pwc-archive/papers-with-abstracts huggingface dataset:
Image-based Survival Analysis for Lung Cancer Patients using CNNs
Authors: Christoph Haarburger, Philippe Weitz, Oliver Rippel, Dorit Merhof
Date: 2018-08-29 00:00:00
Tasks: Survival Analysis, Survival Prediction

Traditional survival models such as the Cox proportional hazards model are
typically based on scalar or categorical clinical features. With the advent of
increasingly large image datasets, it has become feasible to incorporate
quantitative image features into survival prediction. So far, this kind of
analysis is mostly based on radiomics features, i.e. a fixed set of features
that is mathematically defined a priori. To capture highly abstract
information, it is desirable to learn the feature extraction using
convolutional neural networks. However, for tomographic medical images, model
training is difficult because on the one hand, only few samples of 3D image
data fit into one batch at once and on the other hand, survival loss functions
are essentially ordering measures that require large batch sizes. In this work,
we show that by simplifying survival analysis to median survival
classification, convolutional neural networks can be trained with small batch
sizes and learn features that predict survival equally well as end-to-end
hazard prediction networks. Our approach outperforms the previous state of the
art in a publicly available lung cancer dataset.
Proximal boosting: aggregating weak learners to minimize non-differentiable losses
Authors: Erwan Fouillen, Claire Boyer, Maxime Sangnier
Date: 2018-08-29 00:00:00

Gradient boosting is a prediction method that iteratively combines weak learners to produce a complex and accurate model. From an optimization point of view, the learning procedure of gradient boosting mimics a gradient descent on a functional variable. This paper proposes to build upon the proximal point algorithm, when the empirical risk to minimize is not differentiable, in order to introduce a novel boosting approach, called proximal boosting. It comes with a companion algorithm inspired by [1] and called residual proximal boosting, which is aimed at better controlling the approximation error. Theoretical convergence is proved for these two procedures under different hypotheses on the empirical risk and advantages of leveraging proximal methods for boosting are illustrated by numerical experiments on simulated and real-world data. In particular, we exhibit a favorable comparison over gradient boosting regarding convergence rate and prediction accuracy.
Context Mover's Distance & Barycenters: Optimal Transport of Contexts for Building Representations
Authors: Sidak Pal Singh, Andreas Hug, Aymeric Dieuleveut, Martin Jaggi
Date: 2018-08-29 00:00:00
Tasks: Representation Learning, Sentence, Sentence Embedding, Sentence Similarity

We present a framework for building unsupervised representations of entities and their compositions, where each entity is viewed as a probability distribution rather than a vector embedding. In particular, this distribution is supported over the contexts which co-occur with the entity and are embedded in a suitable low-dimensional space. This enables us to consider representation learning from the perspective of Optimal Transport and take advantage of its tools such as Wasserstein distance and barycenters. We elaborate how the method can be applied for obtaining unsupervised representations of text and illustrate the performance (quantitatively as well as qualitatively) on tasks such as measuring sentence similarity, word entailment and similarity, where we empirically observe significant gains (e.g., 4.1% relative improvement over Sent2vec, GenSen). The key benefits of the proposed approach include: (a) capturing uncertainty and polysemy via modeling the entities as distributions, (b) utilizing the underlying geometry of the particular task (with the ground cost), (c) simultaneously providing interpretability with the notion of optimal transport between contexts and (d) easy applicability on top of existing point embedding methods. The code, as well as prebuilt histograms, are available under https://github.com/context-mover/.
APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning
Authors: Yang Gao, Christian M. Meyer, Iryna Gurevych
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Active Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We propose a method to perform automatic document summarisation without using
reference summaries. Instead, our method interactively learns from users'
preferences. The merit of preference-based interactive summarisation is that
preferences are easier for users to provide than reference summaries. Existing
preference-based interactive learning methods suffer from high sample
complexity, i.e. they need to interact with the oracle for many rounds in order
to converge. In this work, we propose a new objective function, which enables
us to leverage active learning, preference learning and reinforcement learning
techniques in order to reduce the sample complexity. Both simulation and
real-user experiments suggest that our method significantly advances the state
of the art. Our source code is freely available at
https://github.com/UKPLab/emnlp2018-april.
Neural Metaphor Detection in Context
Authors: Ge Gao, Eunsol Choi, Yejin Choi, Luke Zettlemoyer
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10

We present end-to-end neural models for detecting metaphorical word use in
context. We show that relatively standard BiLSTM models which operate on
complete sentences work well in this setting, in comparison to previous work
that used more restricted forms of linguistic context. These models establish a
new state-of-the-art on existing verb metaphor detection benchmarks, and show
strong performance on jointly predicting the metaphoricity of all words in a
running text.
Adapting Visual Question Answering Models for Enhancing Multimodal Community Q&A Platforms
Authors: Avikalp Srivastava, Hsin Wen Liu, Sumio Fujita
Date: 2018-08-29 00:00:00
Tasks: Community Question Answering, General Classification, Question Answering, Representation Learning, Retrieval, valid, Visual Question Answering, Visual Question Answering (VQA)

Question categorization and expert retrieval methods have been crucial for information organization and accessibility in community question & answering (CQA) platforms. Research in this area, however, has dealt with only the text modality. With the increasing multimodal nature of web content, we focus on extending these methods for CQA questions accompanied by images. Specifically, we leverage the success of representation learning for text and images in the visual question answering (VQA) domain, and adapt the underlying concept and architecture for automated category classification and expert retrieval on image-based questions posted on Yahoo! Chiebukuro, the Japanese counterpart of Yahoo! Answers. To the best of our knowledge, this is the first work to tackle the multimodality challenge in CQA, and to adapt VQA models for tasks on a more ecologically valid source of visual questions. Our analysis of the differences between visual QA and community QA data drives our proposal of novel augmentations of an attention method tailored for CQA, and use of auxiliary tasks for learning better grounding features. Our final model markedly outperforms the text-only and VQA model baselines for both tasks of classification and expert retrieval on real-world multimodal CQA data.
Diffusion Approximations for Online Principal Component Estimation and Global Convergence
Authors: Chris Junchi Li, Mengdi Wang, Han Liu, Tong Zhang
Date: 2018-08-29 00:00:00
Proceeding: NeurIPS 2017 12

In this paper, we propose to adopt the diffusion approximation tools to study
the dynamics of Oja's iteration which is an online stochastic gradient descent
method for the principal component analysis. Oja's iteration maintains a
running estimate of the true principal component from streaming data and enjoys
less temporal and spatial complexities. We show that the Oja's iteration for
the top eigenvector generates a continuous-state discrete-time Markov chain
over the unit sphere. We characterize the Oja's iteration in three phases using
diffusion approximation and weak convergence tools. Our three-phase analysis
further provides a finite-sample error bound for the running estimate, which
matches the minimax information lower bound for principal component analysis
under the additional assumption of bounded samples.
On Tree-Based Neural Sentence Modeling
Authors: Haoyue Shi, Hao Zhou, Jiaze Chen, Lei LI
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Sentence, Sentiment Analysis, Text Classification

Neural networks with tree-based sentence encoders have shown better results
on many downstream tasks. Most of existing tree-based encoders adopt syntactic
parsing trees as the explicit structure prior. To study the effectiveness of
different tree structures, we replace the parsing trees with trivial trees
(i.e., binary balanced tree, left-branching tree and right-branching tree) in
the encoders. Though trivial trees contain no syntactic information, those
encoders get competitive or even better results on all of the ten downstream
tasks we investigated. This surprising result indicates that explicit syntax
guidance may not be the main contributor to the superior performances of
tree-based neural sentence modeling. Further analysis show that tree modeling
gives better results when crucial words are closer to the final representation.
Additional experiments give more clues on how to design an effective tree-based
encoder. Our code is open-source and available at
https://github.com/ExplorerFreda/TreeEnc.
Online ICA: Understanding Global Dynamics of Nonconvex Optimization via Diffusion Processes
Authors: Chris Junchi Li, Zhaoran Wang, Han Liu
Date: 2018-08-29 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Tensor Decomposition

Solving statistical learning problems often involves nonconvex optimization.
Despite the empirical success of nonconvex statistical optimization methods,
their global dynamics, especially convergence to the desirable local minima,
remain less well understood in theory. In this paper, we propose a new analytic
paradigm based on diffusion processes to characterize the global dynamics of
nonconvex statistical optimization. As a concrete example, we study stochastic
gradient descent (SGD) for the tensor decomposition formulation of independent
component analysis. In particular, we cast different phases of SGD into
diffusion processes, i.e., solutions to stochastic differential equations.
Initialized from an unstable equilibrium, the global dynamics of SGD transit
over three consecutive phases: (i) an unstable Ornstein-Uhlenbeck process
slowly departing from the initialization, (ii) the solution to an ordinary
differential equation, which quickly evolves towards the desirable local
minimum, and (iii) a stable Ornstein-Uhlenbeck process oscillating around the
desirable local minimum. Our proof techniques are based upon Stroock and
Varadhan's weak convergence of Markov chains to diffusion processes, which are
of independent interest.
Replay spoofing detection system for automatic speaker verification using multi-task learning of noise classes
Authors: Hye-jin Shim, Jee-weon Jung, Hee-Soo Heo, Sung-Hyun Yoon, Ha-Jin Yu
Date: 2018-08-29 00:00:00
Tasks: General Classification, Multi-Task Learning, Speaker Verification

In this paper, we propose a replay attack spoofing detection system for
automatic speaker verification using multitask learning of noise classes. We
define the noise that is caused by the replay attack as replay noise. We
explore the effectiveness of training a deep neural network simultaneously for
replay attack spoofing detection and replay noise classification. The
multi-task learning includes classifying the noise of playback devices,
recording environments, and recording devices as well as the spoofing
detection. Each of the three types of the noise classes also includes a genuine
class. The experiment results on the ASVspoof2017 datasets demonstrate that the
performance of our proposed system is improved by 30% relatively on the
evaluation set.
Decoupling Strategy and Generation in Negotiation Dialogues
Authors: He He, Derek Chen, Anusha Balakrishnan, Percy Liang
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Retrieval

We consider negotiation settings in which two agents use natural language to
bargain on goods. Agents need to decide on both high-level strategy (e.g.,
proposing \$50) and the execution of that strategy (e.g., generating "The bike
is brand new. Selling for just \$50."). Recent work on negotiation trains
neural models, but their end-to-end nature makes it hard to control their
strategy, and reinforcement learning tends to lead to degenerate solutions. In
this paper, we propose a modular approach based on coarse di- alogue acts
(e.g., propose(price=50)) that decouples strategy and generation. We show that
we can flexibly set the strategy using supervised learning, reinforcement
learning, or domain-specific knowledge without degeneracy, while our
retrieval-based generation can maintain context-awareness and produce diverse
utterances. We test our approach on the recently proposed DEALORNODEAL game,
and we also collect a richer dataset based on real items on Craigslist. Human
evaluation shows that our systems achieve higher task success rate and more
human-like negotiation behavior than previous approaches.
Voice Conversion Based on Cross-Domain Features Using Variational Auto Encoders
Authors: Wen-Chin Huang, Hsin-Te Hwang, Yu-Huai Peng, Yu Tsao, Hsin-Min Wang
Date: 2018-08-29 00:00:00
Tasks: Voice Conversion

An effective approach to non-parallel voice conversion (VC) is to utilize
deep neural networks (DNNs), specifically variational auto encoders (VAEs), to
model the latent structure of speech in an unsupervised manner. A previous
study has confirmed the ef- fectiveness of VAE using the STRAIGHT spectra for
VC. How- ever, VAE using other types of spectral features such as mel- cepstral
coefficients (MCCs), which are related to human per- ception and have been
widely used in VC, have not been prop- erly investigated. Instead of using one
specific type of spectral feature, it is expected that VAE may benefit from
using multi- ple types of spectral features simultaneously, thereby improving
the capability of VAE for VC. To this end, we propose a novel VAE framework
(called cross-domain VAE, CDVAE) for VC. Specifically, the proposed framework
utilizes both STRAIGHT spectra and MCCs by explicitly regularizing multiple
objectives in order to constrain the behavior of the learned encoder and de-
coder. Experimental results demonstrate that the proposed CD- VAE framework
outperforms the conventional VAE framework in terms of subjective tests.
Improved Semantic-Aware Network Embedding with Fine-Grained Word Alignment
Authors: Dinghan Shen, Xinyuan Zhang, Ricardo Henao, Lawrence Carin
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Link Prediction, Network Embedding, Word Alignment

Network embeddings, which learn low-dimensional representations for each
vertex in a large-scale network, have received considerable attention in recent
years. For a wide range of applications, vertices in a network are typically
accompanied by rich textual information such as user profiles, paper abstracts,
etc. We propose to incorporate semantic features into network embeddings by
matching important words between text sequences for all pairs of vertices. We
introduce a word-by-word alignment framework that measures the compatibility of
embeddings between word pairs, and then adaptively accumulates these alignment
features with a simple yet effective aggregation function. In experiments, we
evaluate the proposed framework on three real-world benchmarks for downstream
tasks, including link prediction and multi-label vertex classification. Results
demonstrate that our model outperforms state-of-the-art network embedding
methods by a large margin.
Elastic bands across the path: A new framework and methods to lower bound DTW
Authors: Chang Wei Tan, Francois Petitjean, Geoffrey I. Webb
Date: 2018-08-29 00:00:00
Tasks: Clustering, Dynamic Time Warping, General Classification, Time Series, Time Series Analysis, Time Series Classification

There has been renewed recent interest in developing effective lower bounds
for Dynamic Time Warping (DTW) distance between time series. These have many
applications in time series indexing, clustering, forecasting, regression and
classification. One of the key time series classification algorithms, the
nearest neighbor algorithm with DTW distance (NN-DTW) is very expensive to
compute, due to the quadratic complexity of DTW. Lower bound search can speed
up NN-DTW substantially. An effective and tight lower bound quickly prunes off
unpromising nearest neighbor candidates from the search space and minimises the
number of the costly DTW computations. The speed up provided by lower bound
search becomes increasingly critical as training set size increases. Different
lower bounds provide different trade-offs between computation time and
tightness. Most existing lower bounds interact with DTW warping window sizes.
They are very tight and effective at smaller warping window sizes, but become
looser as the warping window increases, thus reducing the pruning effectiveness
for NN-DTW. In this work, we present a new class of lower bounds that are
tighter than the popular Keogh lower bound, while requiring similar computation
time. Our new lower bounds take advantage of the DTW boundary condition,
monotonicity and continuity constraints to create a tighter lower bound. Of
particular significance, they remain relatively tight even for large windows. A
single parameter to these new lower bounds controls the speed-tightness
trade-off. We demonstrate that these new lower bounds provide an exceptional
balance between computation time and tightness for the NN-DTW time series
classification task, resulting in greatly improved efficiency for NN-DTW lower
bound search.
Nonlinear regression based on a hybrid quantum computer
Authors: Dan-Bo Zhang, Shi-Liang Zhu, Z. D. Wang
Date: 2018-08-29 00:00:00
Tasks: BIG-bench Machine Learning, Quantum Machine Learning, regression

Incorporating nonlinearity into quantum machine learning is essential for
learning a complicated input-output mapping. We here propose quantum algorithms
for nonlinear regression, where nonlinearity is introduced with feature maps
when loading classical data into quantum states. Our implementation is based on
a hybrid quantum computer, exploiting both discrete and continuous variables,
for their capacity to encode novel features and efficiency of processing
information. We propose encoding schemes that can realize well-known polynomial
and Gaussian kernel ridge regressions, with exponentially speed-up regarding to
the number of samples.
Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction
Authors: Yi Luan, Luheng He, Mari Ostendorf, Hannaneh Hajishirzi
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Articles, Coreference Resolution, Joint Entity and Relation Extraction, Named Entity Recognition (NER), Sentence

We introduce a multi-task setup of identifying and classifying entities,
relations, and coreference clusters in scientific articles. We create SciERC, a
dataset that includes annotations for all three tasks and develop a unified
framework called Scientific Information Extractor (SciIE) for with shared span
representations. The multi-task setup reduces cascading errors between tasks
and leverages cross-sentence relations through coreference links. Experiments
show that our multi-task model outperforms previous models in scientific
information extraction without using any domain-specific features. We further
show that the framework supports construction of a scientific knowledge graph,
which we use to analyze information in scientific literature.
Mapping Language to Code in Programmatic Context
Authors: Srinivasan Iyer, Ioannis Konstas, Alvin Cheung, Luke Zettlemoyer
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder

Source code is rarely written in isolation. It depends significantly on the
programmatic context, such as the class that the code would reside in. To study
this phenomenon, we introduce the task of generating class member functions
given English documentation and the programmatic context provided by the rest
of the class. This task is challenging because the desired code can vary
greatly depending on the functionality the class provides (e.g., a sort
function may or may not be available when we are asked to "return the smallest
element" in a particular member variable list). We introduce CONCODE, a new
large dataset with over 100,000 examples consisting of Java classes from online
code repositories, and develop a new encoder-decoder architecture that models
the interaction between the method documentation and the class environment. We
also present a detailed error analysis suggesting that there is significant
room for future work on this task.
Complexity and mission computability of adaptive computing systems
Authors: Venkat R. Dasari, Mee Seong Im, Billy Geerhart
Date: 2018-08-29 00:00:00

There is a subset of computational problems that are computable in polynomial
time for which an existing algorithm may not complete due to a lack of high
performance technology on a mission field. We define a subclass of
deterministic polynomial time complexity class called mission class, as many
polynomial problems are not computable in mission time. By focusing on such
subclass of languages in the context for successful military applications, we
also discuss their computational and communicational constraints. We
investigate feasible (non)linear models that will minimize energy and maximize
memory, efficiency, and computational power, and also provide an approximate
solution obtained within a pre-determined length of computation time using
limited resources so that an optimal solution to a language could be
determined.
Breaking the Beam Search Curse: A Study of (Re-)Scoring Methods and Stopping Criteria for Neural Machine Translation
Authors: Yilin Yang, Liang Huang, Mingbo Ma
Date: 2018-08-28 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Machine Translation, Translation

Beam search is widely used in neural machine translation, and usually
improves translation quality compared to greedy search. It has been widely
observed that, however, beam sizes larger than 5 hurt translation quality. We
explain why this happens, and propose several methods to address this problem.
Furthermore, we discuss the optimal stopping criteria for these methods.
Results show that our hyperparameter-free methods outperform the widely-used
hyperparameter-free heuristic of length normalization by +2.0 BLEU, and achieve
the best results among all methods on Chinese-to-English translation.