pwc-archive/papers-with-abstracts huggingface dataset:
Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension
Authors: Kai Sun, Dian Yu, Dong Yu, Claire Cardie
Date: 2019-04-21 00:00:00
Proceeding: TACL 2020 1
Tasks: Data Augmentation, Language Modelling, Machine Reading Comprehension, Multiple-choice, Reading Comprehension, World Knowledge

Machine reading comprehension tasks require a machine reader to answer questions relevant to the given document. In this paper, we present the first free-form multiple-Choice Chinese machine reading Comprehension dataset (C^3), containing 13,369 documents (dialogues or more formally written mixed-genre texts) and their associated 19,577 multiple-choice free-form questions collected from Chinese-as-a-second-language examinations. We present a comprehensive analysis of the prior knowledge (i.e., linguistic, domain-specific, and general world knowledge) needed for these real-world problems. We implement rule-based and popular neural methods and find that there is still a significant performance gap between the best performing model (68.5%) and human readers (96.0%), especially on problems that require prior knowledge. We further study the effects of distractor plausibility and data augmentation based on translated relevant datasets for English on model performance. We expect C^3 to present great challenges to existing systems as answering 86.8% of questions requires both knowledge within and beyond the accompanying document, and we hope that C^3 can serve as a platform to study how to leverage various kinds of prior knowledge to better understand a given written or orally oriented text. C^3 is available at https://dataset.org/c3/.
UniSent: Universal Adaptable Sentiment Lexica for 1000+ Languages
Authors: Ehsaneddin Asgari, Fabienne Braune, Benjamin Roth, Christoph Ringlstetter, Mohammad R. K. Mofrad
Date: 2019-04-21 00:00:00
Proceeding: LREC 2020 5
Tasks: Domain Adaptation, Sentiment Analysis, Unsupervised Domain Adaptation

In this paper, we introduce UniSent universal sentiment lexica for $1000+$ languages. Sentiment lexica are vital for sentiment analysis in absence of document-level annotations, a very common scenario for low-resource languages. To the best of our knowledge, UniSent is the largest sentiment resource to date in terms of the number of covered languages, including many low resource ones. In this work, we use a massively parallel Bible corpus to project sentiment information from English to other languages for sentiment analysis on Twitter data. We introduce a method called DomDrift to mitigate the huge domain mismatch between Bible and Twitter by a confidence weighting scheme that uses domain-specific embeddings to compare the nearest neighbors for a candidate sentiment word in the source (Bible) and target (Twitter) domain. We evaluate the quality of UniSent in a subset of languages for which manually created ground truth was available, Macedonian, Czech, German, Spanish, and French. We show that the quality of UniSent is comparable to manually created sentiment resources when it is used as the sentiment seed for the task of word sentiment prediction on top of embedding representations. In addition, we show that emoticon sentiments could be reliably predicted in the Twitter domain using only UniSent and monolingual embeddings in German, Spanish, French, and Italian. With the publication of this paper, we release the UniSent sentiment lexica.
BERTScore: Evaluating Text Generation with BERT
Authors: Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, Yoav Artzi
Date: 2019-04-21 00:00:00
Proceeding: ICLR 2020 1
Tasks: Image Captioning, Machine Translation, Model Selection, Sentence, Text Generation, Translation

We propose BERTScore, an automatic evaluation metric for text generation. Analogously to common metrics, BERTScore computes a similarity score for each token in the candidate sentence with each token in the reference sentence. However, instead of exact matches, we compute token similarity using contextual embeddings. We evaluate using the outputs of 363 machine translation and image captioning systems. BERTScore correlates better with human judgments and provides stronger model selection performance than existing metrics. Finally, we use an adversarial paraphrase detection task to show that BERTScore is more robust to challenging examples when compared to existing metrics.
Deep Learning for Physical-Layer 5G Wireless Techniques: Opportunities, Challenges and Solutions
Authors: Hongji Huang, Song Guo, Guan Gui, Zhen Yang, Jianhua Zhang, Hikmet Sari, Fumiyuki Adachi
Date: 2019-04-21 00:00:00
Tasks: Deep Learning

The new demands for high-reliability and ultra-high capacity wireless
communication have led to extensive research into 5G communications. However,
the current communication systems, which were designed on the basis of
conventional communication theories, signficantly restrict further performance
improvements and lead to severe limitations. Recently, the emerging deep
learning techniques have been recognized as a promising tool for handling the
complicated communication systems, and their potential for optimizing wireless
communications has been demonstrated. In this article, we first review the
development of deep learning solutions for 5G communication, and then propose
efficient schemes for deep learning-based 5G scenarios. Specifically, the key
ideas for several important deep learningbased communication methods are
presented along with the research opportunities and challenges. In particular,
novel communication frameworks of non-orthogonal multiple access (NOMA),
massive multiple-input multiple-output (MIMO), and millimeter wave (mmWave) are
investigated, and their superior performances are demonstrated. We vision that
the appealing deep learning-based wireless physical layer frameworks will bring
a new direction in communication theories and that this work will move us
forward along this road.
DDGK: Learning Graph Representations for Deep Divergence Graph Kernels
Authors: Rami Al-Rfou, Dustin Zelle, Bryan Perozzi
Date: 2019-04-21 00:00:00
Tasks: Feature Engineering, Graph Attention, Graph Classification, Graph Similarity

Can neural networks learn to compare graphs without feature engineering? In
this paper, we show that it is possible to learn representations for graph
similarity with neither domain knowledge nor supervision (i.e.\ feature
engineering or labeled graphs). We propose Deep Divergence Graph Kernels, an
unsupervised method for learning representations over graphs that encodes a
relaxed notion of graph isomorphism. Our method consists of three parts. First,
we learn an encoder for each anchor graph to capture its structure. Second, for
each pair of graphs, we train a cross-graph attention network which uses the
node representations of an anchor graph to reconstruct another graph. This
approach, which we call isomorphism attention, captures how well the
representations of one graph can encode another. We use the attention-augmented
encoder's predictions to define a divergence score for each pair of graphs.
Finally, we construct an embedding space for all graphs using these pair-wise
divergence scores.
  Unlike previous work, much of which relies on 1) supervision, 2) domain
specific knowledge (e.g. a reliance on Weisfeiler-Lehman kernels), and 3) known
node alignment, our unsupervised method jointly learns node representations,
graph representations, and an attention-based alignment between graphs.
  Our experimental results show that Deep Divergence Graph Kernels can learn an
unsupervised alignment between graphs, and that the learned representations
achieve competitive results when used as features on a number of challenging
graph classification tasks. Furthermore, we illustrate how the learned
attention allows insight into the the alignment of sub-structures across
graphs.
Deep Hough Voting for 3D Object Detection in Point Clouds
Authors: Charles R. Qi, Or Litany, Kaiming He, Leonidas J. Guibas
Date: 2019-04-21 00:00:00
Proceeding: ICCV 2019 10
Tasks: 3D Object Detection, 3D Object Detection From Monocular Images, Object, object-detection, Object Detection

Current 3D object detection methods are heavily influenced by 2D detectors. In order to leverage architectures in 2D detectors, they often convert 3D point clouds to regular grids (i.e., to voxel grids or to bird's eye view images), or rely on detection in 2D images to propose 3D boxes. Few works have attempted to directly detect objects in point clouds. In this work, we return to first principles to construct a 3D detection pipeline for point cloud data and as generic as possible. However, due to the sparse nature of the data -- samples from 2D manifolds in 3D space -- we face a major challenge when directly predicting bounding box parameters from scene points: a 3D object centroid can be far from any surface point thus hard to regress accurately in one step. To address the challenge, we propose VoteNet, an end-to-end 3D object detection network based on a synergy of deep point set networks and Hough voting. Our model achieves state-of-the-art 3D detection on two large datasets of real 3D scans, ScanNet and SUN RGB-D with a simple design, compact model size and high efficiency. Remarkably, VoteNet outperforms previous methods by using purely geometric information without relying on color images.
An image structure model for exact edge detection
Authors: Alessandro Dal Palu'
Date: 2019-04-21 00:00:00
Tasks: Denoising, Edge Detection, model

The paper presents a new model for single channel images low-level
interpretation. The image is decomposed into a graph which captures a complete
set of structural features. The description allows to accurately identify every
edge location and its correct connectivity. The key features of the method are:
vector description of the edges, subpixel precision, and parallelism of the
underlying algorithm. The methodology outperforms classical and state of the
art edge detectors at both conceptual and experimental levels. It also enables
graph based algorithms for higher-level feature extraction. Any image
processing pipeline can benefit from such results: e.g., controlled denoising,
edge preserving filtering, upsampling, compression, vector and graph based
pattern matching, neural network training.
Probabilistic Face Embeddings
Authors: Yichun Shi, Anil K. Jain
Date: 2019-04-21 00:00:00
Proceeding: ICCV 2019 10
Tasks: Face Recognition, Face Verification

Embedding methods have achieved success in face recognition by comparing facial features in a latent semantic space. However, in a fully unconstrained face setting, the facial features learned by the embedding model could be ambiguous or may not even be present in the input face, leading to noisy representations. We propose Probabilistic Face Embeddings (PFEs), which represent each face image as a Gaussian distribution in the latent space. The mean of the distribution estimates the most likely feature values while the variance shows the uncertainty in the feature values. Probabilistic solutions can then be naturally derived for matching and fusing PFEs using the uncertainty information. Empirical evaluation on different baseline models, training datasets and benchmarks show that the proposed method can improve the face recognition performance of deterministic embeddings by converting them into PFEs. The uncertainties estimated by PFEs also serve as good indicators of the potential matching accuracy, which are important for a risk-controlled recognition system.
Solution of Definite Integrals using Functional Link Artificial Neural Networks
Authors: Satyasaran Changdar, Snehangshu Bhattacharjee
Date: 2019-04-21 00:00:00

This paper discusses a new method to solve definite integrals using
artificial neural networks. The objective is to build a neural network that
would be a novel alternative to pre-established numerical methods and with the
help of a learning algorithm, be able to solve definite integrals, by
minimising a well constructed error function. The proposed algorithm, with
respect to existing numerical methods, is effective and precise and well-suited
for purposes which require integration of higher order polynomials. The
observations have been recorded and illustrated in tabular and graphical form.
Integrating Association Rules with Decision Trees in Object-Relational Databases
Authors: Maruthi Rohit Ayyagari
Date: 2019-04-21 00:00:00
Tasks: Classification, General Classification

Research has provided evidence that associative classification produces more
accurate results compared to other classification models. The Classification
Based on Association (CBA) is one of the famous Associative Classification
algorithms that generates accurate classifiers. However, current association
classification algorithms reside external to databases, which reduces the
flexibility of enterprise analytics systems. This paper implements the CBA in
Oracle database using two variant models: hardcoding the CBA in Oracle Data
Mining (ODM) package and Integrating Oracle Apriori model with the Oracle
Decision tree model. We compared the proposed model performance with Naive
Bayes, Support Vector Machine, Random Forests, and Decision Tree over 18
datasets from UCI. Results showed that our models outperformed the original CBA
model with 1 percent and is competitive to chosen classification models over
benchmark datasets.
An improved sex specific and age dependent classification model for Parkinson's diagnosis using handwriting measurement
Authors: Ujjwal Gupta, Hritik Bansal, Deepak Joshi
Date: 2019-04-21 00:00:00
Tasks: Classification, General Classification

Accurate diagnosis is crucial for preventing the progression of Parkinson's, as well as improving the quality of life with individuals with Parkinson's disease. In this paper, we develop a sex-specific and age-dependent classification method to diagnose the Parkinson's disease using the online handwriting recorded from individuals with Parkinson's(n=37;m/f-19/18;age-69.3+-10.9years) and healthy controls(n=38;m/f-20/18;age-62.4+-11.3 years).The sex specific and age dependent classifier was observed significantly outperforming the generalized classifier. An improved accuracy of 83.75%(SD+1.63) with female specific classifier, and 79.55%(SD=1.58) with old age dependent classifier was observed in comparison to 75.76%(SD=1.17) accuracy with the generalized classifier. Finally, combining the age and sex information proved to be encouraging in classification. We performed a rigorous analysis to observe the dominance of sex specific and age dependent features for Parkinson's detection and ranked them using the support vector machine(SVM) ranking method. Distinct set of features were observed to be dominating for higher classification accuracy in different category of classification.
Dynamic Past and Future for Neural Machine Translation
Authors: Zaixiang Zheng, Shu-Jian Huang, Zhaopeng Tu, Xin-yu Dai, Jia-Jun Chen
Date: 2019-04-21 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Machine Translation, NMT, Translation

Previous studies have shown that neural machine translation (NMT) models can benefit from explicitly modeling translated (Past) and untranslated (Future) to groups of translated and untranslated contents through parts-to-wholes assignment. The assignment is learned through a novel variant of routing-by-agreement mechanism (Sabour et al., 2017), namely {\em Guided Dynamic Routing}, where the translating status at each decoding step {\em guides} the routing process to assign each source word to its associated group (i.e., translated or untranslated content) represented by a capsule, enabling translation to be made from holistic context. Experiments show that our approach achieves substantial improvements over both RNMT and Transformer by producing more adequate translations. Extensive analysis demonstrates that our method is highly interpretable, which is able to recognize the translated and untranslated contents as expected.
Intermittent Learning: On-Device Machine Learning on Intermittently Powered System
Authors: Seulki Lee, Bashima Islam, Yubo Luo, Shahriar Nirjon
Date: 2019-04-21 00:00:00
Tasks: BIG-bench Machine Learning

This paper introduces intermittent learning - the goal of which is to enable energy harvested computing platforms capable of executing certain classes of machine learning tasks effectively and efficiently. We identify unique challenges to intermittent learning relating to the data and application semantics of machine learning tasks, and to address these challenges, we devise 1) an algorithm that determines a sequence of actions to achieve the desired learning objective under tight energy constraints, and 2) propose three heuristics that help an intermittent learner decide whether to learn or discard training examples at run-time which increases the energy efficiency of the system. We implement and evaluate three intermittent learning applications that learn the 1) air quality, 2) human presence, and 3) vibration using solar, RF, and kinetic energy harvesters, respectively. We demonstrate that the proposed framework improves the energy efficiency of a learner by up to 100% and cuts down the number of learning examples by up to 50% when compared to state-of-the-art intermittent computing systems that do not implement the proposed intermittent learning framework.
Mesh Learning Using Persistent Homology on the Laplacian Eigenfunctions
Authors: Yunhao Zhang, Haowen Liu, Paul Rosen, Mustafa Hajij
Date: 2019-04-21 00:00:00
Tasks: Topological Data Analysis

We use persistent homology along with the eigenfunctions of the Laplacian to
study similarity amongst triangulated 2-manifolds. Our method relies on
studying the lower-star filtration induced by the eigenfunctions of the
Laplacian. This gives us a shape descriptor that inherits the rich information
encoded in the eigenfunctions of the Laplacian. Moreover, the similarity
between these descriptors can be easily computed using tools that are readily
available in Topological Data Analysis. We provide experiments to illustrate
the effectiveness of the proposed method.
Model Compression with Multi-Task Knowledge Distillation for Web-scale Question Answering System
Authors: Ze Yang, Linjun Shou, Ming Gong, Wutao Lin, Daxin Jiang
Date: 2019-04-21 00:00:00
Tasks: Knowledge Distillation, Model Compression, Question Answering

Deep pre-training and fine-tuning models (like BERT, OpenAI GPT) have
demonstrated excellent results in question answering areas. However, due to the
sheer amount of model parameters, the inference speed of these models is very
slow. How to apply these complex models to real business scenarios becomes a
challenging but practical problem. Previous works often leverage model
compression approaches to resolve this problem. However, these methods usually
induce information loss during the model compression procedure, leading to
incomparable results between compressed model and the original model. To tackle
this challenge, we propose a Multi-task Knowledge Distillation Model (MKDM for
short) for web-scale Question Answering system, by distilling knowledge from
multiple teacher models to a light-weight student model. In this way, more
generalized knowledge can be transferred. The experiment results show that our
method can significantly outperform the baseline methods and even achieve
comparable results with the original teacher models, along with significant
speedup of model inference.
Achieving the Bayes Error Rate in Synchronization and Block Models by SDP, Robustly
Authors: Yingjie Fei, Yudong Chen
Date: 2019-04-21 00:00:00
Tasks: Clustering, Stochastic Block Model, valid

We study the statistical performance of semidefinite programming (SDP)
relaxations for clustering under random graph models. Under the
$\mathbb{Z}_{2}$ Synchronization model, Censored Block Model and Stochastic
Block Model, we show that SDP achieves an error rate of the form \[
\exp\Big[-\big(1-o(1)\big)\bar{n} I^* \Big]. \] Here $\bar{n}$ is an
appropriate multiple of the number of nodes and $I^*$ is an
information-theoretic measure of the signal-to-noise ratio. We provide matching
lower bounds on the Bayes error for each model and therefore demonstrate that
the SDP approach is Bayes optimal. As a corollary, our results imply that SDP
achieves the optimal exact recovery threshold under each model. Furthermore, we
show that SDP is robust: the above bound remains valid under semirandom
versions of the models in which the observed graph is modified by a monotone
adversary. Our proof is based on a novel primal-dual analysis of SDP under a
unified framework for all three models, and the analysis shows that SDP tightly
approximates a joint majority voting procedure.
Beyond Explainability: Leveraging Interpretability for Improved Adversarial Learning
Authors: Devinder Kumar, Ibrahim Ben-Daya, Kanav Vats, Jeffery Feng, Graham Taylor and, Alexander Wong
Date: 2019-04-21 00:00:00

In this study, we propose the leveraging of interpretability for tasks beyond
purely the purpose of explainability. In particular, this study puts forward a
novel strategy for leveraging gradient-based interpretability in the realm of
adversarial examples, where we use insights gained to aid adversarial learning.
More specifically, we introduce the concept of spatially constrained one-pixel
adversarial perturbations, where we guide the learning of such adversarial
perturbations towards more susceptible areas identified via gradient-based
interpretability. Experimental results using different benchmark datasets show
that such a spatially constrained one-pixel adversarial perturbation strategy
can noticeably improve the speed of convergence as well as produce successful
attacks that were also visually difficult to perceive, thus illustrating an
effective use of interpretability methods for tasks outside of the purpose of
purely explainability.
Gaussian Process Regression and Classification under Mathematical Constraints with Learning Guarantees
Authors: Jeremiah Zhe Liu
Date: 2019-04-21 00:00:00
Tasks: Form, General Classification, regression

We introduce constrained Gaussian process (CGP), a Gaussian process model for
random functions that allows easy placement of mathematical constrains (e.g.,
non-negativity, monotonicity, etc) on its sample functions. CGP comes with
closed-form probability density function (PDF), and has the attractive feature
that its posterior distributions for regression and classification are again
CGPs with closed-form expressions. Furthermore, we show that CGP inherents the
optimal theoretical properties of the Gaussian process, e.g. rates of posterior
contraction, due to the fact that CGP is an Gaussian process with a more
efficient model space.