Models Summary today acknowledged neural network language model outperform backoff language model application like speech recognition statistical machine translation However training model large amount data take several day present efficient technique adap... | [0.04433417692780495, 0.026261765509843826, 0.00290094455704093, 0.009626961313188076, 0.0018505413318052888, 0.007606645580381155, 0.011134685017168522, 0.008617978543043137, -0.029768086969852448, -0.0508238859474659, -0.028443163260817528, -0.0031198954675346613, 0.036626119166612625, 0.0071296426467597485, 0.022294... |
572 | 572 | ['Joël Legrand', 'Ronan Collobert'] | 1412.7028v4 | 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 ... | Joint RNN-Based Greedy Parsing and Word Composition | 2,014 | http://arxiv.org/pdf/1412.7028v4 | Title Joint RNNBased Greedy Parsing Word Composition Summary paper introduces greedy parser based neural network leverage new compositional subtree representation greedy parser compositional procedure jointly trained tightly depends eachother composition procedure output vector representation summarizes syntactically p... | [0.038988612592220306, 0.04623155668377876, -0.0028700153343379498, 0.06220453232526779, -0.04495514929294586, -0.0065127890557050705, -0.06172054633498192, -0.009365018457174301, -0.02464917115867138, -0.035406388342380524, 0.005040289834141731, -0.015868930146098137, 0.018734531477093697, 0.02240879274904728, -0.0225... |
573 | 573 | ['Pascal Vincent', 'Alexandre de Brébisson', 'Xavier Bouthillier'] | 1412.7091v3 | 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... | Efficient Exact Gradient Update for training Deep Networks with Very
Large Sparse Targets | 2,014 | http://arxiv.org/pdf/1412.7091v3 | Title Efficient Exact Gradient Update training Deep Networks Large Sparse Targets Summary important class problem involves training deep neural network sparse prediction target high dimension occur naturally eg neural language model learning wordembeddings often posed predicting probability next word among vocabulary s... | [0.02790750376880169, 0.03701736778020859, 0.024033328518271446, 0.035924483090639114, 0.025002453476190567, -0.016908086836338043, -0.024662375450134277, 0.01325901784002781, -0.06803694367408752, -0.04705901816487312, -0.037853434681892395, -0.03654506430029869, -0.007122768554836512, 0.014868839643895626, 0.02197933... |
574 | 574 | ['Lili Mou', 'Hao Peng', 'Ge Li', 'Yan Xu', 'Lu Zhang', 'Zhi Jin'] | 1504.01106v5 | 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... | Discriminative Neural Sentence Modeling by Tree-Based Convolution | 2,015 | http://arxiv.org/pdf/1504.01106v5 | Title Discriminative Neural Sentence Modeling TreeBased Convolution Summary paper proposes treebased convolutional neural network TBCNN discriminative sentence modeling model leverage either constituency tree dependency tree sentence treebased convolution process extract sentence structural feature feature aggregated m... | [0.05543934553861618, 0.02635028585791588, 0.011994539760053158, 0.09633726626634598, -0.045005373656749725, 0.015982534736394882, -0.0516539067029953, -0.03248940780758858, -0.03221478313207626, -0.07559776306152344, 1.281684603782196e-06, -0.01715259812772274, -0.030680643394589424, 0.04130610078573227, -0.0360275283... |
575 | 575 | ['Han Zhao', 'Zhengdong Lu', 'Pascal Poupart'] | 1504.05070v2 | 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... | Self-Adaptive Hierarchical Sentence Model | 2,015 | http://arxiv.org/pdf/1504.05070v2 | Title SelfAdaptive Hierarchical Sentence Model Summary ability accurately model sentence varying stage eg wordphrasesentence play central role natural language processing effort towards goal propose selfadaptive hierarchical sentence model AdaSent AdaSent effectively form hierarchy representation word phrase sentence r... | [0.05538245290517807, 0.018063362687826157, -0.032322634011507034, 0.04892224818468094, -0.05491231381893158, 0.003983187954872847, -0.0020803736988455057, -0.04065811634063721, 0.03117627464234829, -0.08946610242128372, -0.021120086312294006, 0.03724454343318939, -0.035660646855831146, 0.05444278568029404, 0.001115854... |
576 | 576 | ['Cicero Nogueira dos Santos', 'Bing Xiang', 'Bowen Zhou'] | 1504.06580v2 | 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... | Classifying Relations by Ranking with Convolutional Neural Networks | 2,015 | http://arxiv.org/pdf/1504.06580v2 | Title Classifying Relations Ranking Convolutional Neural Networks Summary Relation classification important semantic processing task stateoftheart system still rely costly handcrafted feature work tackle relation classification task using convolutional neural network performs classification ranking CRCNN propose new pa... | [0.07756244391202927, 0.04017649590969086, 0.007496031932532787, 0.0741201713681221, 0.000742192380130291, 0.008697119541466236, -0.0011894071940332651, 0.020560026168823242, 0.00620815996080637, -0.057921066880226135, -0.027497971430420876, 0.00218995101749897, -0.005068772938102484, -0.008564497344195843, -0.02058519... |
577 | 577 | ['Thanh-Le Ha', 'Jan Niehues', 'Alex Waibel'] | 1504.07395v1 | 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... | Lexical Translation Model Using a Deep Neural Network Architecture | 2,015 | http://arxiv.org/pdf/1504.07395v1 | Title Lexical Translation Model Using Deep Neural Network Architecture Summary paper combine advantage model using global source sentence context Discriminative Word Lexicon neural network using deep neural network instead linear maximum entropy model Discriminative Word Lexicon model able leverage dependency different... | [0.04355678707361221, 0.011171996593475342, 0.007991897873580456, 0.07533075660467148, -0.052880823612213135, 0.008705146610736847, 0.010591925121843815, -0.006497151684015989, -0.04921087622642517, -0.024072065949440002, -0.04087696596980095, -0.03690309450030327, 0.04492330178618431, 0.016308343037962914, 0.029270466... |
578 | 578 | ['Andrej Karpathy', 'Justin Johnson', 'Li Fei-Fei'] | 1506.02078v2 | 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... | Visualizing and Understanding Recurrent Networks | 2,015 | http://arxiv.org/pdf/1506.02078v2 | Title Visualizing Understanding Recurrent Networks Summary Recurrent Neural Networks RNNs specifically variant Long ShortTerm Memory LSTM enjoying renewed interest result successful application wide range machine learning problem involve sequential data However LSTMs provide exceptional result practice source performan... | [-0.011355806142091751, -0.005041065160185099, -0.01834714412689209, 0.030946750193834305, -0.02056630700826645, -0.010613009333610535, 0.018238291144371033, -0.003978530876338482, -0.042686380445957184, 0.011484013870358467, -0.0167690422385931, -0.05414002388715744, 0.057105064392089844, 0.05567161366343498, 0.024605... |
579 | 579 | ['Cheng-Tao Chung', 'Cheng-Yu Tsai', 'Hsiang-Hung Lu', 'Yuan-ming Liou', 'Yen-Chen Wu', 'Yen-Ju Lu', 'Hung-yi Lee', 'Lin-shan Lee'] | 1506.02327v1 | 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 ... | A Multi-layered Acoustic Tokenizing Deep Neural Network (MAT-DNN) for
Unsupervised Discovery of Linguistic Units and Generation of High Quality
Features | 2,015 | http://arxiv.org/pdf/1506.02327v1 | Title Multilayered Acoustic Tokenizing Deep Neural Network MATDNN Unsupervised Discovery Linguistic Units Generation High Quality Features Summary paper summarizes work done author Zero Resource Speech Challenge organized technical program Interspeech 2015 goal challenge discover linguistic unit directly unlabeled spee... | [-0.023208608850836754, 0.03669661656022072, 0.011429867707192898, 0.01780596747994423, -0.01536133885383606, -0.038021013140678406, 0.07236523181200027, -0.019846657291054726, -0.10259981453418732, -0.01436596643179655, -0.0335264652967453, 0.04753900691866875, -0.011864783242344856, 0.043359726667404175, 0.0296250060... |
580 | 580 | ['Douglas Bagnall'] | 1506.04891v2 | 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 ... | Author Identification using Multi-headed Recurrent Neural Networks | 2,015 | http://arxiv.org/pdf/1506.04891v2 | Title Author Identification using Multiheaded Recurrent Neural Networks Summary Recurrent neural network RNNs good modelling flow text typically need trained far larger corpus available PAN 2015 Author Identification task paper describes novel approach output layer characterlevel RNN language model split several indepe... | [0.041541144251823425, 0.07551287859678268, -0.014381404966115952, 0.06047964096069336, -0.008142398670315742, 0.01562241930514574, 0.06985439360141754, -0.014277661219239235, 0.021943392232060432, 0.013253385201096535, 0.0024878140538930893, -0.008008643053472042, 0.02791939303278923, -0.02467748336493969, 0.005771238... |
581 | 581 | ['Fandong Meng', 'Zhengdong Lu', 'Zhaopeng Tu', 'Hang Li', 'Qun Liu'] | 1506.06442v4 | 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... | A Deep Memory-based Architecture for Sequence-to-Sequence Learning | 2,015 | http://arxiv.org/pdf/1506.06442v4 | Title Deep Memorybased Architecture SequencetoSequence Learning Summary propose DEEPMEMORY novel deep architecture sequencetosequence learning performs task series nonlinear transformation representation input sequence eg Chinese sentence final output sequence eg translation English Inspired recently proposed Neural Tu... | [0.028884893283247948, 0.06040644273161888, -0.02255413681268692, 0.030304238200187683, -0.01584515906870365, 0.012662301771342754, 0.010223411954939365, -0.003853060305118561, -0.012946471571922302, -0.026064522564411163, -0.023150455206632614, -0.04285856708884239, 0.011858006939291954, 0.05385228246450424, 0.0052147... |
582 | 582 | ['Ankit Kumar', 'Ozan Irsoy', 'Peter Ondruska', 'Mohit Iyyer', 'James Bradbury', 'Ishaan Gulrajani', 'Victor Zhong', 'Romain Paulus', 'Richard Socher'] | 1506.07285v5 | 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... | Ask Me Anything: Dynamic Memory Networks for Natural Language Processing | 2,015 | http://arxiv.org/pdf/1506.07285v5 | Title Ask Anything Dynamic Memory Networks Natural Language Processing Summary task natural language processing cast question answering QA problem language input introduce dynamic memory network DMN neural network architecture process input sequence question form episodic memory generates relevant answer Questions trig... | [0.05158541724085808, 0.02812228724360466, -0.02276724763214588, 0.02513764426112175, -0.013104015029966831, 0.012974195182323456, 0.020096927881240845, 0.005087028257548809, 0.0018260703654959798, -0.05064588785171509, 0.012586611323058605, -0.014207405969500542, -0.007919731549918652, 0.08814384788274765, 0.022306222... |
583 | 583 | ['Lantian Li', 'Yiye Lin', 'Zhiyong Zhang', 'Dong Wang'] | 1506.08349v1 | 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... | Improved Deep Speaker Feature Learning for Text-Dependent Speaker
Recognition | 2,015 | http://arxiv.org/pdf/1506.08349v1 | Title Improved Deep Speaker Feature Learning TextDependent Speaker Recognition Summary deep learning approach proposed recently derive speaker identifies dvector deep neural network DNN approach applied textdependent speaker recognition task show reasonable performance gain combined conventional ivector approach Althou... | [-0.002774872351437807, 0.037153977900743484, -0.0013352383393794298, 0.021412957459688187, -0.04566184803843498, 0.02175619639456272, 0.05310129374265671, -0.01793704181909561, -0.021418150514364243, -0.01175806112587452, -0.0002018620871240273, -0.016237840056419373, 0.04732605814933777, -0.021833205595612526, -0.029... |
584 | 584 | ['Nal Kalchbrenner', 'Ivo Danihelka', 'Alex Graves'] | 1507.01526v3 | 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 ... | Grid Long Short-Term Memory | 2,015 | http://arxiv.org/pdf/1507.01526v3 | Title Grid Long ShortTerm Memory Summary paper introduces Grid Long ShortTerm Memory network LSTM cell arranged multidimensional grid applied vector sequence higher dimensional data image network differs existing deep LSTM architecture cell connected network layer well along spatiotemporal dimension data network provid... | [0.0004072290612384677, 0.03275850787758827, -0.0063985129818320274, 0.07208779454231262, 0.00855559017509222, 0.025971632450819016, 0.046660978347063065, -0.023915085941553116, -0.013846147805452347, -0.008136903867125511, -0.03842177987098694, -0.10288789123296738, 0.04769172519445419, 0.04459302872419357, 0.04737400... |
585 | 585 | ['Yang Liu', 'Furu Wei', 'Sujian Li', 'Heng Ji', 'Ming Zhou', 'Houfeng Wang'] | 1507.04646v1 | 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... | A Dependency-Based Neural Network for Relation Classification | 2,015 | http://arxiv.org/pdf/1507.04646v1 | Title DependencyBased Neural Network Relation Classification Summary Previous research relation classification verified effectiveness using dependency shortest path subtrees paper explore make full use combination dependency information first propose new structure termed augmented dependency path ADP composed shortest ... | [0.03561617434024811, 0.046365831047296524, -0.0013076739851385355, 0.07303697615861893, -0.00864286720752716, -0.01818736642599106, -0.06447212398052216, 0.028535889461636543, 0.008817428722977638, -0.04436936601996422, 0.0034797396510839462, 0.02065291255712509, 0.01121324859559536, -0.013998960144817829, -0.02868277... |
586 | 586 | ['Jian Tang', 'Meng Qu', 'Qiaozhu Mei'] | 1508.00200v1 | 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... | PTE: Predictive Text Embedding through Large-scale Heterogeneous Text
Networks | 2,015 | http://arxiv.org/pdf/1508.00200v1 | Title PTE Predictive Text Embedding Largescale Heterogeneous Text Networks Summary Unsupervised text embedding method Skipgram Paragraph Vector attracting increasing attention due simplicity scalability effectiveness However comparing sophisticated deep learning architecture convolutional neural network method usually ... | [0.010130133479833603, -0.006559894885867834, 0.008196325041353703, 0.08284035325050354, -0.006328774616122246, -0.007417625281959772, 0.03629646450281143, 0.010787906125187874, 0.05100501701235771, -0.03620427846908569, -0.02727072685956955, 0.007336152717471123, -0.0077982097864151, 0.04922201484441757, 0.02580201812... |
587 | 587 | ['Dongxu Zhang', 'Dong Wang'] | 1508.01006v2 | 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... | Relation Classification via Recurrent Neural Network | 2,015 | http://arxiv.org/pdf/1508.01006v2 | Title Relation Classification via Recurrent Neural Network Summary Deep learning gained much success sentencelevel relation classification example convolutional neural network CNN delivered competitive performance without much effort feature engineering conventional patternbased method Thus lot work produced based CNN ... | [0.06687229871749878, 0.04454890638589859, -0.002900527324527502, 0.08168329298496246, -0.041271060705184937, -0.015379820019006729, -0.004754744004458189, 0.02586425095796585, -0.014359858818352222, -0.06835654377937317, 0.007101353257894516, -0.036215901374816895, 0.01686316914856434, 0.00855633057653904, -0.03326073... |
588 | 588 | ['Dongxu Zhang', 'Tianyi Luo', 'Dong Wang', 'Rong Liu'] | 1508.01011v1 | 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... | Learning from LDA using Deep Neural Networks | 2,015 | http://arxiv.org/pdf/1508.01011v1 | Title Learning LDA using Deep Neural Networks Summary Latent Dirichlet Allocation LDA threelevel hierarchical Bayesian model topic inference spite great success inferring latent topic distribution LDA timeconsuming Motivated transfer learning approach proposed bynewcitehinton2015distilling present novel method us LDA s... | [0.04232034459710121, 0.05954749137163162, -0.012289010919630527, 0.027016060426831245, -0.02140469290316105, 0.014207686297595501, 0.038581497967243195, -0.012945486232638359, -0.04322564974427223, -0.044133830815553665, -0.01582692191004753, 0.017526760697364807, -0.026834798976778984, 0.030297458171844482, -0.020221... |
589 | 589 | ['Marek Rei'] | 1508.03854v1 | 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 ... | Online Representation Learning in Recurrent Neural Language Models | 2,015 | http://arxiv.org/pdf/1508.03854v1 | Title Online Representation Learning Recurrent Neural Language Models Summary investigate extension continuous online learning recurrent neural network language model model keep separate vector representation current unit text processed adaptively adjusts prediction initial experiment give promising result indicating m... | [0.03535664826631546, -0.001378671615384519, -0.01852804608643055, 0.04573841765522957, -0.020446332171559334, -0.0014789568958804011, 0.01775195635855198, 0.017491959035396576, -0.0062713478691875935, -0.0704064890742302, -0.0024637607857584953, -0.052236977964639664, 0.029771896079182625, 0.07973246276378632, 0.00975... |
590 | 590 | ['Ye Zhang', 'Byron Wallace'] | 1510.03820v4 | 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... | A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional
Neural Networks for Sentence Classification | 2,015 | http://arxiv.org/pdf/1510.03820v4 | Title Sensitivity Analysis Practitioners Guide Convolutional Neural Networks Sentence Classification Summary Convolutional Neural Networks CNNs recently achieved remarkably strong performance practically important task sentence classification kim 2014 kalchbrenner 2014 johnson 2014 However model require practitioner sp... | [0.07863238453865051, 0.04088165611028671, -0.023126494139432907, 0.05393735319375992, -0.03360119089484215, 0.0050298250280320644, 0.028157170861959457, 0.02379668690264225, -0.020739585161209106, -0.06305491179227829, -0.00040149083361029625, -0.00851241685450077, -0.009140674024820328, 0.011043904349207878, -0.00964... |
591 | 591 | ['Yu Zhang', 'Ekapol Chuangsuwanich', 'James Glass', 'Dong Yu'] | 1510.08985v1 | 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 ... | Prediction-Adaptation-Correction Recurrent Neural Networks for
Low-Resource Language Speech Recognition | 2,015 | http://arxiv.org/pdf/1510.08985v1 | Title PredictionAdaptationCorrection Recurrent Neural Networks LowResource Language Speech Recognition Summary paper investigate use predictionadaptationcorrection recurrent neural network PACRNNs lowresource speech recognition PACRNN comprised pair neural network correction network us auxiliary information given predi... | [0.00022266522864811122, 0.014842996373772621, 0.004538939334452152, 0.047181304544210434, -0.009879344142973423, -0.025139303877949715, 0.005989015102386475, -0.003311931388452649, -0.02159503474831581, -0.01609487272799015, -0.03978554904460907, -0.039478711783885956, 0.0883834958076477, 0.03858489543199539, 0.007793... |
592 | 592 | ['Hongyu Guo'] | 1510.09202v1 | 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... | Generating Text with Deep Reinforcement Learning | 2,015 | http://arxiv.org/pdf/1510.09202v1 | Title Generating Text Deep Reinforcement Learning Summary introduce novel schema sequence sequence learning Deep QNetwork DQN decodes output sequence iteratively aim enable decoder first tackle easier portion sequence turn cope difficult part Specifically iteration encoderdecoder Long ShortTerm Memory LSTM network empl... | [0.050332311540842056, 0.03927690535783768, 0.002629154361784458, 0.012904432602226734, -0.016218572854995728, -0.004598155617713928, -0.018037695437669754, 0.0040520173497498035, -0.0072245425544679165, -0.015038839541375637, 0.003156442893669009, 0.005189321935176849, -0.0173582062125206, 0.06151720881462097, 0.00103... |
593 | 593 | ['Junyoung Chung', 'Jacob Devlin', 'Hany Hassan Awadalla'] | 1511.01042v2 | 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... | Detecting Interrogative Utterances with Recurrent Neural Networks | 2,015 | http://arxiv.org/pdf/1511.01042v2 | Title Detecting Interrogative Utterances Recurrent Neural Networks Summary paper explore different neural network architecture predict speaker given utterance asking question making statement com pare outcome regularization method popularly used train deep neural network study different context function affect classifi... | [0.07140016555786133, 0.028240378946065903, -0.0030725793913006783, 0.05219525471329689, -0.05397335812449455, -0.021107256412506104, 0.016506560146808624, 0.009133312851190567, 0.00037390904617495835, -0.0003898455179296434, -0.0007419760804623365, -0.021651597693562508, 0.00403172941878438, 0.03598712384700775, 0.008... |
594 | 594 | ['Navdeep Jaitly', 'David Sussillo', 'Quoc V. Le', 'Oriol Vinyals', 'Ilya Sutskever', 'Samy Bengio'] | 1511.04868v4 | 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... | A Neural Transducer | 2,015 | http://arxiv.org/pdf/1511.04868v4 | Title Neural Transducer Summary Sequencetosequence model achieved impressive result various task However unsuitable task require incremental prediction made data arrives task long input sequence output sequence generate output sequence conditioned entire input sequence paper present Neural Transducer make incremental p... | [0.015562239103019238, 0.054453905671834946, -0.0022053758148103952, -0.013406692072749138, -0.014066972769796848, -0.031723491847515106, 0.003930010832846165, 0.01667247898876667, -0.050774432718753815, -0.0017024906119331717, 0.039740875363349915, 0.01903730072081089, 0.021850761026144028, 0.0713837593793869, 0.03639... |
595 | 595 | ['Ethan Caballero'] | 1511.06420v2 | 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... | Skip-Thought Memory Networks | 2,015 | http://arxiv.org/pdf/1511.06420v2 | Title SkipThought Memory Networks Summary Question Answering QA fundamental natural language processing nlp problem phrased QA Kumar et al 2015 Current weakly supervised memory network model proposed far struggle answering question involve relation among multiple entity facebooks bAbi qa5threeargrelations Weston et al ... | [0.04113145172595978, 0.017919592559337616, -0.005954056512564421, 0.06399160623550415, -0.021321507170796394, 0.032747529447078705, 0.019004343077540398, -0.027221692726016045, 0.02152428589761257, -0.05416605994105339, -0.015545212663710117, -0.015252524055540562, -0.0282201636582613, 0.023438099771738052, 0.03672714... |
596 | 596 | ['Jason P. C. Chiu', 'Eric Nichols'] | 1511.08308v5 | 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... | Named Entity Recognition with Bidirectional LSTM-CNNs | 2,015 | http://arxiv.org/pdf/1511.08308v5 | Title Named Entity Recognition Bidirectional LSTMCNNs Summary Named entity recognition challenging task traditionally required large amount knowledge form feature engineering lexicon achieve high performance paper present novel neural network architecture automatically detects word characterlevel feature using hybrid b... | [0.024691695347428322, 0.0367535836994648, 0.033279772847890854, 0.09188473224639893, -0.01054808497428894, 0.02476241998374462, 0.010465210303664207, 0.051607243716716766, 0.016980402171611786, -0.05492996796965599, -0.009933359920978546, -0.03147837892174721, -0.02064288593828678, 0.055758364498615265, 0.032964557409... |
597 | 597 | ['Konstantin Lopyrev'] | 1512.01712v1 | 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... | Generating News Headlines with Recurrent Neural Networks | 2,015 | http://arxiv.org/pdf/1512.01712v1 | Title Generating News Headlines Recurrent Neural Networks Summary describe application encoderdecoder recurrent neural network LSTM unit attention generating headline text news article find model quite effective concisely paraphrasing news article Furthermore study neural network decides input word pay attention specif... | [0.049424909055233, 0.015581775456666946, 0.013913068920373917, 0.029738688841462135, -0.02919752337038517, 0.0008220571326091886, 0.017116209492087364, 0.009740505367517471, -0.027730794623494148, -0.03231985494494438, 0.014293843880295753, 0.030253801494836807, -0.0017017943318933249, 0.049015358090400696, 0.01676741... |
598 | 598 | ['Pranjal Singh', 'Amitabha Mukerjee'] | 1512.03549v1 | 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... | Words are not Equal: Graded Weighting Model for building Composite
Document Vectors | 2,015 | http://arxiv.org/pdf/1512.03549v1 | Title Words Equal Graded Weighting Model building Composite Document Vectors Summary Despite success distributional semantics composing phrase word vector remains important challenge Several method tried benchmark task sentiment classification including word vector averaging matrixvector approach based parsing onthefly... | [0.08187291771173477, 0.06270714104175568, 0.017307894304394722, 0.03457587584853172, -0.05003199353814125, 0.01512089278548956, 0.007526844274252653, 0.031498175114393234, -0.04426541179418564, -0.07682067900896072, -0.004928107373416424, -0.02226550504565239, 0.03401249274611473, 0.014045490883290768, -0.023086180910... |
599 | 599 | ['Liang Lu', 'Steve Renals'] | 1512.04280v4 | 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... | Small-footprint Deep Neural Networks with Highway Connections for Speech
Recognition | 2,015 | http://arxiv.org/pdf/1512.04280v4 | Title Smallfootprint Deep Neural Networks Highway Connections Speech Recognition Summary speech recognition deep neural network DNNs significantly improved recognition accuracy benchmark datasets application domain However compared conventional Gaussian mixture model DNNbased acoustic model usually much larger number m... | [-0.02974539063870907, 0.05479646474123001, -0.020276816561818123, 0.05071445554494858, -0.0004927068948745728, -0.015778936445713043, 0.05087105557322502, -0.005609317682683468, -0.06028558313846588, 0.004958346951752901, -0.001501046703197062, -0.013900922611355782, 0.052471064031124115, 0.02007078193128109, 0.017005... |
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