pwc-archive/papers-with-abstracts huggingface dataset:
Cold-start recommendations in Collective Matrix Factorization
Authors: David Cortes
Date: 2018-09-02 00:00:00
Tasks: Recommendation Systems

This work explores the ability of collective matrix factorization models in recommender systems to make predictions about users and items for which there is side information available but no feedback or interactions data, and proposes a new formulation with a faster cold-start prediction formula that can be used in real-time systems. While these cold-start recommendations are not as good as warm-start ones, they were found to be of better quality than non-personalized recommendations, and predictions about new users were found to be more reliable than those about new items. The formulation proposed here resulted in improved cold-start recommendations in many scenarios, at the expense of worse warm-start ones.
Natural Language Person Search Using Deep Reinforcement Learning
Authors: Ankit Shah, Tyler Vuong
Date: 2018-09-02 00:00:00
Tasks: Deep Reinforcement Learning, object-detection, Object Detection, Person Search, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval

Recent success in deep reinforcement learning is having an agent learn how to
play Go and beat the world champion without any prior knowledge of the game. In
that task, the agent has to make a decision on what action to take based on the
positions of the pieces. Person Search is recently explored using natural
language based text description of images for video surveillance applications
(S.Li et.al). We see (Fu.et al) provides an end to end approach for
object-based retrieval using deep reinforcement learning without constraints
placed on which objects are being detected. However, we believe for real-world
applications such as person search defining specific constraints which identify
a person as opposed to starting with a general object detection will have
benefits in terms of performance and computational resources required. In our
task, Deep reinforcement learning would localize the person in an image by
reshaping the sizes of the bounding boxes. Deep Reinforcement learning with
appropriate constraints would look only for the relevant person in the image as
opposed to an unconstrained approach where each individual objects in the image
are ranked. For person search, the agent is trying to form a tight bounding box
around the person in the image who matches the description. The bounding box is
initialized to the full image and at each time step, the agent makes a decision
on how to change the current bounding box so that it has a tighter bound around
the person based on the description of the person and the pixel values of the
current bounding box. After the agent takes an action, it will be given a
reward based on the Intersection over Union (IoU) of the current bounding box
and the ground truth box. Once the agent believes that the bounding box is
covering the person, it will indicate that the person is found.
Trivial Transfer Learning for Low-Resource Neural Machine Translation
Authors: Tom Kocmi, Ondřej Bojar
Date: 2018-09-02 00:00:00
Proceeding: WS 2018 10
Tasks: Low Resource Neural Machine Translation, Low-Resource Neural Machine Translation, Machine Translation, Transfer Learning, Translation

Transfer learning has been proven as an effective technique for neural
machine translation under low-resource conditions. Existing methods require a
common target language, language relatedness, or specific training tricks and
regimes. We present a simple transfer learning method, where we first train a
"parent" model for a high-resource language pair and then continue the training
on a lowresource pair only by replacing the training corpus. This "child" model
performs significantly better than the baseline trained for lowresource pair
only. We are the first to show this for targeting different languages, and we
observe the improvements even for unrelated languages with different alphabets.
IntentsKB: A Knowledge Base of Entity-Oriented Search Intents
Authors: Darío Garigliotti, Krisztian Balog
Date: 2018-09-02 00:00:00

We address the problem of constructing a knowledge base of entity-oriented
search intents. Search intents are defined on the level of entity types, each
comprising of a high-level intent category (property, website, service, or
other), along with a cluster of query terms used to express that intent. These
machine-readable statements can be leveraged in various applications, e.g., for
generating entity cards or query recommendations. By structuring
service-oriented search intents, we take one step towards making entities
actionable. The main contribution of this paper is a pipeline of components we
develop to construct a knowledge base of entity intents. We evaluate
performance both component-wise and end-to-end, and demonstrate that our
approach is able to generate high-quality data.
Contextual Neural Model for Translating Bilingual Multi-Speaker Conversations
Authors: Sameen Maruf, André F. T. Martins, Gholamreza Haffari
Date: 2018-09-02 00:00:00
Proceeding: WS 2018 10
Tasks: Document Translation, Machine Translation, Translation

Recent works in neural machine translation have begun to explore document
translation. However, translating online multi-speaker conversations is still
an open problem. In this work, we propose the task of translating Bilingual
Multi-Speaker Conversations, and explore neural architectures which exploit
both source and target-side conversation histories for this task. To initiate
an evaluation for this task, we introduce datasets extracted from Europarl v7
and OpenSubtitles2016. Our experiments on four language-pairs confirm the
significance of leveraging conversation history, both in terms of BLEU and
manual evaluation.
Towards an Intelligent Edge: Wireless Communication Meets Machine Learning
Authors: Guangxu Zhu, Dongzhu Liu, Yuqing Du, Changsheng You, Jun Zhang, Kaibin Huang
Date: 2018-09-02 00:00:00
Tasks: BIG-bench Machine Learning, Edge-computing

The recent revival of artificial intelligence (AI) is revolutionizing almost
every branch of science and technology. Given the ubiquitous smart mobile
gadgets and Internet of Things (IoT) devices, it is expected that a majority of
intelligent applications will be deployed at the edge of wireless networks.
This trend has generated strong interests in realizing an "intelligent edge" to
support AI-enabled applications at various edge devices. Accordingly, a new
research area, called edge learning, emerges, which crosses and revolutionizes
two disciplines: wireless communication and machine learning. A major theme in
edge learning is to overcome the limited computing power, as well as limited
data, at each edge device. This is accomplished by leveraging the mobile edge
computing (MEC) platform and exploiting the massive data distributed over a
large number of edge devices. In such systems, learning from distributed data
and communicating between the edge server and devices are two critical and
coupled aspects, and their fusion poses many new research challenges. This
article advocates a new set of design principles for wireless communication in
edge learning, collectively called learning-driven communication. Illustrative
examples are provided to demonstrate the effectiveness of these design
principles, and unique research opportunities are identified.
Identifying Land Patterns from Satellite Imagery in Amazon Rainforest using Deep Learning
Authors: Somnath Rakshit, Soumyadeep Debnath, Dhiman Mondal
Date: 2018-09-02 00:00:00
Tasks: General Classification, image-classification, Image Classification

The Amazon rainforests have been suffering widespread damage, both via
natural and artificial means. Every minute, it is estimated that the world
loses forest cover the size of 48 football fields. Deforestation in the Amazon
rainforest has led to drastically reduced biodiversity, loss of habitat,
climate change, and other biological losses. In this respect, it has become
essential to track how the nature of these forests change over time. Image
classification using deep learning can help speed up this process by removing
the manual task of classifying each image. Here, it is shown how convolutional
neural networks can be used to track changes in land patterns in the Amazon
rainforests. In this work, a testing accuracy of 96.71% was obtained. This can
help governments and other agencies to track changes in land patterns more
effectively and accurately.
Chittron: An Automatic Bangla Image Captioning System
Authors: Motiur Rahman, Nabeel Mohammed, Nafees Mansoor, Sifat Momen
Date: 2018-09-02 00:00:00
Tasks: Caption Generation, Image Captioning, Language Modeling, Language Modelling

Automatic image caption generation aims to produce an accurate description of
an image in natural language automatically. However, Bangla, the fifth most
widely spoken language in the world, is lagging considerably in the research
and development of such domain. Besides, while there are many established data
sets to related to image annotation in English, no such resource exists for
Bangla yet. Hence, this paper outlines the development of "Chittron", an
automatic image captioning system in Bangla. Moreover, to address the data set
availability issue, a collection of 16,000 Bangladeshi contextual images has
been accumulated and manually annotated in Bangla. This data set is then used
to train a model which integrates a pre-trained VGG16 image embedding model
with stacked LSTM layers. The model is trained to predict the caption when the
input is an image, one word at a time. The results show that the model has
successfully been able to learn a working language model and to generate
captions of images quite accurately in many cases. The results are evaluated
mainly qualitatively. However, BLEU scores are also reported. It is expected
that a better result can be obtained with a bigger and more varied data set.
Look Across Elapse: Disentangled Representation Learning and Photorealistic Cross-Age Face Synthesis for Age-Invariant Face Recognition
Authors: Jian Zhao, Yu Cheng, Yi Cheng, Yang Yang, Haochong Lan, Fang Zhao, Lin Xiong, Yan Xu, Jianshu Li, Sugiri Pranata, ShengMei Shen, Junliang Xing, Hengzhu Liu, Shuicheng Yan, Jiashi Feng
Date: 2018-09-02 00:00:00
Tasks: Age-Invariant Face Recognition, Benchmarking, Face Generation, Face Recognition, Face Verification, MORPH, Representation Learning

Despite the remarkable progress in face recognition related technologies,
reliably recognizing faces across ages still remains a big challenge. The
appearance of a human face changes substantially over time, resulting in
significant intra-class variations. As opposed to current techniques for
age-invariant face recognition, which either directly extract age-invariant
features for recognition, or first synthesize a face that matches target age
before feature extraction, we argue that it is more desirable to perform both
tasks jointly so that they can leverage each other. To this end, we propose a
deep Age-Invariant Model (AIM) for face recognition in the wild with three
distinct novelties. First, AIM presents a novel unified deep architecture
jointly performing cross-age face synthesis and recognition in a mutual
boosting way. Second, AIM achieves continuous face rejuvenation/aging with
remarkable photorealistic and identity-preserving properties, avoiding the
requirement of paired data and the true age of testing samples. Third, we
develop effective and novel training strategies for end-to-end learning the
whole deep architecture, which generates powerful age-invariant face
representations explicitly disentangled from the age variation. Moreover, we
propose a new large-scale Cross-Age Face Recognition (CAFR) benchmark dataset
to facilitate existing efforts and push the frontiers of age-invariant face
recognition research. Extensive experiments on both our CAFR and several other
cross-age datasets (MORPH, CACD and FG-NET) demonstrate the superiority of the
proposed AIM model over the state-of-the-arts. Benchmarking our model on one of
the most popular unconstrained face recognition datasets IJB-C additionally
verifies the promising generalizability of AIM in recognizing faces in the
wild.
Future-Prediction-Based Model for Neural Machine Translation
Authors: Bingzhen Wei, Junyang Lin
Date: 2018-09-02 00:00:00
Tasks: Future prediction, Machine Translation, NMT, Prediction, Translation

We propose a novel model for Neural Machine Translation (NMT). Different from
the conventional method, our model can predict the future text length and words
at each decoding time step so that the generation can be helped with the
information from the future prediction. With such information, the model does
not stop generation without having translated enough content. Experimental
results demonstrate that our model can significantly outperform the baseline
models. Besides, our analysis reflects that our model is effective in the
prediction of the length and words of the untranslated content.
Chinese Pinyin Aided IME, Input What You Have Not Keystroked Yet
Authors: Yafang Huang, Hai Zhao
Date: 2018-09-02 00:00:00
Proceeding: EMNLP 2018 10

Chinese pinyin input method engine (IME) converts pinyin into character so
that Chinese characters can be conveniently inputted into computer through
common keyboard. IMEs work relying on its core component, pinyin-to-character
conversion (P2C). Usually Chinese IMEs simply predict a list of character
sequences for user choice only according to user pinyin input at each turn.
However, Chinese inputting is a multi-turn online procedure, which can be
supposed to be exploited for further user experience promoting. This paper thus
for the first time introduces a sequence-to-sequence model with gated-attention
mechanism for the core task in IMEs. The proposed neural P2C model is learned
by encoding previous input utterance as extra context to enable our IME capable
of predicting character sequence with incomplete pinyin input. Our model is
evaluated in different benchmark datasets showing great user experience
improvement compared to traditional models, which demonstrates the first
engineering practice of building Chinese aided IME.
Exploring Gap Filling as a Cheaper Alternative to Reading Comprehension Questionnaires when Evaluating Machine Translation for Gisting
Authors: Mikel L. Forcada, Carolina Scarton, Lucia Specia, Barry Haddow, Alexandra Birch
Date: 2018-09-02 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Reading Comprehension, Sentence, Translation

A popular application of machine translation (MT) is gisting: MT is consumed
as is to make sense of text in a foreign language. Evaluation of the usefulness
of MT for gisting is surprisingly uncommon. The classical method uses reading
comprehension questionnaires (RCQ), in which informants are asked to answer
professionally-written questions in their language about a foreign text that
has been machine-translated into their language. Recently, gap-filling (GF), a
form of cloze testing, has been proposed as a cheaper alternative to RCQ. In
GF, certain words are removed from reference translations and readers are asked
to fill the gaps left using the machine-translated text as a hint. This paper
reports, for thefirst time, a comparative evaluation, using both RCQ and GF, of
translations from multiple MT systems for the same foreign texts, and a
systematic study on the effect of variables such as gap density, gap-selection
strategies, and document context in GF. The main findings of the study are: (a)
both RCQ and GF clearly identify MT to be useful, (b) global RCQ and GF
rankings for the MT systems are mostly in agreement, (c) GF scores vary very
widely across informants, making comparisons among MT systems hard, and (d)
unlike RCQ, which is framed around documents, GF evaluation can be framed at
the sentence level. These findings support the use of GF as a cheaper
alternative to RCQ.
Enhancing Stock Market Prediction with Extended Coupled Hidden Markov Model over Multi-Sourced Data
Authors: Xi Zhang, Yixuan Li, Senzhang Wang, Binxing Fang, Philip S. Yu
Date: 2018-09-02 00:00:00
Tasks: Prediction, Stock Market Prediction, Stock Prediction

Traditional stock market prediction methods commonly only utilize the
historical trading data, ignoring the fact that stock market fluctuations can
be impacted by various other information sources such as stock related events.
Although some recent works propose event-driven prediction approaches by
considering the event data, how to leverage the joint impacts of multiple data
sources still remains an open research problem. In this work, we study how to
explore multiple data sources to improve the performance of the stock
prediction. We introduce an Extended Coupled Hidden Markov Model incorporating
the news events with the historical trading data. To address the data sparsity
issue of news events for each single stock, we further study the fluctuation
correlations between the stocks and incorporate the correlations into the model
to facilitate the prediction task. Evaluations on China A-share market data in
2016 show the superior performance of our model against previous methods.
Towards Automated Customer Support
Authors: Momchil Hardalov, Ivan Koychev, Preslav Nakov
Date: 2018-09-02 00:00:00
Tasks: Information Retrieval, Machine Translation, Question Answering, Retrieval, Translation

Recent years have seen growing interest in conversational agents, such as
chatbots, which are a very good fit for automated customer support because the
domain in which they need to operate is narrow. This interest was in part
inspired by recent advances in neural machine translation, esp. the rise of
sequence-to-sequence (seq2seq) and attention-based models such as the
Transformer, which have been applied to various other tasks and have opened new
research directions in question answering, chatbots, and conversational
systems. Still, in many cases, it might be feasible and even preferable to use
simple information retrieval techniques. Thus, here we compare three different
models:(i) a retrieval model, (ii) a sequence-to-sequence model with attention,
and (iii) Transformer. Our experiments with the Twitter Customer Support
Dataset, which contains over two million posts from customer support services
of twenty major brands, show that the seq2seq model outperforms the other two
in terms of semantics and word overlap.
Learning to Navigate for Fine-grained Classification
Authors: Ze Yang, Tiange Luo, Dong Wang, Zhiqiang Hu, Jun Gao, Li-Wei Wang
Date: 2018-09-02 00:00:00
Proceeding: ECCV 2018 9
Tasks: Fine-Grained Image Classification, General Classification, Informativeness

Fine-grained classification is challenging due to the difficulty of finding
discriminative features. Finding those subtle traits that fully characterize
the object is not straightforward. To handle this circumstance, we propose a
novel self-supervision mechanism to effectively localize informative regions
without the need of bounding-box/part annotations. Our model, termed NTS-Net
for Navigator-Teacher-Scrutinizer Network, consists of a Navigator agent, a
Teacher agent and a Scrutinizer agent. In consideration of intrinsic
consistency between informativeness of the regions and their probability being
ground-truth class, we design a novel training paradigm, which enables
Navigator to detect most informative regions under the guidance from Teacher.
After that, the Scrutinizer scrutinizes the proposed regions from Navigator and
makes predictions. Our model can be viewed as a multi-agent cooperation,
wherein agents benefit from each other, and make progress together. NTS-Net can
be trained end-to-end, while provides accurate fine-grained classification
predictions as well as highly informative regions during inference. We achieve
state-of-the-art performance in extensive benchmark datasets.
Weakly-Supervised Neural Text Classification
Authors: Yu Meng, Jiaming Shen, Chao Zhang, Jiawei Han
Date: 2018-09-02 00:00:00
Tasks: Classification, Feature Engineering, General Classification, text-classification, Text Classification

Deep neural networks are gaining increasing popularity for the classic text
classification task, due to their strong expressive power and less requirement
for feature engineering. Despite such attractiveness, neural text
classification models suffer from the lack of training data in many real-world
applications. Although many semi-supervised and weakly-supervised text
classification models exist, they cannot be easily applied to deep neural
models and meanwhile support limited supervision types. In this paper, we
propose a weakly-supervised method that addresses the lack of training data in
neural text classification. Our method consists of two modules: (1) a
pseudo-document generator that leverages seed information to generate
pseudo-labeled documents for model pre-training, and (2) a self-training module
that bootstraps on real unlabeled data for model refinement. Our method has the
flexibility to handle different types of weak supervision and can be easily
integrated into existing deep neural models for text classification. We have
performed extensive experiments on three real-world datasets from different
domains. The results demonstrate that our proposed method achieves inspiring
performance without requiring excessive training data and outperforms baseline
methods significantly.
Stochastic Dynamics for Video Infilling
Authors: Qiangeng Xu, Hanwang Zhang, Weiyue Wang, Peter N. Belhumeur, Ulrich Neumann
Date: 2018-09-01 00:00:00

In this paper, we introduce a stochastic dynamics video infilling (SDVI) framework to generate frames between long intervals in a video. Our task differs from video interpolation which aims to produce transitional frames for a short interval between every two frames and increase the temporal resolution. Our task, namely video infilling, however, aims to infill long intervals with plausible frame sequences. Our framework models the infilling as a constrained stochastic generation process and sequentially samples dynamics from the inferred distribution. SDVI consists of two parts: (1) a bi-directional constraint propagation module to guarantee the spatial-temporal coherence among frames, (2) a stochastic sampling process to generate dynamics from the inferred distributions. Experimental results show that SDVI can generate clear frame sequences with varying contents. Moreover, motions in the generated sequence are realistic and able to transfer smoothly from the given start frame to the terminal frame. Our project site is https://xharlie.github.io/projects/project_sites/SDVI/video_results.html