pwc-archive/papers-with-abstracts huggingface dataset:
A Pragmatic Guide to Geoparsing Evaluation
Authors: Milan Gritta, Mohammad Taher Pilehvar, Nigel Collier
Date: 2018-10-29 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Toponym Resolution

Empirical methods in geoparsing have thus far lacked a standard evaluation framework describing the task, metrics and data used to compare state-of-the-art systems. Evaluation is further made inconsistent, even unrepresentative of real-world usage by the lack of distinction between the different types of toponyms, which necessitates new guidelines, a consolidation of metrics and a detailed toponym taxonomy with implications for Named Entity Recognition (NER) and beyond. To address these deficiencies, our manuscript introduces a new framework in three parts. Part 1) Task Definition: clarified via corpus linguistic analysis proposing a fine-grained Pragmatic Taxonomy of Toponyms. Part 2) Metrics: discussed and reviewed for a rigorous evaluation including recommendations for NER/Geoparsing practitioners. Part 3) Evaluation Data: shared via a new dataset called GeoWebNews to provide test/train examples and enable immediate use of our contributions. In addition to fine-grained Geotagging and Toponym Resolution (Geocoding), this dataset is also suitable for prototyping and evaluating machine learning NLP models.
Do Explanations make VQA Models more Predictable to a Human?
Authors: Arjun Chandrasekaran, Viraj Prabhu, Deshraj Yadav, Prithvijit Chattopadhyay, Devi Parikh
Date: 2018-10-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Question Answering, Visual Question Answering, Visual Question Answering (VQA)

A rich line of research attempts to make deep neural networks more
transparent by generating human-interpretable 'explanations' of their decision
process, especially for interactive tasks like Visual Question Answering (VQA).
In this work, we analyze if existing explanations indeed make a VQA model --
its responses as well as failures -- more predictable to a human. Surprisingly,
we find that they do not. On the other hand, we find that human-in-the-loop
approaches that treat the model as a black-box do.
Global Non-convex Optimization with Discretized Diffusions
Authors: Murat A. Erdogdu, Lester Mackey, Ohad Shamir
Date: 2018-10-29 00:00:00
Proceeding: NeurIPS 2018 12

An Euler discretization of the Langevin diffusion is known to converge to the global minimizers of certain convex and non-convex optimization problems. We show that this property holds for any suitably smooth diffusion and that different diffusions are suitable for optimizing different classes of convex and non-convex functions. This allows us to design diffusions suitable for globally optimizing convex and non-convex functions not covered by the existing Langevin theory. Our non-asymptotic analysis delivers computable optimization and integration error bounds based on easily accessed properties of the objective and chosen diffusion. Central to our approach are new explicit Stein factor bounds on the solutions of Poisson equations. We complement these results with improved optimization guarantees for targets other than the standard Gibbs measure.
Multi-label Multi-task Deep Learning for Behavioral Coding
Authors: James Gibson, David C. Atkins, Torrey Creed, Zac Imel, Panayiotis Georgiou, Shrikanth Narayanan
Date: 2018-10-29 00:00:00
Tasks: Deep Learning, Multi-Task Learning

We propose a methodology for estimating human behaviors in psychotherapy
sessions using mutli-label and multi-task learning paradigms. We discuss the
problem of behavioral coding in which data of human interactions is the
annotated with labels to describe relevant human behaviors of interest. We
describe two related, yet distinct, corpora consisting of therapist client
interactions in psychotherapy sessions. We experimentally compare the proposed
learning approaches for estimating behaviors of interest in these datasets.
Specifically, we compare single and multiple label learning approaches, single
and multiple task learning approaches, and evaluate the performance of these
approaches when incorporating turn context. We demonstrate the prediction
performance gains which can be achieved by using the proposed paradigms and
discuss the insights these models provide into these complex interactions.
Gather-Excite: Exploiting Feature Context in Convolutional Neural Networks
Authors: Jie Hu, Li Shen, Samuel Albanie, Gang Sun, Andrea Vedaldi
Date: 2018-10-29 00:00:00
Proceeding: NeurIPS 2018 12

While the use of bottom-up local operators in convolutional neural networks
(CNNs) matches well some of the statistics of natural images, it may also
prevent such models from capturing contextual long-range feature interactions.
In this work, we propose a simple, lightweight approach for better context
exploitation in CNNs. We do so by introducing a pair of operators: gather,
which efficiently aggregates feature responses from a large spatial extent, and
excite, which redistributes the pooled information to local features. The
operators are cheap, both in terms of number of added parameters and
computational complexity, and can be integrated directly in existing
architectures to improve their performance. Experiments on several datasets
show that gather-excite can bring benefits comparable to increasing the depth
of a CNN at a fraction of the cost. For example, we find ResNet-50 with
gather-excite operators is able to outperform its 101-layer counterpart on
ImageNet with no additional learnable parameters. We also propose a parametric
gather-excite operator pair which yields further performance gains, relate it
to the recently-introduced Squeeze-and-Excitation Networks, and analyse the
effects of these changes to the CNN feature activation statistics.
Content Selection in Deep Learning Models of Summarization
Authors: Chris Kedzie, Kathleen McKeown, Hal Daume III
Date: 2018-10-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Articles, Deep Learning, Sentence

We carry out experiments with deep learning models of summarization across
the domains of news, personal stories, meetings, and medical articles in order
to understand how content selection is performed. We find that many
sophisticated features of state of the art extractive summarizers do not
improve performance over simpler models. These results suggest that it is
easier to create a summarizer for a new domain than previous work suggests and
bring into question the benefit of deep learning models for summarization for
those domains that do have massive datasets (i.e., news). At the same time,
they suggest important questions for new research in summarization; namely, new
forms of sentence representations or external knowledge sources are needed that
are better suited to the summarization task.
A Comparative Measurement Study of Deep Learning as a Service Framework
Authors: Yanzhao Wu, Ling Liu, Calton Pu, Wenqi Cao, Semih Sahin, Wenqi Wei, Qi Zhang
Date: 2018-10-29 00:00:00
Tasks: CPU, GPU

Big data powered Deep Learning (DL) and its applications have blossomed in recent years, fueled by three technological trends: a large amount of digitized data openly accessible, a growing number of DL software frameworks in open source and commercial markets, and a selection of affordable parallel computing hardware devices. However, no single DL framework, to date, dominates in terms of performance and accuracy even for baseline classification tasks on standard datasets, making the selection of a DL framework an overwhelming task. This paper takes a holistic approach to conduct empirical comparison and analysis of four representative DL frameworks with three unique contributions. First, given a selection of CPU-GPU configurations, we show that for a specific DL framework, different configurations of its hyper-parameters may have a significant impact on both performance and accuracy of DL applications. Second, to the best of our knowledge, this study is the first to identify the opportunities for improving the training time performance and the accuracy of DL frameworks by configuring parallel computing libraries and tuning individual and multiple hyper-parameters. Third, we also conduct a comparative measurement study on the resource consumption patterns of four DL frameworks and their performance and accuracy implications, including CPU and memory usage, and their correlations to varying settings of hyper-parameters under different configuration combinations of hardware, parallel computing libraries. We argue that this measurement study provides in-depth empirical comparison and analysis of four representative DL frameworks, and offers practical guidance for service providers to deploying and delivering DL as a Service (DLaaS) and for application developers and DLaaS consumers to select the right DL frameworks for the right DL workloads.
Audiovisual speaker conversion: jointly and simultaneously transforming facial expression and acoustic characteristics
Authors: Fuming Fang, Xin Wang, Junichi Yamagishi, Isao Echizen
Date: 2018-10-29 00:00:00
Tasks: Image Reconstruction

An audiovisual speaker conversion method is presented for simultaneously
transforming the facial expressions and voice of a source speaker into those of
a target speaker. Transforming the facial and acoustic features together makes
it possible for the converted voice and facial expressions to be highly
correlated and for the generated target speaker to appear and sound natural. It
uses three neural networks: a conversion network that fuses and transforms the
facial and acoustic features, a waveform generation network that produces the
waveform from both the converted facial and acoustic features, and an image
reconstruction network that outputs an RGB facial image also based on both the
converted features. The results of experiments using an emotional audiovisual
database showed that the proposed method achieved significantly higher
naturalness compared with one that separately transformed acoustic and facial
features.
From Gene Expression to Drug Response: A Collaborative Filtering Approach
Authors: Cheng Qian, Nicholas D. Sidiropoulos, Magda Amiridi, Amin Emad
Date: 2018-10-29 00:00:00
Tasks: Collaborative Filtering, Sensitivity

Predicting the response of cancer cells to drugs is an important problem in
pharmacogenomics. Recent efforts in generation of large scale datasets
profiling gene expression and drug sensitivity in cell lines have provided a
unique opportunity to study this problem. However, one major challenge is the
small number of samples (cell lines) compared to the number of features (genes)
even in these large datasets. We propose a collaborative filtering (CF) like
algorithm for modeling gene-drug relationship to identify patients most likely
to benefit from a treatment. Due to the correlation of gene expressions in
different cell lines, the gene expression matrix is approximately low-rank,
which suggests that drug responses could be estimated from a reduced dimension
latent space of the gene expression. Towards this end, we propose a joint
low-rank matrix factorization and latent linear regression approach.
Experiments with data from the Genomics of Drug Sensitivity in Cancer database
are included to show that the proposed method can predict drug-gene
associations better than the state-of-the-art methods.
Visual Re-ranking with Natural Language Understanding for Text Spotting
Authors: Ahmed Sabir, Francesc Moreno-Noguer, Lluís Padró
Date: 2018-10-29 00:00:00
Tasks: Language Modeling, Language Modelling, Natural Language Understanding, Re-Ranking, Scene Text Recognition, Text Spotting

Many scene text recognition approaches are based on purely visual information
and ignore the semantic relation between scene and text. In this paper, we
tackle this problem from natural language processing perspective to fill the
gap between language and vision. We propose a post-processing approach to
improve scene text recognition accuracy by using occurrence probabilities of
words (unigram language model), and the semantic correlation between scene and
text. For this, we initially rely on an off-the-shelf deep neural network,
already trained with a large amount of data, which provides a series of text
hypotheses per input image. These hypotheses are then re-ranked using word
frequencies and semantic relatedness with objects or scenes in the image. As a
result of this combination, the performance of the original network is boosted
with almost no additional cost. We validate our approach on ICDAR'17 dataset.
Explicit Feedbacks Meet with Implicit Feedbacks : A Combined Approach for Recommendation System
Authors: Supriyo Mandal, Abyayananda Maiti
Date: 2018-10-29 00:00:00
Tasks: Recommendation Systems

Recommender systems recommend items more accurately by analyzing users'
potential interest on different brands' items. In conjunction with users'
rating similarity, the presence of users' implicit feedbacks like clicking
items, viewing items specifications, watching videos etc. have been proved to
be helpful for learning users' embedding, that helps better rating prediction
of users. Most existing recommender systems focus on modeling of ratings and
implicit feedbacks ignoring users' explicit feedbacks. Explicit feedbacks can
be used to validate the reliability of the particular users and can be used to
learn about the users' characteristic. Users' characteristic mean what type of
reviewers they are. In this paper, we explore three different models for
recommendation with more accuracy focusing on users' explicit feedbacks and
implicit feedbacks. First one is RHC-PMF that predicts users' rating more
accurately based on user's three explicit feedbacks (rating, helpfulness score
and centrality) and second one is RV-PMF, where user's implicit feedback (view
relationship) is considered. Last one is RHCV-PMF, where both type of feedbacks
are considered. In this model users' explicit feedbacks' similarity indicate
the similarity of their reliability and characteristic and implicit feedback's
similarity indicates their preference similarity. Extensive experiments on real
world dataset, i.e. Amazon.com online review dataset shows that our models
perform better compare to base-line models in term of users' rating prediction.
RHCV-PMF model also performs better rating prediction compare to baseline
models for cold start users and cold start items.
Hypergraph based semi-supervised learning algorithms applied to speech recognition problem: a novel approach
Authors: Loc Hoang Tran, Trang Hoang, Bui Hoang Nam Huynh
Date: 2018-10-28 00:00:00
Tasks: Sensitivity, speech-recognition, Speech Recognition

Most network-based speech recognition methods are based on the assumption
that the labels of two adjacent speech samples in the network are likely to be
the same. However, assuming the pairwise relationship between speech samples is
not complete. The information a group of speech samples that show very similar
patterns and tend to have similar labels is missed. The natural way overcoming
the information loss of the above assumption is to represent the feature data
of speech samples as the hypergraph. Thus, in this paper, the three
un-normalized, random walk, and symmetric normalized hypergraph Laplacian based
semi-supervised learning methods applied to hypergraph constructed from the
feature data of speech samples in order to predict the labels of speech samples
are introduced. Experiment results show that the sensitivity performance
measures of these three hypergraph Laplacian based semi-supervised learning
methods are greater than the sensitivity performance measures of the Hidden
Markov Model method (the current state of the art method applied to speech
recognition problem) and graph based semi-supervised learning methods (i.e. the
current state of the art network-based method for classification problems)
applied to network created from the feature data of speech samples.
Convolutional neural networks with extra-classical receptive fields
Authors: Brian Hu, Stefan Mihalas
Date: 2018-10-27 00:00:00
Proceeding: NeurIPS Workshop Neuro_AI 2019 12

Convolutional neural networks (CNNs) have had great success in many
real-world applications and have also been used to model visual processing in
the brain. However, these networks are quite brittle - small changes in the
input image can dramatically change a network's output prediction. In contrast
to what is known from biology, these networks largely rely on feedforward
connections, ignoring the influence of recurrent connections. They also focus
on supervised rather than unsupervised learning. To address these issues, we
combine traditional supervised learning via backpropagation with a specialized
unsupervised learning rule to learn lateral connections between neurons within
a convolutional neural network. These connections have been shown to optimally
integrate information from the surround, generating extra-classical receptive
fields for the neurons in our new proposed model (CNNEx). Models with optimal
lateral connections are more robust to noise and achieve better performance on
noisy versions of the MNIST and CIFAR-10 datasets. Resistance to noise can be
further improved by combining our model with additional regularization
techniques such as dropout and weight decay. Although the image statistics of
MNIST and CIFAR-10 differ greatly, the same unsupervised learning rule
generalized to both datasets. Our results demonstrate the potential usefulness
of combining supervised and unsupervised learning techniques and suggest that
the integration of lateral connections into convolutional neural networks is an
important area of future research.
Scaling Speech Enhancement in Unseen Environments with Noise Embeddings
Authors: Gil Keren, Jing Han, Björn Schuller
Date: 2018-10-26 00:00:00
Tasks: Speech Enhancement, speech-recognition, Speech Recognition

We address the problem of speech enhancement generalisation to unseen
environments by performing two manipulations. First, we embed an additional
recording from the environment alone, and use this embedding to alter
activations in the main enhancement subnetwork. Second, we scale the number of
noise environments present at training time to 16,784 different environments.
Experiment results show that both manipulations reduce word error rates of a
pretrained speech recognition system and improve enhancement quality according
to a number of performance measures. Specifically, our best model reduces the
word error rate from 34.04% on noisy speech to 15.46% on the enhanced speech.
Enhanced audio samples can be found in
https://speechenhancement.page.link/samples.
Model parameter estimation using coherent structure coloring
Authors: Kristy L. Schlueter-Kuck, John O. Dabiri
Date: 2018-10-31 00:00:00
Tasks: model, parameter estimation

Lagrangian data assimilation is a complex problem in oceanic and atmospheric
modeling. Tracking drifters in large-scale geophysical flows can involve
uncertainty in drifter location, complex inertial effects, and other factors
which make comparing them to simulated Lagrangian trajectories from numerical
models extremely challenging. Temporal and spatial discretization, factors
necessary in modeling large scale flows, also contribute to separation between
real and simulated drifter trajectories. The chaotic advection inherent in
these turbulent flows tends to separate even closely spaced tracer particles,
making error metrics based solely on drifter displacements unsuitable for
estimating model parameters. We propose to instead use error in the coherent
structure coloring (CSC) field to assess model skill. The CSC field provides a
spatial representation of the underlying coherent patterns in the flow, and we
show that it is a more robust metric for assessing model accuracy. Through the
use of two test cases, one considering spatial uncertainty in particle
initialization, and one examining the influence of stochastic error along a
trajectory and temporal discretization, we show that error in the coherent
structure coloring field can be used to accurately determine single or multiple
simultaneously unknown model parameters, whereas a conventional error metric
based on error in drifter displacement fails. Because the CSC field enhances
the difference in error between correct and incorrect model parameters, error
minima in model parameter sweeps become more distinct. The effectiveness and
robustness of this method for single and multi-parameter estimation in
analytical flows suggests that Lagrangian data assimilation for real oceanic
and atmospheric models would benefit from a similar approach.
Improving Machine Reading Comprehension with General Reading Strategies
Authors: Kai Sun, Dian Yu, Dong Yu, Claire Cardie
Date: 2018-10-31 00:00:00
Proceeding: NAACL 2019 6
Tasks: ARC, Language Modeling, Language Modelling, Machine Reading Comprehension, Multiple-choice, Question Answering, Reading Comprehension

Reading strategies have been shown to improve comprehension levels,
especially for readers lacking adequate prior knowledge. Just as the process of
knowledge accumulation is time-consuming for human readers, it is
resource-demanding to impart rich general domain knowledge into a deep language
model via pre-training. Inspired by reading strategies identified in cognitive
science, and given limited computational resources -- just a pre-trained model
and a fixed number of training instances -- we propose three general strategies
aimed to improve non-extractive machine reading comprehension (MRC): (i) BACK
AND FORTH READING that considers both the original and reverse order of an
input sequence, (ii) HIGHLIGHTING, which adds a trainable embedding to the text
embedding of tokens that are relevant to the question and candidate answers,
and (iii) SELF-ASSESSMENT that generates practice questions and candidate
answers directly from the text in an unsupervised manner.
  By fine-tuning a pre-trained language model (Radford et al., 2018) with our
proposed strategies on the largest general domain multiple-choice MRC dataset
RACE, we obtain a 5.8% absolute increase in accuracy over the previous best
result achieved by the same pre-trained model fine-tuned on RACE without the
use of strategies. We further fine-tune the resulting model on a target MRC
task, leading to an absolute improvement of 6.2% in average accuracy over
previous state-of-the-art approaches on six representative non-extractive MRC
datasets from different domains (i.e., ARC, OpenBookQA, MCTest, SemEval-2018
Task 11, ROCStories, and MultiRC). These results demonstrate the effectiveness
of our proposed strategies and the versatility and general applicability of our
fine-tuned models that incorporate these strategies. Core code is available at
https://github.com/nlpdata/strategy/.
Targeted stochastic gradient Markov chain Monte Carlo for hidden Markov models with rare latent states
Authors: Rihui Ou, Deborshee Sen, Alexander L Young, David B Dunson
Date: 2018-10-31 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

Markov chain Monte Carlo (MCMC) algorithms for hidden Markov models often rely on the forward-backward sampler. This makes them computationally slow as the length of the time series increases, motivating the development of sub-sampling-based approaches. These approximate the full posterior by using small random subsequences of the data at each MCMC iteration within stochastic gradient MCMC. In the presence of imbalanced data resulting from rare latent states, subsequences often exclude rare latent state data, leading to inaccurate inference and prediction/detection of rare events. We propose a targeted sub-sampling (TASS) approach that over-samples observations corresponding to rare latent states when calculating the stochastic gradient of parameters associated with them. TASS uses an initial clustering of the data to construct subsequence weights that reduce the variance in gradient estimation. This leads to improved sampling efficiency, in particular in settings where the rare latent states correspond to extreme observations. We demonstrate substantial gains in predictive and inferential accuracy on real and synthetic examples.