pwc-archive/papers-with-abstracts huggingface dataset:
An Adversarial Learning Framework For A Persona-Based Multi-Turn Dialogue Model
Authors: Oluwatobi Olabiyi, Anish Khazane, Alan Salimov, Erik T. Mueller
Date: 2019-04-29 00:00:00
Proceeding: NAACL 2019 6
Tasks: Attribute

In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq) neural network conversation model to a multi-turn dialogue scenario by modifying the state-of-the-art hredGAN architecture to simultaneously capture utterance attributes such as speaker identity, dialogue topic, speaker sentiments and so on. The proposed system, phredGAN has a persona-based HRED generator (PHRED) and a conditional discriminator. We also explore two approaches to accomplish the conditional discriminator: (1) phredGAN_a, a system that passes the attribute representation as an additional input into a traditional adversarial discriminator, and (2) phredGAN_d, a dual discriminator system which in addition to the adversarial discriminator, collaboratively predicts the attribute(s) that generated the input utterance. To demonstrate the superior performance of phredGAN over the persona Seq2Seq model, we experiment with two conversational datasets, the Ubuntu Dialogue Corpus (UDC) and TV series transcripts from the Big Bang Theory and Friends. Performance comparison is made with respect to a variety of quantitative measures as well as crowd-sourced human evaluation. We also explore the trade-offs from using either variant of phredGAN on datasets with many but weak attribute modalities (such as with Big Bang Theory and Friends) and ones with few but strong attribute modalities (customer-agent interactions in Ubuntu dataset).
A Persona-based Multi-turn Conversation Model in an Adversarial Learning Framework
Authors: Oluwatobi O. Olabiyi, Anish Khazane, Erik T. Mueller
Date: 2019-04-29 00:00:00
Tasks: Decoder

In this paper, we extend the persona-based sequence-to-sequence (Seq2Seq)
neural network conversation model to multi-turn dialogue by modifying the
state-of-the-art hredGAN architecture. To achieve this, we introduce an
additional input modality into the encoder and decoder of hredGAN to capture
other attributes such as speaker identity, location, sub-topics, and other
external attributes that might be available from the corpus of human-to-human
interactions. The resulting persona hredGAN ($phredGAN$) shows better
performance than both the existing persona-based Seq2Seq and hredGAN models
when those external attributes are available in a multi-turn dialogue corpus.
This superiority is demonstrated on TV drama series with character consistency
(such as Big Bang Theory and Friends) and customer service interaction datasets
such as Ubuntu dialogue corpus in terms of perplexity, BLEU, ROUGE, and
Distinct n-gram scores.
Neural Machine Translation with Recurrent Highway Networks
Authors: Maulik Parmar, V. Susheela Devi
Date: 2019-04-28 00:00:00
Tasks: Decoder, Language Modelling, Machine Translation, NMT, Sentence, Translation

Recurrent Neural Networks have lately gained a lot of popularity in language
modelling tasks, especially in neural machine translation(NMT). Very recent NMT
models are based on Encoder-Decoder, where a deep LSTM based encoder is used to
project the source sentence to a fixed dimensional vector and then another deep
LSTM decodes the target sentence from the vector. However there has been very
little work on exploring architectures that have more than one layer in
space(i.e. in each time step). This paper examines the effectiveness of the
simple Recurrent Highway Networks(RHN) in NMT tasks. The model uses Recurrent
Highway Neural Network in encoder and decoder, with attention .We also explore
the reconstructor model to improve adequacy. We demonstrate the effectiveness
of all three approaches on the IWSLT English-Vietnamese dataset. We see that
RHN performs on par with LSTM based models and even better in some cases.We see
that deep RHN models are easy to train compared to deep LSTM based models
because of highway connections. The paper also investigates the effects of
increasing recurrent depth in each time step.
Deep Learning for Sequential Recommendation: Algorithms, Influential Factors, and Evaluations
Authors: Hui Fang, Danning Zhang, Yiheng Shu, Guibing Guo
Date: 2019-04-30 00:00:00
Tasks: Deep Learning, Recommendation Systems, Sequential Recommendation, Survey

In the field of sequential recommendation, deep learning (DL)-based methods have received a lot of attention in the past few years and surpassed traditional models such as Markov chain-based and factorization-based ones. However, there is little systematic study on DL-based methods, especially regarding to how to design an effective DL model for sequential recommendation. In this view, this survey focuses on DL-based sequential recommender systems by taking the aforementioned issues into consideration. Specifically,we illustrate the concept of sequential recommendation, propose a categorization of existing algorithms in terms of three types of behavioral sequence, summarize the key factors affecting the performance of DL-based models, and conduct corresponding evaluations to demonstrate the effects of these factors. We conclude this survey by systematically outlining future directions and challenges in this field.
Evaluating the Portability of an NLP System for Processing Echocardiograms: A Retrospective, Multi-site Observational Study
Authors: Prakash Adekkanattu, Guoqian Jiang, Yuan Luo, Paul R. Kingsbury, Zhen-Xing Xu, Luke V. Rasmussen, Jennifer A. Pacheco, Richard C. Kiefer, Daniel J. Stone, Pascal S. Brandt, Liang Yao, Yizhen Zhong, Yu Deng, Fei Wang, Jessica S. Ancker, Thomas R. Campion, Jyotishman Pathak
Date: 2019-04-02 00:00:00

While natural language processing (NLP) of unstructured clinical narratives
holds the potential for patient care and clinical research, portability of NLP
approaches across multiple sites remains a major challenge. This study
investigated the portability of an NLP system developed initially at the
Department of Veterans Affairs (VA) to extract 27 key cardiac concepts from
free-text or semi-structured echocardiograms from three academic medical
centers: Weill Cornell Medicine, Mayo Clinic and Northwestern Medicine. While
the NLP system showed high precision and recall measurements for four target
concepts (aortic valve regurgitation, left atrium size at end systole, mitral
valve regurgitation, tricuspid valve regurgitation) across all sites, we found
moderate or poor results for the remaining concepts and the NLP system
performance varied between individual sites.
PhonSenticNet: A Cognitive Approach to Microtext Normalization for Concept-Level Sentiment Analysis
Authors: Ranjan Satapathy, Aalind Singh, Erik Cambria
Date: 2019-04-24 00:00:00
Tasks: Sentiment Analysis

With the current upsurge in the usage of social media platforms, the trend of
using short text (microtext) in place of standard words has seen a significant
rise. The usage of microtext poses a considerable performance issue in
concept-level sentiment analysis, since models are trained on standard words.
This paper discusses the impact of coupling sub-symbolic (phonetics) with
symbolic (machine learning) Artificial Intelligence to transform the
out-of-vocabulary concepts into their standard in-vocabulary form. The phonetic
distance is calculated using the Sorensen similarity algorithm. The
phonetically similar invocabulary concepts thus obtained are then used to
compute the correct polarity value, which was previously being miscalculated
because of the presence of microtext. Our proposed framework increases the
accuracy of polarity detection by 6% as compared to the earlier model. This
also validates the fact that microtext normalization is a necessary
pre-requisite for the sentiment analysis task.
Poly-encoders: Transformer Architectures and Pre-training Strategies for Fast and Accurate Multi-sentence Scoring
Authors: Samuel Humeau, Kurt Shuster, Marie-Anne Lachaux, Jason Weston
Date: 2019-04-22 00:00:00
Tasks: Conversational Response Selection, Sentence

The use of deep pre-trained bidirectional transformers has led to remarkable progress in a number of applications (Devlin et al., 2018). For tasks that make pairwise comparisons between sequences, matching a given input with a corresponding label, two approaches are common: Cross-encoders performing full self-attention over the pair and Bi-encoders encoding the pair separately. The former often performs better, but is too slow for practical use. In this work, we develop a new transformer architecture, the Poly-encoder, that learns global rather than token level self-attention features. We perform a detailed comparison of all three approaches, including what pre-training and fine-tuning strategies work best. We show our models achieve state-of-the-art results on three existing tasks; that Poly-encoders are faster than Cross-encoders and more accurate than Bi-encoders; and that the best results are obtained by pre-training on large datasets similar to the downstream tasks.
A Novel Task-Oriented Text Corpus in Silent Speech Recognition and its Natural Language Generation Construction Method
Authors: Dong Cao, Dong-dong Zhang, Haibo Chen
Date: 2019-04-19 00:00:00
Tasks: Data-to-Text Generation, Diversity, EEG, Electroencephalogram (EEG), Silent Speech Recognition, speech-recognition, Speech Recognition, Text Generation

Millions of people with severe speech disorders around the world may regain
their communication capabilities through techniques of silent speech
recognition (SSR). Using electroencephalography (EEG) as a biomarker for speech
decoding has been popular for SSR. However, the lack of SSR text corpus has
impeded the development of this technique. Here, we construct a novel
task-oriented text corpus, which is utilized in the field of SSR. In the
process of construction, we propose a task-oriented hybrid construction method
based on natural language generation algorithm. The algorithm focuses on the
strategy of data-to-text generation, and has two advantages including
linguistic quality and high diversity. These two advantages use template-based
method and deep neural networks respectively. In an SSR experiment with the
generated text corpus, analysis results show that the performance of our hybrid
construction method outperforms the pure method such as template-based natural
language generation or neural natural language generation models.
Point-less: More Abstractive Summarization with Pointer-Generator Networks
Authors: Freek Boutkan, Jorn Ranzijn, David Rau, Eelco van der Wel
Date: 2019-04-18 00:00:00
Tasks: Abstractive Text Summarization

The Pointer-Generator architecture has shown to be a big improvement for
abstractive summarization seq2seq models. However, the summaries produced by
this model are largely extractive as over 30% of the generated sentences are
copied from the source text. This work proposes a multihead attention
mechanism, pointer dropout, and two new loss functions to promote more
abstractive summaries while maintaining similar ROUGE scores. Both the
multihead attention and dropout do not improve N-gram novelty, however, the
dropout acts as a regularizer which improves the ROUGE score. The new loss
function achieves significantly higher novel N-grams and sentences, at the cost
of a slightly lower ROUGE score.
TextKD-GAN: Text Generation using KnowledgeDistillation and Generative Adversarial Networks
Authors: Md. Akmal Haidar, Mehdi Rezagholizadeh
Date: 2019-04-23 00:00:00
Tasks: Image Generation, Knowledge Distillation, Language Modeling, Language Modelling, Machine Translation, Text Generation, Text Summarization, Translation

Text generation is of particular interest in many NLP applications such as
machine translation, language modeling, and text summarization. Generative
adversarial networks (GANs) achieved a remarkable success in high quality image
generation in computer vision,and recently, GANs have gained lots of interest
from the NLP community as well. However, achieving similar success in NLP would
be more challenging due to the discrete nature of text. In this work, we
introduce a method using knowledge distillation to effectively exploit GAN
setup for text generation. We demonstrate how autoencoders (AEs) can be used
for providing a continuous representation of sentences, which is a smooth
representation that assign non-zero probabilities to more than one word. We
distill this representation to train the generator to synthesize similar smooth
representations. We perform a number of experiments to validate our idea using
different datasets and show that our proposed approach yields better
performance in terms of the BLEU score and Jensen-Shannon distance (JSD)
measure compared to traditional GAN-based text generation approaches without
pre-training.
CraftAssist Instruction Parsing: Semantic Parsing for a Minecraft Assistant
Authors: Yacine Jernite, Kavya Srinet, Jonathan Gray, Arthur Szlam
Date: 2019-04-17 00:00:00
Tasks: Minecraft, Semantic Parsing

We propose a large scale semantic parsing dataset focused on
instruction-driven communication with an agent in Minecraft. We describe the
data collection process which yields additional 35K human generated
instructions with their semantic annotations. We report the performance of
three baseline models and find that while a dataset of this size helps us train
a usable instruction parser, it still poses interesting generalization
challenges which we hope will help develop better and more robust models.
Review-Driven Answer Generation for Product-Related Questions in E-Commerce
Authors: Shiqian Chen, Chenliang Li, Feng Ji, Wei Zhou, Haiqing Chen
Date: 2019-04-27 00:00:00
Tasks: Answer Generation

The users often have many product-related questions before they make a
purchase decision in E-commerce. However, it is often time-consuming to examine
each user review to identify the desired information. In this paper, we propose
a novel review-driven framework for answer generation for product-related
questions in E-commerce, named RAGE. We develope RAGE on the basis of the
multi-layer convolutional architecture to facilitate speed-up of answer
generation with the parallel computation. For each question, RAGE first
extracts the relevant review snippets from the reviews of the corresponding
product. Then, we devise a mechanism to identify the relevant information from
the noise-prone review snippets and incorporate this information to guide the
answer generation. The experiments on two real-world E-Commerce datasets show
that the proposed RAGE significantly outperforms the existing alternatives in
producing more accurate and informative answers in natural language. Moreover,
RAGE takes much less time for both model training and answer generation than
the existing RNN based generation models.
Using Context Information to Enhance Simple Question Answering
Authors: Lin Li, Mengjing Zhang, Zhaohui Chao, Jianwen Xiang
Date: 2019-04-27 00:00:00
Tasks: Question Answering

With the rapid development of knowledge bases(KBs),question
answering(QA)based on KBs has become a hot research issue. In this paper,we
propose two frameworks(i.e.,pipeline framework,an end-to-end framework)to focus
answering single-relation factoid question. In both of two frameworks,we study
the effect of context information on the quality of QA,such as the entity's
notable type,out-degree. In the end-to-end framework,we combine char-level
encoding and self-attention mechanisms,using weight sharing and multi-task
strategies to enhance the accuracy of QA. Experimental results show that
context information can get better results of simple QA whether it is the
pipeline framework or the end-to-end framework. In addition,we find that the
end-to-end framework achieves results competitive with state-of-the-art
approaches in terms of accuracy and take much shorter time than them.
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Authors: zczcz
Date: 2004-12-11 00:00:00
Proceeding: zczczc 2004 12

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Multi-Scale Deep Compressive Sensing Network
Authors: Canh, Thuong Nguyen; Jeon, Byeungwoo
Date: 2018-12-12 00:00:00
Proceeding: Visual Communications and Image Processing Conference 2018 12
Tasks: Compressive Sensing

With joint learning of the sampling and recovery, the deep learning-based compressive sensing (DCS) has shown significant improvement in performance and running time reduction. Its reconstructed image, however, losses high-frequency content especially at low subrates. It is understood due to relatively much low-frequency information captured into the sampling matrix. This behaviour happens similarly in the multi-scale sampling scheme which also samples more low-frequency components. This paper proposes a multi-scale DCS (MS-DCSNet) based on convolutional neural network. Firstly, we convert image signal using multiple scale-based wavelet transform. Then, the signal is captured through the convolution block by block across scales. The initial reconstructed image is directly recovered from multi-scale measurements. Multi-scale wavelet convolution is utilized to enhance the final reconstruction quality. The network learns to perform both multi-scale in sampling and reconstruction thus results in better reconstruction quality.
Difference of Convolution for Deep Compressive Sensing
Authors: Canh, Thuong Nguyen; Jeon, Byeungwoo
Date: 2019-09-22 00:00:00
Proceeding: IEEE International Conference on Image Processing 2019 9
Tasks: Compressive Sensing

Deep learning-based compressive sensing (DCS) has improved the compressive sensing (CS) with fast and high reconstruction quality. Researchers have further extended it to multi-scale DCS which improves reconstruction quality based on Wavelet decomposition. In this work, we mimic the Difference of Gaussian via convolution and propose a scheme named as Difference of convolution-based multi-scale DCS (DoC-DCS). Unlike the multi-scale DCS based on a well-designed filter in wavelet domain, the proposed DoC-DCS learns decomposition, thereby, outperforms other state-of-the-art compressive sensing methods.
EDVR: Video Restoration with Enhanced Deformable Convolutional Networks
Authors: Xintao Wang, Kelvin C. K. Chan, Ke Yu, Chao Dong, Chen Change Loy
Date: 2019-05-07 00:00:00
Tasks: Deblurring, Super-Resolution, Video Enhancement, Video Restoration, Video Super-Resolution

Video restoration tasks, including super-resolution, deblurring, etc, are drawing increasing attention in the computer vision community. A challenging benchmark named REDS is released in the NTIRE19 Challenge. This new benchmark challenges existing methods from two aspects: (1) how to align multiple frames given large motions, and (2) how to effectively fuse different frames with diverse motion and blur. In this work, we propose a novel Video Restoration framework with Enhanced Deformable networks, termed EDVR, to address these challenges. First, to handle large motions, we devise a Pyramid, Cascading and Deformable (PCD) alignment module, in which frame alignment is done at the feature level using deformable convolutions in a coarse-to-fine manner. Second, we propose a Temporal and Spatial Attention (TSA) fusion module, in which attention is applied both temporally and spatially, so as to emphasize important features for subsequent restoration. Thanks to these modules, our EDVR wins the champions and outperforms the second place by a large margin in all four tracks in the NTIRE19 video restoration and enhancement challenges. EDVR also demonstrates superior performance to state-of-the-art published methods on video super-resolution and deblurring. The code is available at https://github.com/xinntao/EDVR.
The Alt-Right and Global Information Warfare
Authors: Emmi Bevensee, Alexander Reid Ross
Date: 2019-05-07 00:00:00

The Alt-Right is a neo-fascist white supremacist movement that is involved in violent extremism and shows signs of engagement in extensive disinformation campaigns. Using social media data mining, this study develops a deeper understanding of such targeted disinformation campaigns and the ways they spread. It also adds to the available literature on the endogenous and exogenous influences within the US far right, as well as motivating factors that drive disinformation campaigns, such as geopolitical strategy. This study is to be taken as a preliminary analysis to indicate future methods and follow-on research that will help develop an integrated approach to understanding the strategies and associations of the modern fascist movement.
Context-Aware Automatic Occlusion Removal
Authors: Kumara Kahatapitiya, Dumindu Tissera, Ranga Rodrigo
Date: 2019-05-07 00:00:00
Tasks: Image Enhancement

Occlusion removal is an interesting application of image enhancement, for which, existing work suggests manually-annotated or domain-specific occlusion removal. No work tries to address automatic occlusion detection and removal as a context-aware generic problem. In this paper, we present a novel methodology to identify objects that do not relate to the image context as occlusions and remove them, reconstructing the space occupied coherently. The proposed system detects occlusions by considering the relation between foreground and background object classes represented as vector embeddings, and removes them through inpainting. We test our system on COCO-Stuff dataset and conduct a user study to establish a baseline in context-aware automatic occlusion removal.
Learning Unsupervised Multi-View Stereopsis via Robust Photometric Consistency
Authors: Tejas Khot, Shubham Agrawal, Shubham Tulsiani, Christoph Mertz, Simon Lucey, Martial Hebert
Date: 2019-05-07 00:00:00
Tasks: 3D geometry, Depth Estimation, Depth Prediction

We present a learning based approach for multi-view stereopsis (MVS). While current deep MVS methods achieve impressive results, they crucially rely on ground-truth 3D training data, and acquisition of such precise 3D geometry for supervision is a major hurdle. Our framework instead leverages photometric consistency between multiple views as supervisory signal for learning depth prediction in a wide baseline MVS setup. However, naively applying photo consistency constraints is undesirable due to occlusion and lighting changes across views. To overcome this, we propose a robust loss formulation that: a) enforces first order consistency and b) for each point, selectively enforces consistency with some views, thus implicitly handling occlusions. We demonstrate our ability to learn MVS without 3D supervision using a real dataset, and show that each component of our proposed robust loss results in a significant improvement. We qualitatively observe that our reconstructions are often more complete than the acquired ground truth, further showing the merits of this approach. Lastly, our learned model generalizes to novel settings, and our approach allows adaptation of existing CNNs to datasets without ground-truth 3D by unsupervised finetuning. Project webpage: https://tejaskhot.github.io/unsup_mvs