pwc-archive/papers-with-abstracts huggingface dataset:
Learning to Ask Questions in Open-domain Conversational Systems with Typed Decoders
Authors: Yansen Wang, Chen-Yi Liu, Minlie Huang, Liqiang Nie
Date: 2018-05-13 00:00:00
Proceeding: ACL 2018 7
Tasks: Decoder, Question Generation, Question-Generation, Sentence

Asking good questions in large-scale, open-domain conversational systems is
quite significant yet rather untouched. This task, substantially different from
traditional question generation, requires to question not only with various
patterns but also on diverse and relevant topics. We observe that a good
question is a natural composition of {\it interrogatives}, {\it topic words},
and {\it ordinary words}. Interrogatives lexicalize the pattern of questioning,
topic words address the key information for topic transition in dialogue, and
ordinary words play syntactical and grammatical roles in making a natural
sentence. We devise two typed decoders (\textit{soft typed decoder} and
\textit{hard typed decoder}) in which a type distribution over the three types
is estimated and used to modulate the final generation distribution. Extensive
experiments show that the typed decoders outperform state-of-the-art baselines
and can generate more meaningful questions.
Building Language Models for Text with Named Entities
Authors: Md. Rizwan Parvez, Saikat Chakraborty, Baishakhi Ray, Kai-Wei Chang
Date: 2018-05-13 00:00:00
Proceeding: ACL 2018 7
Tasks: Code Generation, Language Modeling, Language Modelling, Recipe Generation

Text in many domains involves a significant amount of named entities.
Predict- ing the entity names is often challenging for a language model as they
appear less frequent on the training corpus. In this paper, we propose a novel
and effective approach to building a discriminative language model which can
learn the entity names by leveraging their entity type information. We also
introduce two benchmark datasets based on recipes and Java programming codes,
on which we evalu- ate the proposed model. Experimental re- sults show that our
model achieves 52.2% better perplexity in recipe generation and 22.06% on code
generation than the state-of-the-art language models.
Hierarchical Neural Story Generation
Authors: Angela Fan, Mike Lewis, Yann Dauphin
Date: 2018-05-13 00:00:00
Proceeding: ACL 2018 7
Tasks: Story Generation

We explore story generation: creative systems that can build coherent and
fluent passages of text about a topic. We collect a large dataset of 300K
human-written stories paired with writing prompts from an online forum. Our
dataset enables hierarchical story generation, where the model first generates
a premise, and then transforms it into a passage of text. We gain further
improvements with a novel form of model fusion that improves the relevance of
the story to the prompt, and adding a new gated multi-scale self-attention
mechanism to model long-range context. Experiments show large improvements over
strong baselines on both automated and human evaluations. Human judges prefer
stories generated by our approach to those from a strong non-hierarchical model
by a factor of two to one.
Spatial Uncertainty Sampling for End-to-End Control
Authors: Alexander Amini, Ava Soleimany, Sertac Karaman, Daniela Rus
Date: 2018-05-13 00:00:00
Tasks: Autonomous Vehicles, Bayesian Inference

End-to-end trained neural networks (NNs) are a compelling approach to autonomous vehicle control because of their ability to learn complex tasks without manual engineering of rule-based decisions. However, challenging road conditions, ambiguous navigation situations, and safety considerations require reliable uncertainty estimation for the eventual adoption of full-scale autonomous vehicles. Bayesian deep learning approaches provide a way to estimate uncertainty by approximating the posterior distribution of weights given a set of training data. Dropout training in deep NNs approximates Bayesian inference in a deep Gaussian process and can thus be used to estimate model uncertainty. In this paper, we propose a Bayesian NN for end-to-end control that estimates uncertainty by exploiting feature map correlation during training. This approach achieves improved model fits, as well as tighter uncertainty estimates, than traditional element-wise dropout. We evaluate our algorithms on a challenging dataset collected over many different road types, times of day, and weather conditions, and demonstrate how uncertainties can be used in conjunction with a human controller in a parallel autonomous setting.
Enhanced Signal Recovery via Sparsity Inducing Image Priors
Authors: Hojjat Seyed Mousavi
Date: 2018-05-13 00:00:00
Tasks: Clustering, General Classification, Super-Resolution

Parsimony in signal representation is a topic of active research. Sparse
signal processing and representation is the outcome of this line of research
which has many applications in information processing and has shown significant
improvement in real-world applications such as recovery, classification,
clustering, super resolution, etc. This vast influence of sparse signal
processing in real-world problems raises a significant need in developing novel
sparse signal representation algorithms to obtain more robust systems. In such
algorithms, a few open challenges remain in (a) efficiently posing sparsity on
signals that can capture the structure of underlying signal and (b) the design
of tractable algorithms that can recover signals under aforementioned sparse
models.
Explainable Recommendation: A Survey and New Perspectives
Authors: Yongfeng Zhang, Xu Chen
Date: 2018-04-30 00:00:00
Tasks: Explainable Recommendation, Persuasiveness, Product Recommendation, Recommendation Systems, Survey

Explainable recommendation attempts to develop models that generate not only high-quality recommendations but also intuitive explanations. The explanations may either be post-hoc or directly come from an explainable model (also called interpretable or transparent model in some contexts). Explainable recommendation tries to address the problem of why: by providing explanations to users or system designers, it helps humans to understand why certain items are recommended by the algorithm, where the human can either be users or system designers. Explainable recommendation helps to improve the transparency, persuasiveness, effectiveness, trustworthiness, and satisfaction of recommendation systems. It also facilitates system designers for better system debugging. In recent years, a large number of explainable recommendation approaches -- especially model-based methods -- have been proposed and applied in real-world systems. In this survey, we provide a comprehensive review for the explainable recommendation research. We first highlight the position of explainable recommendation in recommender system research by categorizing recommendation problems into the 5W, i.e., what, when, who, where, and why. We then conduct a comprehensive survey of explainable recommendation on three perspectives: 1) We provide a chronological research timeline of explainable recommendation. 2) We provide a two-dimensional taxonomy to classify existing explainable recommendation research. 3) We summarize how explainable recommendation applies to different recommendation tasks. We also devote a chapter to discuss the explanation perspectives in broader IR and AI/ML research. We end the survey by discussing potential future directions to promote the explainable recommendation research area and beyond.
Triangular Architecture for Rare Language Translation
Authors: Shuo Ren, Wenhu Chen, Shujie Liu, Mu Li, Ming Zhou, Shuai Ma
Date: 2018-05-13 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, NMT, Translation

Neural Machine Translation (NMT) performs poor on the low-resource language
pair $(X,Z)$, especially when $Z$ is a rare language. By introducing another
rich language $Y$, we propose a novel triangular training architecture (TA-NMT)
to leverage bilingual data $(Y,Z)$ (may be small) and $(X,Y)$ (can be rich) to
improve the translation performance of low-resource pairs. In this triangular
architecture, $Z$ is taken as the intermediate latent variable, and translation
models of $Z$ are jointly optimized with a unified bidirectional EM algorithm
under the goal of maximizing the translation likelihood of $(X,Y)$. Empirical
results demonstrate that our method significantly improves the translation
quality of rare languages on MultiUN and IWSLT2012 datasets, and achieves even
better performance combining back-translation methods.
AttriGuard: A Practical Defense Against Attribute Inference Attacks via Adversarial Machine Learning
Authors: Jinyuan Jia, Neil Zhenqiang Gong
Date: 2018-05-13 00:00:00
Tasks: Attribute, BIG-bench Machine Learning

Users in various web and mobile applications are vulnerable to attribute inference attacks, in which an attacker leverages a machine learning classifier to infer a target user's private attributes (e.g., location, sexual orientation, political view) from its public data (e.g., rating scores, page likes). Existing defenses leverage game theory or heuristics based on correlations between the public data and attributes. These defenses are not practical. Specifically, game-theoretic defenses require solving intractable optimization problems, while correlation-based defenses incur large utility loss of users' public data. In this paper, we present AttriGuard, a practical defense against attribute inference attacks. AttriGuard is computationally tractable and has small utility loss. Our AttriGuard works in two phases. Suppose we aim to protect a user's private attribute. In Phase I, for each value of the attribute, we find a minimum noise such that if we add the noise to the user's public data, then the attacker's classifier is very likely to infer the attribute value for the user. We find the minimum noise via adapting existing evasion attacks in adversarial machine learning. In Phase II, we sample one attribute value according to a certain probability distribution and add the corresponding noise found in Phase I to the user's public data. We formulate finding the probability distribution as solving a constrained convex optimization problem. We extensively evaluate AttriGuard and compare it with existing methods using a real-world dataset. Our results show that AttriGuard substantially outperforms existing methods. Our work is the first one that shows evasion attacks can be used as defensive techniques for privacy protection.
BET on Independence
Authors: Kai Zhang
Date: 2016-10-17 00:00:00

We study the problem of nonparametric dependence detection. Many existing
methods may suffer severe power loss due to non-uniform consistency, which we
illustrate with a paradox. To avoid such power loss, we approach the
nonparametric test of independence through the new framework of binary
expansion statistics (BEStat) and binary expansion testing (BET), which examine
dependence through a novel binary expansion filtration approximation of the
copula. Through a Hadamard transform, we find that the symmetry statistics in
the filtration are complete sufficient statistics for dependence. These
statistics are also uncorrelated under the null. By utilizing symmetry
statistics, the BET avoids the problem of non-uniform consistency and improves
upon a wide class of commonly used methods (a) by achieving the minimax rate in
sample size requirement for reliable power and (b) by providing clear
interpretations of global relationships upon rejection of independence. The
binary expansion approach also connects the symmetry statistics with the
current computing system to facilitate efficient bitwise implementation. We
illustrate the BET with a study of the distribution of stars in the night sky
and with an exploratory data analysis of the TCGA breast cancer data.
Curriculum Adversarial Training
Authors: Qi-Zhi Cai, Min Du, Chang Liu, Dawn Song
Date: 2018-05-13 00:00:00

Recently, deep learning has been applied to many security-sensitive
applications, such as facial authentication. The existence of adversarial
examples hinders such applications. The state-of-the-art result on defense
shows that adversarial training can be applied to train a robust model on MNIST
against adversarial examples; but it fails to achieve a high empirical
worst-case accuracy on a more complex task, such as CIFAR-10 and SVHN. In our
work, we propose curriculum adversarial training (CAT) to resolve this issue.
The basic idea is to develop a curriculum of adversarial examples generated by
attacks with a wide range of strengths. With two techniques to mitigate the
forgetting and the generalization issues, we demonstrate that CAT can improve
the prior art's empirical worst-case accuracy by a large margin of 25% on
CIFAR-10 and 35% on SVHN. At the same, the model's performance on
non-adversarial inputs is comparable to the state-of-the-art models.
Zero-Shot Dialog Generation with Cross-Domain Latent Actions
Authors: Tiancheng Zhao, Maxine Eskenazi
Date: 2018-05-13 00:00:00
Proceeding: WS 2018 7
Tasks: Dialogue Generation, Goal-Oriented Dialog, Text Generation

This paper introduces zero-shot dialog generation (ZSDG), as a step towards
neural dialog systems that can instantly generalize to new situations with
minimal data. ZSDG enables an end-to-end generative dialog system to generalize
to a new domain for which only a domain description is provided and no training
dialogs are available. Then a novel learning framework, Action Matching, is
proposed. This algorithm can learn a cross-domain embedding space that models
the semantics of dialog responses which, in turn, lets a neural dialog
generation model generalize to new domains. We evaluate our methods on a new
synthetic dialog dataset, and an existing human-human dialog dataset. Results
show that our method has superior performance in learning dialog models that
rapidly adapt their behavior to new domains and suggests promising future
research.
Automatic Extraction of Commonsense LocatedNear Knowledge
Authors: Frank F. Xu, Bill Yuchen Lin, Kenny Q. Zhu
Date: 2017-11-11 00:00:00
Proceeding: ACL 2018 7
Tasks: Relation, Sentence

LocatedNear relation is a kind of commonsense knowledge describing two
physical objects that are typically found near each other in real life. In this
paper, we study how to automatically extract such relationship through a
sentence-level relation classifier and aggregating the scores of entity pairs
from a large corpus. Also, we release two benchmark datasets for evaluation and
future research.
Coarse-to-Fine Decoding for Neural Semantic Parsing
Authors: Li Dong, Mirella Lapata
Date: 2018-05-12 00:00:00
Proceeding: ACL 2018 7
Tasks: Semantic Parsing

Semantic parsing aims at mapping natural language utterances into structured
meaning representations. In this work, we propose a structure-aware neural
architecture which decomposes the semantic parsing process into two stages.
Given an input utterance, we first generate a rough sketch of its meaning,
where low-level information (such as variable names and arguments) is glossed
over. Then, we fill in missing details by taking into account the natural
language input and the sketch itself. Experimental results on four datasets
characteristic of different domains and meaning representations show that our
approach consistently improves performance, achieving competitive results
despite the use of relatively simple decoders.
Scene-Aware Audio for 360\textdegree{} Videos
Authors: Dingzeyu Li, Timothy R. Langlois, Changxi Zheng
Date: 2018-05-12 00:00:00

Although 360\textdegree{} cameras ease the capture of panoramic footage, it
remains challenging to add realistic 360\textdegree{} audio that blends into
the captured scene and is synchronized with the camera motion. We present a
method for adding scene-aware spatial audio to 360\textdegree{} videos in
typical indoor scenes, using only a conventional mono-channel microphone and a
speaker. We observe that the late reverberation of a room's impulse response is
usually diffuse spatially and directionally. Exploiting this fact, we propose a
method that synthesizes the directional impulse response between any source and
listening locations by combining a synthesized early reverberation part and a
measured late reverberation tail. The early reverberation is simulated using a
geometric acoustic simulation and then enhanced using a frequency modulation
method to capture room resonances. The late reverberation is extracted from a
recorded impulse response, with a carefully chosen time duration that separates
out the late reverberation from the early reverberation. In our validations, we
show that our synthesized spatial audio matches closely with recordings using
ambisonic microphones. Lastly, we demonstrate the strength of our method in
several applications.
Jointly Predicting Predicates and Arguments in Neural Semantic Role Labeling
Authors: Luheng He, Kenton Lee, Omer Levy, Luke Zettlemoyer
Date: 2018-05-12 00:00:00
Proceeding: ACL 2018 7
Tasks: Semantic Role Labeling

Recent BIO-tagging-based neural semantic role labeling models are very high
performing, but assume gold predicates as part of the input and cannot
incorporate span-level features. We propose an end-to-end approach for jointly
predicting all predicates, arguments spans, and the relations between them. The
model makes independent decisions about what relationship, if any, holds
between every possible word-span pair, and learns contextualized span
representations that provide rich, shared input features for each decision.
Experiments demonstrate that this approach sets a new state of the art on
PropBank SRL without gold predicates.
An Optimal Policy for Dynamic Assortment Planning Under Uncapacitated Multinomial Logit Models
Authors: Xi Chen, Yining Wang, Yuan Zhou
Date: 2018-05-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Management

We study the dynamic assortment planning problem, where for each arriving
customer, the seller offers an assortment of substitutable products and
customer makes the purchase among offered products according to an
uncapacitated multinomial logit (MNL) model. Since all the utility parameters
of MNL are unknown, the seller needs to simultaneously learn customers' choice
behavior and make dynamic decisions on assortments based on the current
knowledge. The goal of the seller is to maximize the expected revenue, or
equivalently, to minimize the expected regret. Although dynamic assortment
planning problem has received an increasing attention in revenue management,
most existing policies require the estimation of mean utility for each product
and the final regret usually involves the number of products $N$. The optimal
regret of the dynamic assortment planning problem under the most basic and
popular choice model---MNL model is still open. By carefully analyzing a
revenue potential function, we develop a trisection based policy combined with
adaptive confidence bound construction, which achieves an {item-independent}
regret bound of $O(\sqrt{T})$, where $T$ is the length of selling horizon. We
further establish the matching lower bound result to show the optimality of our
policy. There are two major advantages of the proposed policy. First, the
regret of all our policies has no dependence on $N$. Second, our policies are
almost assumption free: there is no assumption on mean utility nor any
"separability" condition on the expected revenues for different assortments.
Our result also extends the unimodal bandit literature.
Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces
Authors: Junhong Lin, Alessandro Rudi, Lorenzo Rosasco, Volkan Cevher
Date: 2018-01-20 00:00:00
Tasks: regression

In this paper, we study regression problems over a separable Hilbert space with the square loss, covering non-parametric regression over a reproducing kernel Hilbert space. We investigate a class of spectral/regularized algorithms, including ridge regression, principal component regression, and gradient methods. We prove optimal, high-probability convergence results in terms of variants of norms for the studied algorithms, considering a capacity assumption on the hypothesis space and a general source condition on the target function. Consequently, we obtain almost sure convergence results with optimal rates. Our results improve and generalize previous results, filling a theoretical gap for the non-attainable cases.
Sparse Approximation of 3D Meshes using the Spectral Geometry of the Hamiltonian Operator
Authors: Yoni Choukroun, Gautam Pai, Ron Kimmel
Date: 2017-07-07 00:00:00

The discrete Laplace operator is ubiquitous in spectral shape analysis, since
its eigenfunctions are provably optimal in representing smooth functions
defined on the surface of the shape. Indeed, subspaces defined by its
eigenfunctions have been utilized for shape compression, treating the
coordinates as smooth functions defined on the given surface. However, surfaces
of shapes in nature often contain geometric structures for which the general
smoothness assumption may fail to hold. At the other end, some explicit mesh
compression algorithms utilize the order by which vertices that represent the
surface are traversed, a property which has been ignored in spectral
approaches. Here, we incorporate the order of vertices into an operator that
defines a novel spectral domain. We propose a method for representing 3D meshes
using the spectral geometry of the Hamiltonian operator, integrated within a
sparse approximation framework. We adapt the concept of a potential function
from quantum physics and incorporate vertex ordering information into the
potential, yielding a novel data-dependent operator. The potential function
modifies the spectral geometry of the Laplacian to focus on regions with finer
details of the given surface. By sparsely encoding the geometry of the shape
using the proposed data-dependent basis, we improve compression performance
compared to previous results that use the standard Laplacian basis and spectral
graph wavelets.
Learning to Find Eye Region Landmarks for Remote Gaze Estimation in Unconstrained Settings
Authors: Seonwook Park, Xucong Zhang, Andreas Bulling, Otmar Hilliges
Date: 2018-05-12 00:00:00
Tasks: Gaze Estimation

Conventional feature-based and model-based gaze estimation methods have
proven to perform well in settings with controlled illumination and specialized
cameras. In unconstrained real-world settings, however, such methods are
surpassed by recent appearance-based methods due to difficulties in modeling
factors such as illumination changes and other visual artifacts. We present a
novel learning-based method for eye region landmark localization that enables
conventional methods to be competitive to latest appearance-based methods.
Despite having been trained exclusively on synthetic data, our method exceeds
the state of the art for iris localization and eye shape registration on
real-world imagery. We then use the detected landmarks as input to iterative
model-fitting and lightweight learning-based gaze estimation methods. Our
approach outperforms existing model-fitting and appearance-based methods in the
context of person-independent and personalized gaze estimation.