pwc-archive/papers-with-abstracts huggingface dataset:
Guidelines and Annotation Framework for Arabic Author Profiling
Authors: Wajdi Zaghouani, Anis Charfi
Date: 2018-08-23 00:00:00
Tasks: Author Profiling

In this paper, we present the annotation pipeline and the guidelines we wrote
as part of an effort to create a large manually annotated Arabic author
profiling dataset from various social media sources covering 16 Arabic
countries and 11 dialectal regions. The target size of the annotated ARAP-Tweet
corpus is more than 2.4 million words. We illustrate and summarize our general
and dialect-specific guidelines for each of the dialectal regions selected. We
also present the annotation framework and logistics. We control the annotation
quality frequently by computing the inter-annotator agreement during the
annotation process. Finally, we describe the issues encountered during the
annotation phase, especially those related to the peculiarities of Arabic
dialectal varieties as used in social media.
Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images
Authors: Michele Volpi, Devis Tuia
Date: 2018-08-23 00:00:00
Tasks: Multi-Task Learning, Semantic Segmentation

When approaching the semantic segmentation of overhead imagery in the
decimeter spatial resolution range, successful strategies usually combine
powerful methods to learn the visual appearance of the semantic classes (e.g.
convolutional neural networks) with strategies for spatial regularization (e.g.
graphical models such as conditional random fields). In this paper, we propose
a method to learn evidence in the form of semantic class likelihoods, semantic
boundaries across classes and shallow-to-deep visual features, each one modeled
by a multi-task convolutional neural network architecture. We combine this
bottom-up information with top-down spatial regularization encoded by a
conditional random field model optimizing the label space across a hierarchy of
segments with constraints related to structural, spatial and data-dependent
pairwise relationships between regions. Our results show that such strategy
provide better regularization than a series of strong baselines reflecting
state-of-the-art technologies. The proposed strategy offers a flexible and
principled framework to include several sources of visual and structural
information, while allowing for different degrees of spatial regularization
accounting for priors about the expected output structures.
Arap-Tweet: A Large Multi-Dialect Twitter Corpus for Gender, Age and Language Variety Identification
Authors: Wajdi Zaghouani, Anis Charfi
Date: 2018-08-23 00:00:00
Proceeding: LREC 2018 5
Tasks: Author Profiling

In this paper, we present Arap-Tweet, which is a large-scale and
multi-dialectal corpus of Tweets from 11 regions and 16 countries in the Arab
world representing the major Arabic dialectal varieties. To build this corpus,
we collected data from Twitter and we provided a team of experienced annotators
with annotation guidelines that they used to annotate the corpus for age
categories, gender, and dialectal variety. During the data collection effort,
we based our search on distinctive keywords that are specific to the different
Arabic dialects and we also validated the location using Twitter API. In this
paper, we report on the corpus data collection and annotation efforts. We also
present some issues that we encountered during these phases. Then, we present
the results of the evaluation performed to ensure the consistency of the
annotation. The provided corpus will enrich the limited set of available
language resources for Arabic and will be an invaluable enabler for developing
author profiling tools and NLP tools for Arabic.
PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition
Authors: Haoxuan You, Yifan Feng, Rongrong Ji, Yue Gao
Date: 2018-08-23 00:00:00
Tasks: 3D Object Recognition, 3D Shape Classification, 3D Shape Recognition, 3D Shape Representation, Object Recognition, Retrieval

3D object recognition has attracted wide research attention in the field of
multimedia and computer vision. With the recent proliferation of deep learning,
various deep models with different representations have achieved the
state-of-the-art performance. Among them, point cloud and multi-view based 3D
shape representations are promising recently, and their corresponding deep
models have shown significant performance on 3D shape recognition. However,
there is little effort concentrating point cloud data and multi-view data for
3D shape representation, which is, in our consideration, beneficial and
compensated to each other. In this paper, we propose the Point-View Network
(PVNet), the first framework integrating both the point cloud and the
multi-view data towards joint 3D shape recognition. More specifically, an
embedding attention fusion scheme is proposed that could employ high-level
features from the multi-view data to model the intrinsic correlation and
discriminability of different structure features from the point cloud data. In
particular, the discriminative descriptions are quantified and leveraged as the
soft attention mask to further refine the structure feature of the 3D shape. We
have evaluated the proposed method on the ModelNet40 dataset for 3D shape
classification and retrieval tasks. Experimental results and comparisons with
state-of-the-art methods demonstrate that our framework can achieve superior
performance.
Exploring Shared Structures and Hierarchies for Multiple NLP Tasks
Authors: Junkun Chen, Kaiyu Chen, Xinchi Chen, Xipeng Qiu, Xuanjing Huang
Date: 2018-08-23 00:00:00
Tasks: General Classification, Multi-Task Learning, Neural Architecture Search, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), text-classification, Text Classification

Designing shared neural architecture plays an important role in multi-task
learning. The challenge is that finding an optimal sharing scheme heavily
relies on the expert knowledge and is not scalable to a large number of diverse
tasks. Inspired by the promising work of neural architecture search (NAS), we
apply reinforcement learning to automatically find possible shared architecture
for multi-task learning. Specifically, we use a controller to select from a set
of shareable modules and assemble a task-specific architecture, and repeat the
same procedure for other tasks. The controller is trained with reinforcement
learning to maximize the expected accuracies for all tasks. We conduct
extensive experiments on two types of tasks, text classification and sequence
labeling, which demonstrate the benefits of our approach.
Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks
Authors: Michele Polese, Rittwik Jana, Velin Kounev, Ke Zhang, Supratim Deb, Michele Zorzi
Date: 2018-08-23 00:00:00
Tasks: BIG-bench Machine Learning

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers' view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.
Playing 20 Question Game with Policy-Based Reinforcement Learning
Authors: Huang Hu, Xianchao Wu, Bingfeng Luo, Chongyang Tao, Can Xu, Wei Wu, Zhan Chen
Date: 2018-08-23 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Object, Question Selection, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The 20 Questions (Q20) game is a well known game which encourages deductive reasoning and creativity. In the game, the answerer first thinks of an object such as a famous person or a kind of animal. Then the questioner tries to guess the object by asking 20 questions. In a Q20 game system, the user is considered as the answerer while the system itself acts as the questioner which requires a good strategy of question selection to figure out the correct object and win the game. However, the optimal policy of question selection is hard to be derived due to the complexity and volatility of the game environment. In this paper, we propose a novel policy-based Reinforcement Learning (RL) method, which enables the questioner agent to learn the optimal policy of question selection through continuous interactions with users. To facilitate training, we also propose to use a reward network to estimate the more informative reward. Compared to previous methods, our RL method is robust to noisy answers and does not rely on the Knowledge Base of objects. Experimental results show that our RL method clearly outperforms an entropy-based engineering system and has competitive performance in a noisy-free simulation environment.
Attention-Guided Answer Distillation for Machine Reading Comprehension
Authors: Minghao Hu, Yuxing Peng, Furu Wei, Zhen Huang, Dongsheng Li, Nan Yang, Ming Zhou
Date: 2018-08-23 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Knowledge Distillation, Machine Reading Comprehension, Reading Comprehension

Despite that current reading comprehension systems have achieved significant
advancements, their promising performances are often obtained at the cost of
making an ensemble of numerous models. Besides, existing approaches are also
vulnerable to adversarial attacks. This paper tackles these problems by
leveraging knowledge distillation, which aims to transfer knowledge from an
ensemble model to a single model. We first demonstrate that vanilla knowledge
distillation applied to answer span prediction is effective for reading
comprehension systems. We then propose two novel approaches that not only
penalize the prediction on confusing answers but also guide the training with
alignment information distilled from the ensemble. Experiments show that our
best student model has only a slight drop of 0.4% F1 on the SQuAD test set
compared to the ensemble teacher, while running 12x faster during inference. It
even outperforms the teacher on adversarial SQuAD datasets and NarrativeQA
benchmark.
DOPING: Generative Data Augmentation for Unsupervised Anomaly Detection with GAN
Authors: Swee Kiat Lim, Yi Loo, Ngoc-Trung Tran, Ngai-Man Cheung, Gemma Roig, Yuval Elovici
Date: 2018-08-23 00:00:00
Tasks: Anomaly Detection, Data Augmentation, Generative Adversarial Network, Unsupervised Anomaly Detection

Recently, the introduction of the generative adversarial network (GAN) and
its variants has enabled the generation of realistic synthetic samples, which
has been used for enlarging training sets. Previous work primarily focused on
data augmentation for semi-supervised and supervised tasks. In this paper, we
instead focus on unsupervised anomaly detection and propose a novel generative
data augmentation framework optimized for this task. In particular, we propose
to oversample infrequent normal samples - normal samples that occur with small
probability, e.g., rare normal events. We show that these samples are
responsible for false positives in anomaly detection. However, oversampling of
infrequent normal samples is challenging for real-world high-dimensional data
with multimodal distributions. To address this challenge, we propose to use a
GAN variant known as the adversarial autoencoder (AAE) to transform the
high-dimensional multimodal data distributions into low-dimensional unimodal
latent distributions with well-defined tail probability. Then, we
systematically oversample at the `edge' of the latent distributions to increase
the density of infrequent normal samples. We show that our oversampling
pipeline is a unified one: it is generally applicable to datasets with
different complex data distributions. To the best of our knowledge, our method
is the first data augmentation technique focused on improving performance in
unsupervised anomaly detection. We validate our method by demonstrating
consistent improvements across several real-world datasets.
Weakly-supervised Neural Semantic Parsing with a Generative Ranker
Authors: Jianpeng Cheng, Mirella Lapata
Date: 2018-08-23 00:00:00
Proceeding: CONLL 2018 10
Tasks: Semantic Parsing

Weakly-supervised semantic parsers are trained on utterance-denotation pairs,
treating logical forms as latent. The task is challenging due to the large
search space and spuriousness of logical forms. In this paper we introduce a
neural parser-ranker system for weakly-supervised semantic parsing. The parser
generates candidate tree-structured logical forms from utterances using clues
of denotations. These candidates are then ranked based on two criterion: their
likelihood of executing to the correct denotation, and their agreement with the
utterance semantics. We present a scheduled training procedure to balance the
contribution of the two objectives. Furthermore, we propose to use a neurally
encoded lexicon to inject prior domain knowledge to the model. Experiments on
three Freebase datasets demonstrate the effectiveness of our semantic parser,
achieving results within the state-of-the-art range.
Exploiting Rich Syntactic Information for Semantic Parsing with Graph-to-Sequence Model
Authors: Kun Xu, Lingfei Wu, Zhiguo Wang, Mo Yu, Li-Wei Chen, Vadim Sheinin
Date: 2018-08-23 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Graph-to-Sequence, Semantic Parsing

Existing neural semantic parsers mainly utilize a sequence encoder, i.e., a
sequential LSTM, to extract word order features while neglecting other valuable
syntactic information such as dependency graph or constituent trees. In this
paper, we first propose to use the \textit{syntactic graph} to represent three
types of syntactic information, i.e., word order, dependency and constituency
features. We further employ a graph-to-sequence model to encode the syntactic
graph and decode a logical form. Experimental results on benchmark datasets
show that our model is comparable to the state-of-the-art on Jobs640, ATIS and
Geo880. Experimental results on adversarial examples demonstrate the robustness
of the model is also improved by encoding more syntactic information.
Latent Dirichlet Allocation for Internet Price War
Authors: Chenchen Li, Xiang Yan, Xiaotie Deng, Yuan Qi, Wei Chu, Le Song, Junlong Qiao, Jianshan He, Junwu Xiong
Date: 2018-08-23 00:00:00

Internet market makers are always facing intense competitive environment,
where personalized price reductions or discounted coupons are provided for
attracting more customers. Participants in such a price war scenario have to
invest a lot to catch up with other competitors. However, such a huge cost of
money may not always lead to an improvement of market share. This is mainly due
to a lack of information about others' strategies or customers' willingness
when participants develop their strategies.
  In order to obtain this hidden information through observable data, we study
the relationship between companies and customers in the Internet price war.
Theoretically, we provide a formalization of the problem as a stochastic game
with imperfect and incomplete information. Then we develop a variant of Latent
Dirichlet Allocation (LDA) to infer latent variables under the current market
environment, which represents the preferences of customers and strategies of
competitors. To our best knowledge, it is the first time that LDA is applied to
game scenario.
  We conduct simulated experiments where our LDA model exhibits a significant
improvement on finding strategies in the Internet price war by including all
available market information of the market maker's competitors. And the model
is applied to an open dataset for real business. Through comparisons on the
likelihood of prediction for users' behavior and distribution distance between
inferred opponent's strategy and the real one, our model is shown to be able to
provide a better understanding for the market environment.
  Our work marks a successful learning method to infer latent information in
the environment of price war by the LDA modeling, and sets an example for
related competitive applications to follow.
Review-Driven Multi-Label Music Style Classification by Exploiting Style Correlations
Authors: Guangxiang Zhao, Jingjing Xu, Qi Zeng, Xuancheng Ren
Date: 2018-08-23 00:00:00
Proceeding: NAACL 2019 6
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

This paper explores a new natural language processing task, review-driven
multi-label music style classification. This task requires the system to
identify multiple styles of music based on its reviews on websites. The biggest
challenge lies in the complicated relations of music styles. It has brought
failure to many multi-label classification methods. To tackle this problem, we
propose a novel deep learning approach to automatically learn and exploit style
correlations. The proposed method consists of two parts: a label-graph based
neural network, and a soft training mechanism with correlation-based continuous
label representation. Experimental results show that our approach achieves
large improvements over the baselines on the proposed dataset. Especially, the
micro F1 is improved from 53.9 to 64.5, and the one-error is reduced from 30.5
to 22.6. Furthermore, the visualized analysis shows that our approach performs
well in capturing style correlations.
Structured Interpretation of Temporal Relations
Authors: Yuchen Zhang, Nianwen Xue
Date: 2018-08-23 00:00:00
Proceeding: LREC 2018 5

Temporal relations between events and time expressions in a document are
often modeled in an unstructured manner where relations between individual
pairs of time expressions and events are considered in isolation. This often
results in inconsistent and incomplete annotation and computational modeling.
We propose a novel annotation approach where events and time expressions in a
document form a dependency tree in which each dependency relation corresponds
to an instance of temporal anaphora where the antecedent is the parent and the
anaphor is the child. We annotate a corpus of 235 documents using this approach
in the two genres of news and narratives, with 48 documents doubly annotated.
We report a stable and high inter-annotator agreement on the doubly annotated
subset, validating our approach, and perform a quantitative comparison between
the two genres of the entire corpus. We make this corpus publicly available.
Caveats for information bottleneck in deterministic scenarios
Authors: Artemy Kolchinsky, Brendan D. Tracey, Steven Van Kuyk
Date: 2018-08-23 00:00:00
Proceeding: ICLR 2019 5

Information bottleneck (IB) is a method for extracting information from one
random variable $X$ that is relevant for predicting another random variable
$Y$. To do so, IB identifies an intermediate "bottleneck" variable $T$ that has
low mutual information $I(X;T)$ and high mutual information $I(Y;T)$. The "IB
curve" characterizes the set of bottleneck variables that achieve maximal
$I(Y;T)$ for a given $I(X;T)$, and is typically explored by maximizing the "IB
Lagrangian", $I(Y;T) - \beta I(X;T)$. In some cases, $Y$ is a deterministic
function of $X$, including many classification problems in supervised learning
where the output class $Y$ is a deterministic function of the input $X$. We
demonstrate three caveats when using IB in any situation where $Y$ is a
deterministic function of $X$: (1) the IB curve cannot be recovered by
maximizing the IB Lagrangian for different values of $\beta$; (2) there are
"uninteresting" trivial solutions at all points of the IB curve; and (3) for
multi-layer classifiers that achieve low prediction error, different layers
cannot exhibit a strict trade-off between compression and prediction, contrary
to a recent proposal. We also show that when $Y$ is a small perturbation away
from being a deterministic function of $X$, these three caveats arise in an
approximate way. To address problem (1), we propose a functional that, unlike
the IB Lagrangian, can recover the IB curve in all cases. We demonstrate the
three caveats on the MNIST dataset.
Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models
Authors: Nathan J Olliverre, Guang Yang, Gregory Slabaugh, Constantino Carlos Reyes-Aldasoro, Eduardo Alonso
Date: 2018-08-23 00:00:00

Magnetic Resonance Spectroscopy (MRS) provides valuable information to help
with the identification and understanding of brain tumors, yet MRS is not a
widely available medical imaging modality. Aiming to counter this issue, this
research draws on the advancements in machine learning techniques in other
fields for the generation of artificial data. The generated methods were tested
through the evaluation of their output against that of a real-world labelled
MRS brain tumor data-set. Furthermore the resultant output from the generative
techniques were each used to train separate traditional classifiers which were
tested on a subset of the real MRS brain tumor dataset. The results suggest
that there exist methods capable of producing accurate, ground truth based MRS
voxels. These findings indicate that through generative techniques, large
datasets can be made available for training deep, learning models for the use
in brain tumor diagnosis.
TreeGAN: Syntax-Aware Sequence Generation with Generative Adversarial Networks
Authors: Xinyue Liu, Xiangnan Kong, Lei Liu, Kuorong Chiang
Date: 2018-08-22 00:00:00
Tasks: Image Generation

Generative Adversarial Networks (GANs) have shown great capacity on image
generation, in which a discriminative model guides the training of a generative
model to construct images that resemble real images. Recently, GANs have been
extended from generating images to generating sequences (e.g., poems, music and
codes). Existing GANs on sequence generation mainly focus on general sequences,
which are grammar-free. In many real-world applications, however, we need to
generate sequences in a formal language with the constraint of its
corresponding grammar. For example, to test the performance of a database, one
may want to generate a collection of SQL queries, which are not only similar to
the queries of real users, but also follow the SQL syntax of the target
database. Generating such sequences is highly challenging because both the
generator and discriminator of GANs need to consider the structure of the
sequences and the given grammar in the formal language. To address these
issues, we study the problem of syntax-aware sequence generation with GANs, in
which a collection of real sequences and a set of pre-defined grammatical rules
are given to both discriminator and generator. We propose a novel GAN
framework, namely TreeGAN, to incorporate a given Context-Free Grammar (CFG)
into the sequence generation process. In TreeGAN, the generator employs a
recurrent neural network (RNN) to construct a parse tree. Each generated parse
tree can then be translated to a valid sequence of the given grammar. The
discriminator uses a tree-structured RNN to distinguish the generated trees
from real trees. We show that TreeGAN can generate sequences for any CFG and
its generation fully conforms with the given syntax. Experiments on synthetic
and real data sets demonstrated that TreeGAN significantly improves the quality
of the sequence generation in context-free languages.