pwc-archive/papers-with-abstracts huggingface dataset:
Rethinking Action Spaces for Reinforcement Learning in End-to-end Dialog Agents with Latent Variable Models
Authors: Tiancheng Zhao, Kaige Xie, Maxine Eskenazi
Date: 2019-02-23 00:00:00
Proceeding: NAACL 2019 6
Tasks: Decision Making, Dialogue Generation, Dialogue Management, Goal-Oriented Dialogue Systems, Policy Gradient Methods, Reinforcement Learning, Variational Inference

Defining action spaces for conversational agents and optimizing their
decision-making process with reinforcement learning is an enduring challenge.
Common practice has been to use handcrafted dialog acts, or the output
vocabulary, e.g. in neural encoder decoders, as the action spaces. Both have
their own limitations. This paper proposes a novel latent action framework that
treats the action spaces of an end-to-end dialog agent as latent variables and
develops unsupervised methods in order to induce its own action space from the
data. Comprehensive experiments are conducted examining both continuous and
discrete action types and two different optimization methods based on
stochastic variational inference. Results show that the proposed latent actions
achieve superior empirical performance improvement over previous word-level
policy gradient methods on both DealOrNoDeal and MultiWoz dialogs. Our detailed
analysis also provides insights about various latent variable approaches for
policy learning and can serve as a foundation for developing better latent
actions in future research.
ABI Neural Ensemble Model for Gender Prediction Adapt Bar-Ilan Submission for the CLIN29 Shared Task on Gender Prediction
Authors: Eva Vanmassenhove, Amit Moryossef, Alberto Poncelas, Andy Way, Dimitar Shterionov
Date: 2019-02-23 00:00:00
Tasks: Gender Prediction, Prediction

We present our system for the CLIN29 shared task on cross-genre gender
detection for Dutch. We experimented with a multitude of neural models (CNN,
RNN, LSTM, etc.), more "traditional" models (SVM, RF, LogReg, etc.), different
feature sets as well as data pre-processing. The final results suggested that
using tokenized, non-lowercased data works best for most of the neural models,
while a combination of word clusters, character trigrams and word lists showed
to be most beneficial for the majority of the more "traditional" (that is,
non-neural) models, beating features used in previous tasks such as n-grams,
character n-grams, part-of-speech tags and combinations thereof. In
contradiction with the results described in previous comparable shared tasks,
our neural models performed better than our best traditional approaches with
our best feature set-up. Our final model consisted of a weighted ensemble model
combining the top 25 models. Our final model won both the in-domain gender
prediction task and the cross-genre challenge, achieving an average accuracy of
64.93% on the in-domain gender prediction task, and 56.26% on cross-genre
gender prediction.
Evidence Sentence Extraction for Machine Reading Comprehension
Authors: Hai Wang, Dian Yu, Kai Sun, Jianshu Chen, Dong Yu, David Mcallester, Dan Roth
Date: 2019-02-23 00:00:00
Proceeding: CONLL 2019 11
Tasks: Machine Reading Comprehension, Multiple-choice, Open-Domain Question Answering, Question Answering, Reading Comprehension, Sentence

Remarkable success has been achieved in the last few years on some limited machine reading comprehension (MRC) tasks. However, it is still difficult to interpret the predictions of existing MRC models. In this paper, we focus on extracting evidence sentences that can explain or support the answers of multiple-choice MRC tasks, where the majority of answer options cannot be directly extracted from reference documents. Due to the lack of ground truth evidence sentence labels in most cases, we apply distant supervision to generate imperfect labels and then use them to train an evidence sentence extractor. To denoise the noisy labels, we apply a recently proposed deep probabilistic logic learning framework to incorporate both sentence-level and cross-sentence linguistic indicators for indirect supervision. We feed the extracted evidence sentences into existing MRC models and evaluate the end-to-end performance on three challenging multiple-choice MRC datasets: MultiRC, RACE, and DREAM, achieving comparable or better performance than the same models that take as input the full reference document. To the best of our knowledge, this is the first work extracting evidence sentences for multiple-choice MRC.
Vector of Locally-Aggregated Word Embeddings (VLAWE): A Novel Document-level Representation
Authors: Radu Tudor Ionescu, Andrei M. Butnaru
Date: 2019-02-23 00:00:00
Proceeding: NAACL 2019 6
Tasks: Multi-Label Text Classification, Sentiment Analysis, Subjectivity Analysis, text-classification, Text Classification, Word Embeddings

In this paper, we propose a novel representation for text documents based on aggregating word embedding vectors into document embeddings. Our approach is inspired by the Vector of Locally-Aggregated Descriptors used for image representation, and it works as follows. First, the word embeddings gathered from a collection of documents are clustered by k-means in order to learn a codebook of semnatically-related word embeddings. Each word embedding is then associated to its nearest cluster centroid (codeword). The Vector of Locally-Aggregated Word Embeddings (VLAWE) representation of a document is then computed by accumulating the differences between each codeword vector and each word vector (from the document) associated to the respective codeword. We plug the VLAWE representation, which is learned in an unsupervised manner, into a classifier and show that it is useful for a diverse set of text classification tasks. We compare our approach with a broad range of recent state-of-the-art methods, demonstrating the effectiveness of our approach. Furthermore, we obtain a considerable improvement on the Movie Review data set, reporting an accuracy of 93.3%, which represents an absolute gain of 10% over the state-of-the-art approach. Our code is available at https://github.com/raduionescu/vlawe-boswe/.
Transfer Learning for Non-Intrusive Load Monitoring
Authors: Michele DIncecco, Stefano Squartini, Mingjun Zhong
Date: 2019-02-23 00:00:00
Tasks: Non-Intrusive Load Monitoring, Transfer Learning

Non-intrusive load monitoring (NILM) is a technique to recover source appliances from only the recorded mains in a household. NILM is unidentifiable and thus a challenge problem because the inferred power value of an appliance given only the mains could not be unique. To mitigate the unidentifiable problem, various methods incorporating domain knowledge into NILM have been proposed and shown effective experimentally. Recently, among these methods, deep neural networks are shown performing best. Arguably, the recently proposed sequence-to-point (seq2point) learning is promising for NILM. However, the results were only carried out on the same data domain. It is not clear if the method could be generalised or transferred to different domains, e.g., the test data were drawn from a different country comparing to the training data. We address this issue in the paper, and two transfer learning schemes are proposed, i.e., appliance transfer learning (ATL) and cross-domain transfer learning (CTL). For ATL, our results show that the latent features learnt by a `complex' appliance, e.g., washing machine, can be transferred to a `simple' appliance, e.g., kettle. For CTL, our conclusion is that the seq2point learning is transferable. Precisely, when the training and test data are in a similar domain, seq2point learning can be directly applied to the test data without fine tuning; when the training and test data are in different domains, seq2point learning needs fine tuning before applying to the test data. Interestingly, we show that only the fully connected layers need fine tuning for transfer learning. Source code can be found at https://github.com/MingjunZhong/transferNILM.
Re-evaluating ADEM: A Deeper Look at Scoring Dialogue Responses
Authors: Ananya B. Sai, Mithun Das Gupta, Mitesh M. Khapra, Mukundhan Srinivasan
Date: 2019-02-23 00:00:00
Tasks: Dialogue Evaluation, Response Generation

Automatically evaluating the quality of dialogue responses for unstructured
domains is a challenging problem. ADEM(Lowe et al. 2017) formulated the
automatic evaluation of dialogue systems as a learning problem and showed that
such a model was able to predict responses which correlate significantly with
human judgements, both at utterance and system level. Their system was shown to
have beaten word-overlap metrics such as BLEU with large margins. We start with
the question of whether an adversary can game the ADEM model. We design a
battery of targeted attacks at the neural network based ADEM evaluation system
and show that automatic evaluation of dialogue systems still has a long way to
go. ADEM can get confused with a variation as simple as reversing the word
order in the text! We report experiments on several such adversarial scenarios
that draw out counterintuitive scores on the dialogue responses. We take a
systematic look at the scoring function proposed by ADEM and connect it to
linear system theory to predict the shortcomings evident in the system. We also
devise an attack that can fool such a system to rate a response generation
system as favorable. Finally, we allude to future research directions of using
the adversarial attacks to design a truly automated dialogue evaluation system.
Categorization in the Wild: Generalizing Cognitive Models to Naturalistic Data across Languages
Authors: Lea Frermann, Mirella Lapata
Date: 2019-02-23 00:00:00
Tasks: World Knowledge

Categories such as animal or furniture are acquired at an early age and play
an important role in processing, organizing, and communicating world knowledge.
Categories exist across cultures: they allow to efficiently represent the
complexity of the world, and members of a community strongly agree on their
nature, revealing a shared mental representation. Models of category learning
and representation, however, are typically tested on data from small-scale
experiments involving small sets of concepts with artificially restricted
features; and experiments predominantly involve participants of selected
cultural and socio-economical groups (very often involving western native
speakers of English such as U.S. college students) . This work investigates
whether models of categorization generalize (a) to rich and noisy data
approximating the environment humans live in; and (b) across languages and
cultures. We present a Bayesian cognitive model designed to jointly learn
categories and their structured representation from natural language text which
allows us to (a) evaluate performance on a large scale, and (b) apply our model
to a diverse set of languages. We show that meaningful categories comprising
hundreds of concepts and richly structured featural representations emerge
across languages. Our work illustrates the potential of recent advances in
computational modeling and large scale naturalistic datasets for cognitive
science research.
Augmenting Neural Machine Translation with Knowledge Graphs
Authors: Diego Moussallem, Mihael Arčan, Axel-Cyrille Ngonga Ngomo, Paul Buitelaar
Date: 2019-02-23 00:00:00
Tasks: Knowledge Graphs, Machine Translation, NMT, Translation

While neural networks have been used extensively to make substantial progress
in the machine translation task, they are known for being heavily dependent on
the availability of large amounts of training data. Recent efforts have tried
to alleviate the data sparsity problem by augmenting the training data using
different strategies, such as back-translation. Along with the data scarcity,
the out-of-vocabulary words, mostly entities and terminological expressions,
pose a difficult challenge to Neural Machine Translation systems. In this
paper, we hypothesize that knowledge graphs enhance the semantic feature
extraction of neural models, thus optimizing the translation of entities and
terminological expressions in texts and consequently leading to a better
translation quality. We hence investigate two different strategies for
incorporating knowledge graphs into neural models without modifying the neural
network architectures. We also examine the effectiveness of our augmentation
method to recurrent and non-recurrent (self-attentional) neural architectures.
Our knowledge graph augmented neural translation model, dubbed KG-NMT, achieves
significant and consistent improvements of +3 BLEU, METEOR and chrF3 on average
on the newstest datasets between 2014 and 2018 for WMT English-German
translation task.
A Degeneracy Framework for Scalable Graph Autoencoders
Authors: Guillaume Salha, Romain Hennequin, Viet Anh Tran, Michalis Vazirgiannis
Date: 2019-02-23 00:00:00

In this paper, we present a general framework to scale graph autoencoders (AE) and graph variational autoencoders (VAE). This framework leverages graph degeneracy concepts to train models only from a dense subset of nodes instead of using the entire graph. Together with a simple yet effective propagation mechanism, our approach significantly improves scalability and training speed while preserving performance. We evaluate and discuss our method on several variants of existing graph AE and VAE, providing the first application of these models to large graphs with up to millions of nodes and edges. We achieve empirically competitive results w.r.t. several popular scalable node embedding methods, which emphasizes the relevance of pursuing further research towards more scalable graph AE and VAE.
Deep Learning Approach on Information Diffusion in Heterogeneous Networks
Authors: Soheila Molaei, Hadi Zare, Hadi Veisi
Date: 2019-02-23 00:00:00
Tasks: Deep Learning, Representation Learning, World Knowledge

There are many real-world knowledge based networked systems with multi-type interacting entities that can be regarded as heterogeneous networks including human connections and biological evolutions. One of the main issues in such networks is to predict information diffusion such as shape, growth and size of social events and evolutions in the future. While there exist a variety of works on this topic mainly using a threshold-based approach, they suffer from the local viewpoint on the network and sensitivity to the threshold parameters. In this paper, information diffusion is considered through a latent representation learning of the heterogeneous networks to encode in a deep learning model. To this end, we propose a novel meta-path representation learning approach, Heterogeneous Deep Diffusion(HDD), to exploit meta-paths as main entities in networks. At first, the functional heterogeneous structures of the network are learned by a continuous latent representation through traversing meta-paths with the aim of global end-to-end viewpoint. Then, the well-known deep learning architectures are employed on our generated features to predict diffusion processes in the network. The proposed approach enables us to apply it on different information diffusion tasks such as topic diffusion and cascade prediction. We demonstrate the proposed approach on benchmark network datasets through the well-known evaluation measures. The experimental results show that our approach outperforms the earlier state-of-the-art methods.
Illumination-invariant Face recognition by fusing thermal and visual images via gradient transfer
Authors: Sumit Agarwal, Harshit S. Sikchi, Suparna Rooj, Shubhobrata Bhattacharya, Aurobinda Routray
Date: 2019-02-23 00:00:00
Tasks: Face Detection, Face Recognition

Face recognition in real life situations like low illumination condition is
still an open challenge in biometric security. It is well established that the
state-of-the-art methods in face recognition provide low accuracy in the case
of poor illumination. In this work, we propose an algorithm for a more robust
illumination invariant face recognition using a multi-modal approach. We
propose a new dataset consisting of aligned faces of thermal and visual images
of a hundred subjects. We then apply face detection on thermal images using the
biggest blob extraction method and apply them for fusing images of different
modalities for the purpose of face recognition. An algorithm is proposed to
implement fusion of thermal and visual images. We reason for why relying on
only one modality can give erroneous results. We use a lighter and faster CNN
model called MobileNet for the purpose of face recognition with faster
inferencing and to be able to be use it in real time biometric systems. We test
our proposed method on our own created dataset to show that real-time face
recognition on fused images shows far better results than using visual or
thermal images separately.
VCWE: Visual Character-Enhanced Word Embeddings
Authors: Chi Sun, Xipeng Qiu, Xuanjing Huang
Date: 2019-02-23 00:00:00
Proceeding: NAACL 2019 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Part-Of-Speech Tagging, Sentiment Analysis, Word Embeddings, Word Similarity

Chinese is a logographic writing system, and the shape of Chinese characters
contain rich syntactic and semantic information. In this paper, we propose a
model to learn Chinese word embeddings via three-level composition: (1) a
convolutional neural network to extract the intra-character compositionality
from the visual shape of a character; (2) a recurrent neural network with
self-attention to compose character representation into word embeddings; (3)
the Skip-Gram framework to capture non-compositionality directly from the
contextual information. Evaluations demonstrate the superior performance of our
model on four tasks: word similarity, sentiment analysis, named entity
recognition and part-of-speech tagging.
A visual encoding model based on deep neural networks and transfer learning
Authors: Chi Zhang, Kai Qiao, Linyuan Wang, Li Tong, Guoen Hu, Ruyuan Zhang, Bin Yan
Date: 2019-02-23 00:00:00
Tasks: Prediction, Transfer Learning

Background: Building visual encoding models to accurately predict visual
responses is a central challenge for current vision-based brain-machine
interface techniques. To achieve high prediction accuracy on neural signals,
visual encoding models should include precise visual features and appropriate
prediction algorithms. Most existing visual encoding models employ hand-craft
visual features (e.g., Gabor wavelets or semantic labels) or data-driven
features (e.g., features extracted from deep neural networks (DNN)). They also
assume a linear mapping between feature representation to brain activity.
However, it remains unknown whether such linear mapping is sufficient for
maximizing prediction accuracy. New Method: We construct a new visual encoding
framework to predict cortical responses in a benchmark functional magnetic
resonance imaging (fMRI) dataset. In this framework, we employ the transfer
learning technique to incorporate a pre-trained DNN (i.e., AlexNet) and train a
nonlinear mapping from visual features to brain activity. This nonlinear
mapping replaces the conventional linear mapping and is supposed to improve
prediction accuracy on brain activity. Results: The proposed framework can
significantly predict responses of over 20% voxels in early visual areas (i.e.,
V1-lateral occipital region, LO) and achieve unprecedented prediction accuracy.
Comparison with Existing Methods: Comparing to two conventional visual encoding
models, we find that the proposed encoding model shows consistent higher
prediction accuracy in all early visual areas, especially in relatively
anterior visual areas (i.e., V4 and LO). Conclusions: Our work proposes a new
framework to utilize pre-trained visual features and train non-linear mappings
from visual features to brain activity.
Identifying Malicious Web Domains Using Machine Learning Techniques with Online Credibility and Performance Data
Authors: Zhongyi Hu, Raymond Chiong, Ilung Pranata, Willy Susilo, Yukun Bao
Date: 2019-02-23 00:00:00
Tasks: BIG-bench Machine Learning, feature selection

Malicious web domains represent a big threat to web users' privacy and
security. With so much freely available data on the Internet about web domains'
popularity and performance, this study investigated the performance of
well-known machine learning techniques used in conjunction with this type of
online data to identify malicious web domains. Two datasets consisting of
malware and phishing domains were collected to build and evaluate the machine
learning classifiers. Five single classifiers and four ensemble classifiers
were applied to distinguish malicious domains from benign ones. In addition, a
binary particle swarm optimisation (BPSO) based feature selection method was
used to improve the performance of single classifiers. Experimental results
show that, based on the web domains' popularity and performance data features,
the examined machine learning techniques can accurately identify malicious
domains in different ways. Furthermore, the BPSO-based feature selection
procedure is shown to be an effective way to improve the performance of
classifiers.
Experimental Study on CTL model checking using Machine Learning
Authors: Weijun Zhu
Date: 2019-02-23 00:00:00
Tasks: BIG-bench Machine Learning

The existing core methods, which are employed by the popular CTL model
checking tools, are facing the famous state explode problem. In our previous
study, a method based on the Machine Learning (ML) algorithms was proposed to
address this problem. However, the accuracy is not satisfactory. First, we
conduct a comprehensive experiment on Graph Lab to seek the optimal accuracy
using the five machine learning algorithms. Second, given the optimal accuracy,
the average time is seeked. The results show that the Logistic Regressive
(LR)-based approach can simulate CTL model checking with the accuracy of 98.8%,
and its average efficiency is 459 times higher than that of the existing
method, as well as the Boosted Tree (BT)-based approach can simulate CTL model
checking with the accuracy of 98.7%, and its average efficiency is 639 times
higher than that of the existing method.
Facial Motion Prior Networks for Facial Expression Recognition
Authors: Yuedong Chen, Jian-Feng Wang, Shikai Chen, Zhongchao shi, Jianfei Cai
Date: 2019-02-23 00:00:00
Tasks: Deep Learning, Facial Expression Recognition, Facial Expression Recognition (FER)

Deep learning based facial expression recognition (FER) has received a lot of attention in the past few years. Most of the existing deep learning based FER methods do not consider domain knowledge well, which thereby fail to extract representative features. In this work, we propose a novel FER framework, named Facial Motion Prior Networks (FMPN). Particularly, we introduce an addition branch to generate a facial mask so as to focus on facial muscle moving regions. To guide the facial mask learning, we propose to incorporate prior domain knowledge by using the average differences between neutral faces and the corresponding expressive faces as the training guidance. Extensive experiments on three facial expression benchmark datasets demonstrate the effectiveness of the proposed method, compared with the state-of-the-art approaches.