pwc-archive/papers-with-abstracts huggingface dataset:
Extracting Core Claims from Scientific Articles
Authors: Tom Jansen, Tobias Kuhn
Date: 2017-07-24 00:00:00
Tasks: Articles, Sentence

The number of scientific articles has grown rapidly over the years and there
are no signs that this growth will slow down in the near future. Because of
this, it becomes increasingly difficult to keep up with the latest developments
in a scientific field. To address this problem, we present here an approach to
help researchers learn about the latest developments and findings by extracting
in a normalized form core claims from scientific articles. This normalized
representation is a controlled natural language of English sentences called
AIDA, which has been proposed in previous work as a method to formally
structure and organize scientific findings and discourse. We show how such AIDA
sentences can be automatically extracted by detecting the core claim of an
article, checking for AIDA compliance, and - if necessary - transforming it
into a compliant sentence. While our algorithm is still far from perfect, our
results indicate that the different steps are feasible and they support the
claim that AIDA sentences might be a promising approach to improve scientific
communication in the future.
Image Pivoting for Learning Multilingual Multimodal Representations
Authors: Spandana Gella, Rico Sennrich, Frank Keller, Mirella Lapata
Date: 2017-07-24 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Image Description, Image Retrieval, Semantic Textual Similarity

In this paper we propose a model to learn multimodal multilingual
representations for matching images and sentences in different languages, with
the aim of advancing multilingual versions of image search and image
understanding. Our model learns a common representation for images and their
descriptions in two different languages (which need not be parallel) by
considering the image as a pivot between two languages. We introduce a new
pairwise ranking loss function which can handle both symmetric and asymmetric
similarity between the two modalities. We evaluate our models on
image-description ranking for German and English, and on semantic textual
similarity of image descriptions in English. In both cases we achieve
state-of-the-art performance.
Adversarial Sets for Regularising Neural Link Predictors
Authors: Pasquale Minervini, Thomas Demeester, Tim Rocktäschel, Sebastian Riedel
Date: 2017-07-24 00:00:00
Tasks: Link Prediction, Relational Reasoning

In adversarial training, a set of models learn together by pursuing competing
goals, usually defined on single data instances. However, in relational
learning and other non-i.i.d domains, goals can also be defined over sets of
instances. For example, a link predictor for the is-a relation needs to be
consistent with the transitivity property: if is-a(x_1, x_2) and is-a(x_2, x_3)
hold, is-a(x_1, x_3) needs to hold as well. Here we use such assumptions for
deriving an inconsistency loss, measuring the degree to which the model
violates the assumptions on an adversarially-generated set of examples. The
training objective is defined as a minimax problem, where an adversary finds
the most offending adversarial examples by maximising the inconsistency loss,
and the model is trained by jointly minimising a supervised loss and the
inconsistency loss on the adversarial examples. This yields the first method
that can use function-free Horn clauses (as in Datalog) to regularise any
neural link predictor, with complexity independent of the domain size. We show
that for several link prediction models, the optimisation problem faced by the
adversary has efficient closed-form solutions. Experiments on link prediction
benchmarks indicate that given suitable prior knowledge, our method can
significantly improve neural link predictors on all relevant metrics.
Transition-Based Generation from Abstract Meaning Representations
Authors: Timo Schick
Date: 2017-07-24 00:00:00
Tasks: Abstract Meaning Representation, Language Modeling, Language Modelling, Sentence

This work addresses the task of generating English sentences from Abstract
Meaning Representation (AMR) graphs. To cope with this task, we transform each
input AMR graph into a structure similar to a dependency tree and annotate it
with syntactic information by applying various predefined actions to it.
Subsequently, a sentence is obtained from this tree structure by visiting its
nodes in a specific order. We train maximum entropy models to estimate the
probability of each individual action and devise an algorithm that efficiently
approximates the best sequence of actions to be applied. Using a substandard
language model, our generator achieves a Bleu score of 27.4 on the LDC2014T12
test set, the best result reported so far without using silver standard
annotations from another corpus as additional training data.
Neural Machine Translation with Source-Side Latent Graph Parsing
Authors: Kazuma Hashimoto, Yoshimasa Tsuruoka
Date: 2017-02-08 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Machine Translation, NMT, Translation

This paper presents a novel neural machine translation model which jointly
learns translation and source-side latent graph representations of sentences.
Unlike existing pipelined approaches using syntactic parsers, our end-to-end
model learns a latent graph parser as part of the encoder of an attention-based
neural machine translation model, and thus the parser is optimized according to
the translation objective. In experiments, we first show that our model
compares favorably with state-of-the-art sequential and pipelined syntax-based
NMT models. We also show that the performance of our model can be further
improved by pre-training it with a small amount of treebank annotations. Our
final ensemble model significantly outperforms the previous best models on the
standard English-to-Japanese translation dataset.
Joint Background Reconstruction and Foreground Segmentation via A Two-stage Convolutional Neural Network
Authors: Xu Zhao, Yingying Chen, Ming Tang, Jinqiao Wang
Date: 2017-07-24 00:00:00
Tasks: Decoder, Foreground Segmentation, Segmentation

Foreground segmentation in video sequences is a classic topic in computer
vision. Due to the lack of semantic and prior knowledge, it is difficult for
existing methods to deal with sophisticated scenes well. Therefore, in this
paper, we propose an end-to-end two-stage deep convolutional neural network
(CNN) framework for foreground segmentation in video sequences. In the first
stage, a convolutional encoder-decoder sub-network is employed to reconstruct
the background images and encode rich prior knowledge of background scenes. In
the second stage, the reconstructed background and current frame are input into
a multi-channel fully-convolutional sub-network (MCFCN) for accurate foreground
segmentation. In the two-stage CNN, the reconstruction loss and segmentation
loss are jointly optimized. The background images and foreground objects are
output simultaneously in an end-to-end way. Moreover, by incorporating the
prior semantic knowledge of foreground and background in the pre-training
process, our method could restrain the background noise and keep the integrity
of foreground objects at the same time. Experiments on CDNet 2014 show that our
method outperforms the state-of-the-art by 4.9%.
A Joint Many-Task Model: Growing a Neural Network for Multiple NLP Tasks
Authors: Kazuma Hashimoto, Caiming Xiong, Yoshimasa Tsuruoka, Richard Socher
Date: 2016-11-05 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Chunking, Multi-Task Learning

Transfer and multi-task learning have traditionally focused on either a
single source-target pair or very few, similar tasks. Ideally, the linguistic
levels of morphology, syntax and semantics would benefit each other by being
trained in a single model. We introduce a joint many-task model together with a
strategy for successively growing its depth to solve increasingly complex
tasks. Higher layers include shortcut connections to lower-level task
predictions to reflect linguistic hierarchies. We use a simple regularization
term to allow for optimizing all model weights to improve one task's loss
without exhibiting catastrophic interference of the other tasks. Our single
end-to-end model obtains state-of-the-art or competitive results on five
different tasks from tagging, parsing, relatedness, and entailment tasks.
PDD Graph: Bridging Electronic Medical Records and Biomedical Knowledge Graphs via Entity Linking
Authors: Meng Wang, Jiaheng Zhang, Jun Liu, Wei Hu, Sen Wang, Xue Li, Wenqiang Liu
Date: 2017-07-17 00:00:00
Tasks: Entity Linking, Knowledge Graphs

Electronic medical records contain multi-format electronic medical data that
consist of an abundance of medical knowledge. Facing with patient's symptoms,
experienced caregivers make right medical decisions based on their professional
knowledge that accurately grasps relationships between symptoms, diagnosis and
corresponding treatments. In this paper, we aim to capture these relationships
by constructing a large and high-quality heterogenous graph linking patients,
diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework
to extract important medical entities from MIMIC-III (Medical Information Mart
for Intensive Care III) and automatically link them with the existing
biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD
graph presented in this paper is accessible on the Web via the SPARQL endpoint,
and provides a pathway for medical discovery and applications, such as
effective treatment recommendations.
Interpreting Classifiers through Attribute Interactions in Datasets
Authors: Andreas Henelius, Kai Puolamäki, Antti Ukkonen
Date: 2017-07-24 00:00:00
Tasks: Attribute, General Classification, Pharmacovigilance

In this work we present the novel ASTRID method for investigating which
attribute interactions classifiers exploit when making predictions. Attribute
interactions in classification tasks mean that two or more attributes together
provide stronger evidence for a particular class label. Knowledge of such
interactions makes models more interpretable by revealing associations between
attributes. This has applications, e.g., in pharmacovigilance to identify
interactions between drugs or in bioinformatics to investigate associations
between single nucleotide polymorphisms. We also show how the found attribute
partitioning is related to a factorisation of the data generating distribution
and empirically demonstrate the utility of the proposed method.
CAp 2017 challenge: Twitter Named Entity Recognition
Authors: Cédric Lopez, Ioannis Partalas, Georgios Balikas, Nadia Derbas, Amélie Martin, Coralie Reutenauer, Frédérique Segond, Massih-Reza Amini
Date: 2017-07-24 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

The paper describes the CAp 2017 challenge. The challenge concerns the
problem of Named Entity Recognition (NER) for tweets written in French. We
first present the data preparation steps we followed for constructing the
dataset released in the framework of the challenge. We begin by demonstrating
why NER for tweets is a challenging problem especially when the number of
entities increases. We detail the annotation process and the necessary
decisions we made. We provide statistics on the inter-annotator agreement, and
we conclude the data description part with examples and statistics for the
data. We, then, describe the participation in the challenge, where 8 teams
participated, with a focus on the methods employed by the challenge
participants and the scores achieved in terms of F$_1$ measure. Importantly,
the constructed dataset comprising $\sim$6,000 tweets annotated for 13 types of
entities, which to the best of our knowledge is the first such dataset in
French, is publicly available at \url{http://cap2017.imag.fr/competition.html} .
Automatic breast cancer grading in lymph nodes using a deep neural network
Authors: Thomas Wollmann, Karl Rohr
Date: 2017-07-24 00:00:00
Tasks: Classification, General Classification, whole slide images

The progression of breast cancer can be quantified in lymph node whole-slide
images (WSIs). We describe a novel method for effectively performing
classification of whole-slide images and patient level breast cancer grading.
Our method utilises a deep neural network. The method performs classification
on small patches and uses model averaging for boosting. In the first step,
region of interest patches are determined and cropped automatically by color
thresholding and then classified by the deep neural network. The classification
results are used to determine a slide level class and for further aggregation
to predict a patient level grade. Fast processing speed of our method enables
high throughput image analysis.
Temporal Convolution Based Action Proposal: Submission to ActivityNet 2017
Authors: Tianwei Lin, Xu Zhao, Zheng Shou
Date: 2017-07-21 00:00:00
Tasks: Action Classification, Action Localization, General Classification, Temporal Action Localization

In this notebook paper, we describe our approach in the submission to the
temporal action proposal (task 3) and temporal action localization (task 4) of
ActivityNet Challenge hosted at CVPR 2017. Since the accuracy in action
classification task is already very high (nearly 90% in ActivityNet dataset),
we believe that the main bottleneck for temporal action localization is the
quality of action proposals. Therefore, we mainly focus on the temporal action
proposal task and propose a new proposal model based on temporal convolutional
network. Our approach achieves the state-of-the-art performances on both
temporal action proposal task and temporal action localization task.
Learning Rare Word Representations using Semantic Bridging
Authors: Victor Prokhorov, Mohammad Taher Pilehvar, Dimitri Kartsaklis, Pietro Lió, Nigel Collier
Date: 2017-07-24 00:00:00
Tasks: Graph Embedding, Word Similarity

We propose a methodology that adapts graph embedding techniques (DeepWalk
(Perozzi et al., 2014) and node2vec (Grover and Leskovec, 2016)) as well as
cross-lingual vector space mapping approaches (Least Squares and Canonical
Correlation Analysis) in order to merge the corpus and ontological sources of
lexical knowledge. We also perform comparative analysis of the used algorithms
in order to identify the best combination for the proposed system. We then
apply this to the task of enhancing the coverage of an existing word
embedding's vocabulary with rare and unseen words. We show that our technique
can provide considerable extra coverage (over 99%), leading to consistent
performance gain (around 10% absolute gain is achieved with w2v-gn-500K cf.\S
3.3) on the Rare Word Similarity dataset.
Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach
Authors: Giorgio Roffo, Simone Melzi, Umberto Castellani, Alessandro Vinciarelli
Date: 2017-07-24 00:00:00
Proceeding: ICCV 2017 10
Tasks: feature selection, Object Recognition, Object Tracking, Visual Object Tracking

Feature selection is playing an increasingly significant role with respect to
many computer vision applications spanning from object recognition to visual
object tracking. However, most of the recent solutions in feature selection are
not robust across different and heterogeneous set of data. In this paper, we
address this issue proposing a robust probabilistic latent graph-based feature
selection algorithm that performs the ranking step while considering all the
possible subsets of features, as paths on a graph, bypassing the combinatorial
problem analytically. An appealing characteristic of the approach is that it
aims to discover an abstraction behind low-level sensory data, that is,
relevancy. Relevancy is modelled as a latent variable in a PLSA-inspired
generative process that allows the investigation of the importance of a feature
when injected into an arbitrary set of cues. The proposed method has been
tested on ten diverse benchmarks, and compared against eleven state of the art
feature selection methods. Results show that the proposed approach attains the
highest performance levels across many different scenarios and difficulties,
thereby confirming its strong robustness while setting a new state of the art
in feature selection domain.
Approximate Near Neighbors for General Symmetric Norms
Authors: Alexandr Andoni, Huy L. Nguyen, Aleksandar Nikolov, Ilya Razenshteyn, Erik Waingarten
Date: 2016-11-18 00:00:00

We show that every symmetric normed space admits an efficient nearest
neighbor search data structure with doubly-logarithmic approximation.
Specifically, for every $n$, $d = n^{o(1)}$, and every $d$-dimensional
symmetric norm $\|\cdot\|$, there exists a data structure for
$\mathrm{poly}(\log \log n)$-approximate nearest neighbor search over
$\|\cdot\|$ for $n$-point datasets achieving $n^{o(1)}$ query time and
$n^{1+o(1)}$ space. The main technical ingredient of the algorithm is a
low-distortion embedding of a symmetric norm into a low-dimensional iterated
product of top-$k$ norms.
  We also show that our techniques cannot be extended to general norms.
Generating Steganographic Images via Adversarial Training
Authors: Jamie Hayes, George Danezis
Date: 2017-03-01 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Image Generation

Adversarial training was recently shown to be competitive against supervised
learning methods on computer vision tasks, however, studies have mainly been
confined to generative tasks such as image synthesis. In this paper, we apply
adversarial training techniques to the discriminative task of learning a
steganographic algorithm. Steganography is a collection of techniques for
concealing information by embedding it within a non-secret medium, such as
cover texts or images. We show that adversarial training can produce robust
steganographic techniques: our unsupervised training scheme produces a
steganographic algorithm that competes with state-of-the-art steganographic
techniques, and produces a robust steganalyzer, which performs the
discriminative task of deciding if an image contains secret information. We
define a game between three parties, Alice, Bob and Eve, in order to
simultaneously train both a steganographic algorithm and a steganalyzer. Alice
and Bob attempt to communicate a secret message contained within an image,
while Eve eavesdrops on their conversation and attempts to determine if secret
information is embedded within the image. We represent Alice, Bob and Eve by
neural networks, and validate our scheme on two independent image datasets,
showing our novel method of studying steganographic problems is surprisingly
competitive against established steganographic techniques.
Likelihood Estimation for Generative Adversarial Networks
Authors: Hamid Eghbal-zadeh, Gerhard Widmer
Date: 2017-07-24 00:00:00

We present a simple method for assessing the quality of generated images in
Generative Adversarial Networks (GANs). The method can be applied in any kind
of GAN without interfering with the learning procedure or affecting the
learning objective. The central idea is to define a likelihood function that
correlates with the quality of the generated images. In particular, we derive a
Gaussian likelihood function from the distribution of the embeddings (hidden
activations) of the real images in the discriminator, and based on this, define
two simple measures of how likely it is that the embeddings of generated images
are from the distribution of the embeddings of the real images. This yields a
simple measure of fitness for generated images, for all varieties of GANs.
Empirical results on CIFAR-10 demonstrate a strong correlation between the
proposed measures and the perceived quality of the generated images.
Metric Learning for Generalizing Spatial Relations to New Objects
Authors: Oier Mees, Nichola Abdo, Mladen Mazuran, Wolfram Burgard
Date: 2017-03-06 00:00:00
Tasks: Metric Learning

Human-centered environments are rich with a wide variety of spatial relations
between everyday objects. For autonomous robots to operate effectively in such
environments, they should be able to reason about these relations and
generalize them to objects with different shapes and sizes. For example, having
learned to place a toy inside a basket, a robot should be able to generalize
this concept using a spoon and a cup. This requires a robot to have the
flexibility to learn arbitrary relations in a lifelong manner, making it
challenging for an expert to pre-program it with sufficient knowledge to do so
beforehand. In this paper, we address the problem of learning spatial relations
by introducing a novel method from the perspective of distance metric learning.
Our approach enables a robot to reason about the similarity between pairwise
spatial relations, thereby enabling it to use its previous knowledge when
presented with a new relation to imitate. We show how this makes it possible to
learn arbitrary spatial relations from non-expert users using a small number of
examples and in an interactive manner. Our extensive evaluation with real-world
data demonstrates the effectiveness of our method in reasoning about a
continuous spectrum of spatial relations and generalizing them to new objects.
PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations
Authors: Rico Jonschkowski, Roland Hafner, Jonathan Scholz, Martin Riedmiller
Date: 2017-05-27 00:00:00
Tasks: Image Reconstruction, Position

We propose position-velocity encoders (PVEs) which learn---without
supervision---to encode images to positions and velocities of task-relevant
objects. PVEs encode a single image into a low-dimensional position state and
compute the velocity state from finite differences in position. In contrast to
autoencoders, position-velocity encoders are not trained by image
reconstruction, but by making the position-velocity representation consistent
with priors about interacting with the physical world. We applied PVEs to
several simulated control tasks from pixels and achieved promising preliminary
results.
Content-Based Weak Supervision for Ad-Hoc Re-Ranking
Authors: Sean MacAvaney, Andrew Yates, Kai Hui, Ophir Frieder
Date: 2017-07-01 00:00:00
Tasks: Information Retrieval, Re-Ranking

One challenge with neural ranking is the need for a large amount of manually-labeled relevance judgments for training. In contrast with prior work, we examine the use of weak supervision sources for training that yield pseudo query-document pairs that already exhibit relevance (e.g., newswire headline-content pairs and encyclopedic heading-paragraph pairs). We also propose filtering techniques to eliminate training samples that are too far out of domain using two techniques: a heuristic-based approach and novel supervised filter that re-purposes a neural ranker. Using several leading neural ranking architectures and multiple weak supervision datasets, we show that these sources of training pairs are effective on their own (outperforming prior weak supervision techniques), and that filtering can further improve performance.