pwc-archive/papers-with-abstracts huggingface dataset:
Generate, Filter, and Rank: Grammaticality Classification for Production-Ready NLG Systems
Authors: Ashwini Challa, Kartikeya Upasani, Anusha Balakrishnan, Rajen Subba
Date: 2019-04-05 00:00:00
Proceeding: NAACL 2019 6
Tasks: Classification, General Classification, Grammatical Error Correction, Text Generation

Neural approaches to Natural Language Generation (NLG) have been promising
for goal-oriented dialogue. One of the challenges of productionizing these
approaches, however, is the ability to control response quality, and ensure
that generated responses are acceptable. We propose the use of a generate,
filter, and rank framework, in which candidate responses are first filtered to
eliminate unacceptable responses, and then ranked to select the best response.
While acceptability includes grammatical correctness and semantic correctness,
we focus only on grammaticality classification in this paper, and show that
existing datasets for grammatical error correction don't correctly capture the
distribution of errors that data-driven generators are likely to make. We
release a grammatical classification and semantic correctness classification
dataset for the weather domain that consists of responses generated by 3
data-driven NLG systems. We then explore two supervised learning approaches
(CNNs and GBDTs) for classifying grammaticality. Our experiments show that
grammaticality classification is very sensitive to the distribution of errors
in the data, and that these distributions vary significantly with both the
source of the response as well as the domain. We show that it's possible to
achieve high precision with reasonable recall on our dataset.
AMASS: Archive of Motion Capture as Surface Shapes
Authors: Naureen Mahmood, Nima Ghorbani, Nikolaus F. Troje, Gerard Pons-Moll, Michael J. Black
Date: 2019-04-05 00:00:00
Proceeding: ICCV 2019 10

Large datasets are the cornerstone of recent advances in computer vision
using deep learning. In contrast, existing human motion capture (mocap)
datasets are small and the motions limited, hampering progress on learning
models of human motion. While there are many different datasets available, they
each use a different parameterization of the body, making it difficult to
integrate them into a single meta dataset. To address this, we introduce AMASS,
a large and varied database of human motion that unifies 15 different optical
marker-based mocap datasets by representing them within a common framework and
parameterization. We achieve this using a new method, MoSh++, that converts
mocap data into realistic 3D human meshes represented by a rigged body model;
here we use SMPL [doi:10.1145/2816795.2818013], which is widely used and
provides a standard skeletal representation as well as a fully rigged surface
mesh. The method works for arbitrary marker sets, while recovering soft-tissue
dynamics and realistic hand motion. We evaluate MoSh++ and tune its
hyperparameters using a new dataset of 4D body scans that are jointly recorded
with marker-based mocap. The consistent representation of AMASS makes it
readily useful for animation, visualization, and generating training data for
deep learning. Our dataset is significantly richer than previous human motion
collections, having more than 40 hours of motion data, spanning over 300
subjects, more than 11,000 motions, and will be publicly available to the
research community.
Synthesized Policies for Transfer and Adaptation across Tasks and Environments
Authors: Hexiang Hu, Liyu Chen, Boqing Gong, Fei Sha
Date: 2019-04-05 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Reinforcement Learning

The ability to transfer in reinforcement learning is key towards building an agent of general artificial intelligence. In this paper, we consider the problem of learning to simultaneously transfer across both environments (ENV) and tasks (TASK), probably more importantly, by learning from only sparse (ENV, TASK) pairs out of all the possible combinations. We propose a novel compositional neural network architecture which depicts a meta rule for composing policies from the environment and task embeddings. Notably, one of the main challenges is to learn the embeddings jointly with the meta rule. We further propose new training methods to disentangle the embeddings, making them both distinctive signatures of the environments and tasks and effective building blocks for composing the policies. Experiments on GridWorld and Thor, of which the agent takes as input an egocentric view, show that our approach gives rise to high success rates on all the (ENV, TASK) pairs after learning from only 40% of them.
Robust Subspace Recovery with Adversarial Outliers
Authors: Tyler Maunu, Gilad Lerman
Date: 2019-04-05 00:00:00

We study the problem of robust subspace recovery (RSR) in the presence of
adversarial outliers. That is, we seek a subspace that contains a large portion
of a dataset when some fraction of the data points are arbitrarily corrupted.
We first examine a theoretical estimator that is intractable to calculate and
use it to derive information-theoretic bounds of exact recovery. We then
propose two tractable estimators: a variant of RANSAC and a simple relaxation
of the theoretical estimator. The two estimators are fast to compute and
achieve state-of-the-art theoretical performance in a noiseless RSR setting
with adversarial outliers. The former estimator achieves better theoretical
guarantees in the noiseless case, while the latter estimator is robust to small
noise, and its guarantees significantly improve with non-adversarial models of
outliers. We give a complete comparison of guarantees for the adversarial RSR
problem, as well as a short discussion on the estimation of affine subspaces.
A Variational Auto-Encoder Model for Stochastic Point Processes
Authors: Nazanin Mehrasa, Akash Abdu Jyothi, Thibaut Durand, JiaWei He, Leonid Sigal, Greg Mori
Date: 2019-04-05 00:00:00
Proceeding: CVPR 2019 6
Tasks: Point Processes

We propose a novel probabilistic generative model for action sequences. The
model is termed the Action Point Process VAE (APP-VAE), a variational
auto-encoder that can capture the distribution over the times and categories of
action sequences. Modeling the variety of possible action sequences is a
challenge, which we show can be addressed via the APP-VAE's use of latent
representations and non-linear functions to parameterize distributions over
which event is likely to occur next in a sequence and at what time. We
empirically validate the efficacy of APP-VAE for modeling action sequences on
the MultiTHUMOS and Breakfast datasets.
Domain Authoring Assistant for Intelligent Virtual Agents
Authors: Sepehr Janghorbani, Ashutosh Modi, Jakob Buhmann, Mubbasir Kapadia
Date: 2019-04-05 00:00:00

Developing intelligent virtual characters has attracted a lot of attention in
the recent years. The process of creating such characters often involves a team
of creative authors who describe different aspects of the characters in natural
language, and planning experts that translate this description into a planning
domain. This can be quite challenging as the team of creative authors should
diligently define every aspect of the character especially if it contains
complex human-like behavior. Also a team of engineers has to manually translate
the natural language description of a character's personality into the planning
domain knowledge. This can be extremely time and resource demanding and can be
an obstacle to author's creativity. The goal of this paper is to introduce an
authoring assistant tool to automate the process of domain generation from
natural language description of virtual characters, thus bridging between the
creative authoring team and the planning domain experts. Moreover, the proposed
tool also identifies possible missing information in the domain description and
iteratively makes suggestions to the author.
Extracting Factual Min/Max Age Information from Clinical Trial Studies
Authors: Yufang Hou, Debasis Ganguly, Lea A. Deleris, Francesca Bonin
Date: 2019-04-05 00:00:00
Proceeding: WS 2019 6
Tasks: Articles, Passage Retrieval, Question Answering, Retrieval

Population age information is an essential characteristic of clinical trials.
In this paper, we focus on extracting minimum and maximum (min/max) age values
for the study samples from clinical research articles. Specifically, we
investigate the use of a neural network model for question answering to address
this information extraction task. The min/max age QA model is trained on the
massive structured clinical study records from ClinicalTrials.gov. For each
article, based on multiple min and max age values extracted from the QA model,
we predict both actual min/max age values for the study samples and filter out
non-factual age expressions. Our system improves the results over (i) a passage
retrieval based IE system and (ii) a CRF-based system by a large margin when
evaluated on an annotated dataset consisting of 50 research papers on smoking
cessation.
Is 'Unsupervised Learning' a Misconceived Term?
Authors: Stephen G. Odaibo
Date: 2019-04-05 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Dimensionality Reduction

Is all of machine learning supervised to some degree? The field of machine
learning has traditionally been categorized pedagogically into
$supervised~vs~unsupervised~learning$; where supervised learning has typically
referred to learning from labeled data, while unsupervised learning has
typically referred to learning from unlabeled data. In this paper, we assert
that all machine learning is in fact supervised to some degree, and that the
scope of supervision is necessarily commensurate to the scope of learning
potential. In particular, we argue that clustering algorithms such as k-means,
and dimensionality reduction algorithms such as principal component analysis,
variational autoencoders, and deep belief networks are each internally
supervised by the data itself to learn their respective representations of its
features. Furthermore, these algorithms are not capable of external inference
until their respective outputs (clusters, principal components, or
representation codes) have been identified and externally labeled in effect. As
such, they do not suffice as examples of unsupervised learning. We propose that
the categorization `supervised vs unsupervised learning' be dispensed with, and
instead, learning algorithms be categorized as either
$internally~or~externally~supervised$ (or both). We believe this change in
perspective will yield new fundamental insights into the structure and
character of data and of learning algorithms.
Cross-Lingual Transfer of Semantic Roles: From Raw Text to Semantic Roles
Authors: Maryam Aminian, Mohammad Sadegh Rasooli, Mona Diab
Date: 2019-04-05 00:00:00
Proceeding: WS 2019 5
Tasks: Cross-Lingual Transfer, Semantic Role Labeling

We describe a transfer method based on annotation projection to develop a
dependency-based semantic role labeling system for languages for which no
supervised linguistic information other than parallel data is available. Unlike
previous work that presumes the availability of supervised features such as
lemmas, part-of-speech tags, and dependency parse trees, we only make use of
word and character features. Our deep model considers using character-based
representations as well as unsupervised stem embeddings to alleviate the need
for supervised features. Our experiments outperform a state-of-the-art method
that uses supervised lexico-syntactic features on 6 out of 7 languages in the
Universal Proposition Bank.
Attention Distillation for Learning Video Representations
Authors: Miao Liu, Xin Chen, Yun Zhang, Yin Li, James M. Rehg
Date: 2019-04-05 00:00:00
Tasks: Action Recognition, Video Recognition

We address the challenging problem of learning motion representations using deep models for video recognition. To this end, we make use of attention modules that learn to highlight regions in the video and aggregate features for recognition. Specifically, we propose to leverage output attention maps as a vehicle to transfer the learned representation from a motion (flow) network to an RGB network. We systematically study the design of attention modules, and develop a novel method for attention distillation. Our method is evaluated on major action benchmarks, and consistently improves the performance of the baseline RGB network by a significant margin. Moreover, we demonstrate that our attention maps can leverage motion cues in learning to identify the location of actions in video frames. We believe our method provides a step towards learning motion-aware representations in deep models. Our project page is available at https://aptx4869lm.github.io/AttentionDistillation/
Spatial CUSUM for Signal Region Detection
Authors: Xin Zhang, Zhengyuan Zhu
Date: 2019-04-05 00:00:00
Tasks: Epidemiology

Detecting weak clustered signal in spatial data is important but challenging
in applications such as medical image and epidemiology. A more efficient
detection algorithm can provide more precise early warning, and effectively
reduce the decision risk and cost. To date, many methods have been developed to
detect signals with spatial structures. However, most of the existing methods
are either too conservative for weak signals or computationally too intensive.
In this paper, we consider a novel method named Spatial CUSUM (SCUSUM), which
employs the idea of the CUSUM procedure and false discovery rate controlling.
We develop theoretical properties of the method which indicates that
asymptotically SCUSUM can reach high classification accuracy. In the simulation
study, we demonstrate that SCUSUM is sensitive to weak spatial signals. This
new method is applied to a real fMRI dataset as illustration, and more
irregular weak spatial signals are detected in the images compared to some
existing methods, including the conventional FDR, FDR$_L$ and scan statistics.
An Analysis of Attention over Clinical Notes for Predictive Tasks
Authors: Sarthak Jain, Ramin Mohammadi, Byron C. Wallace
Date: 2019-04-05 00:00:00
Proceeding: WS 2019 6

The shift to electronic medical records (EMRs) has engendered research into
machine learning and natural language technologies to analyze patient records,
and to predict from these clinical outcomes of interest. Two observations
motivate our aims here. First, unstructured notes contained within EMR often
contain key information, and hence should be exploited by models. Second, while
strong predictive performance is important, interpretability of models is
perhaps equally so for applications in this domain. Together, these points
suggest that neural models for EMR may benefit from incorporation of attention
over notes, which one may hope will both yield performance gains and afford
transparency in predictions. In this work we perform experiments to explore
this question using two EMR corpora and four different predictive tasks, that:
(i) inclusion of attention mechanisms is critical for neural encoder modules
that operate over notes fields in order to yield competitive performance, but,
(ii) unfortunately, while these boost predictive performance, it is decidedly
less clear whether they provide meaningful support for predictions.
HOList: An Environment for Machine Learning of Higher-Order Theorem Proving
Authors: Kshitij Bansal, Sarah M. Loos, Markus N. Rabe, Christian Szegedy, Stewart Wilcox
Date: 2019-04-05 00:00:00
Tasks: Automated Theorem Proving, BIG-bench Machine Learning, Deep Learning, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We present an environment, benchmark, and deep learning driven automated theorem prover for higher-order logic. Higher-order interactive theorem provers enable the formalization of arbitrary mathematical theories and thereby present an interesting, open-ended challenge for deep learning. We provide an open-source framework based on the HOL Light theorem prover that can be used as a reinforcement learning environment. HOL Light comes with a broad coverage of basic mathematical theorems on calculus and the formal proof of the Kepler conjecture, from which we derive a challenging benchmark for automated reasoning. We also present a deep reinforcement learning driven automated theorem prover, DeepHOL, with strong initial results on this benchmark.
An Unsupervised Autoregressive Model for Speech Representation Learning
Authors: Yu-An Chung, Wei-Ning Hsu, Hao Tang, James Glass
Date: 2019-04-05 00:00:00
Tasks: General Classification, model, Representation Learning, Speaker Verification, Speech Representation Learning

This paper proposes a novel unsupervised autoregressive neural model for learning generic speech representations. In contrast to other speech representation learning methods that aim to remove noise or speaker variabilities, ours is designed to preserve information for a wide range of downstream tasks. In addition, the proposed model does not require any phonetic or word boundary labels, allowing the model to benefit from large quantities of unlabeled data. Speech representations learned by our model significantly improve performance on both phone classification and speaker verification over the surface features and other supervised and unsupervised approaches. Further analysis shows that different levels of speech information are captured by our model at different layers. In particular, the lower layers tend to be more discriminative for speakers, while the upper layers provide more phonetic content.
ShapeMask: Learning to Segment Novel Objects by Refining Shape Priors
Authors: Wei-cheng Kuo, Anelia Angelova, Jitendra Malik, Tsung-Yi Lin
Date: 2019-04-05 00:00:00
Proceeding: ICCV 2019 10
Tasks: Instance Segmentation, Object, Semantic Segmentation

Instance segmentation aims to detect and segment individual objects in a
scene. Most existing methods rely on precise mask annotations of every
category. However, it is difficult and costly to segment objects in novel
categories because a large number of mask annotations is required. We introduce
ShapeMask, which learns the intermediate concept of object shape to address the
problem of generalization in instance segmentation to novel categories.
ShapeMask starts with a bounding box detection and gradually refines it by
first estimating the shape of the detected object through a collection of shape
priors. Next, ShapeMask refines the coarse shape into an instance level mask by
learning instance embeddings. The shape priors provide a strong cue for
object-like prediction, and the instance embeddings model the instance specific
appearance information. ShapeMask significantly outperforms the
state-of-the-art by 6.4 and 3.8 AP when learning across categories, and obtains
competitive performance in the fully supervised setting. It is also robust to
inaccurate detections, decreased model capacity, and small training data.
Moreover, it runs efficiently with 150ms inference time and trains within 11
hours on TPUs. With a larger backbone model, ShapeMask increases the gap with
state-of-the-art to 9.4 and 6.2 AP across categories. Code will be released.
Distinguishing Clinical Sentiment: The Importance of Domain Adaptation in Psychiatric Patient Health Records
Authors: Eben Holderness, Philip Cawkwell, Kirsten Bolton, James Pustejovsky, Mei-Hua Hall
Date: 2019-04-05 00:00:00
Proceeding: WS 2019 6
Tasks: BIG-bench Machine Learning, Decision Making, Domain Adaptation, Sentence, Sentiment Analysis

Recently natural language processing (NLP) tools have been developed to
identify and extract salient risk indicators in electronic health records
(EHRs). Sentiment analysis, although widely used in non-medical areas for
improving decision making, has been studied minimally in the clinical setting.
In this study, we undertook, to our knowledge, the first domain adaptation of
sentiment analysis to psychiatric EHRs by defining psychiatric clinical
sentiment, performing an annotation project, and evaluating multiple
sentence-level sentiment machine learning (ML) models. Results indicate that
off-the-shelf sentiment analysis tools fail in identifying clinically positive
or negative polarity, and that the definition of clinical sentiment that we
provide is learnable with relatively small amounts of training data. This
project is an initial step towards further refining sentiment analysis methods
for clinical use. Our long-term objective is to incorporate the results of this
project as part of a machine learning model that predicts inpatient readmission
risk. We hope that this work will initiate a discussion concerning domain
adaptation of sentiment analysis to the clinical setting.
NELEC at SemEval-2019 Task 3: Think Twice Before Going Deep
Authors: Parag Agrawal, Anshuman Suri
Date: 2019-04-05 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Deep Learning, Emotion Recognition in Conversation, General Classification, Sentiment Classification

Existing Machine Learning techniques yield close to human performance on
text-based classification tasks. However, the presence of multi-modal noise in
chat data such as emoticons, slang, spelling mistakes, code-mixed data, etc.
makes existing deep-learning solutions perform poorly. The inability of
deep-learning systems to robustly capture these covariates puts a cap on their
performance. We propose NELEC: Neural and Lexical Combiner, a system which
elegantly combines textual and deep-learning based methods for sentiment
classification. We evaluate our system as part of the third task of 'Contextual
Emotion Detection in Text' as part of SemEval-2019. Our system performs
significantly better than the baseline, as well as our deep-learning model
benchmarks. It achieved a micro-averaged F1 score of 0.7765, ranking 3rd on the
test-set leader-board. Our code is available at
https://github.com/iamgroot42/nelec
The Fishyscapes Benchmark: Measuring Blind Spots in Semantic Segmentation
Authors: Hermann Blum, Paul-Edouard Sarlin, Juan Nieto, Roland Siegwart, Cesar Cadena
Date: 2019-04-05 00:00:00
Tasks: Anomaly Detection, Autonomous Driving, Segmentation, Semantic Segmentation

Deep learning has enabled impressive progress in the accuracy of semantic segmentation. Yet, the ability to estimate uncertainty and detect failure is key for safety-critical applications like autonomous driving. Existing uncertainty estimates have mostly been evaluated on simple tasks, and it is unclear whether these methods generalize to more complex scenarios. We present Fishyscapes, the first public benchmark for uncertainty estimation in a real-world task of semantic segmentation for urban driving. It evaluates pixel-wise uncertainty estimates towards the detection of anomalous objects in front of the vehicle. We~adapt state-of-the-art methods to recent semantic segmentation models and compare approaches based on softmax confidence, Bayesian learning, and embedding density. Our results show that anomaly detection is far from solved even for ordinary situations, while our benchmark allows measuring advancements beyond the state-of-the-art.