pwc-archive/papers-with-abstracts huggingface dataset:
Identifying and Controlling Important Neurons in Neural Machine Translation
Authors: Anthony Bau, Yonatan Belinkov, Hassan Sajjad, Nadir Durrani, Fahim Dalvi, James Glass
Date: 2018-11-03 00:00:00
Proceeding: ICLR 2019 5
Tasks: Machine Translation, NMT, Translation

Neural machine translation (NMT) models learn representations containing
substantial linguistic information. However, it is not clear if such
information is fully distributed or if some of it can be attributed to
individual neurons. We develop unsupervised methods for discovering important
neurons in NMT models. Our methods rely on the intuition that different models
learn similar properties, and do not require any costly external supervision.
We show experimentally that translation quality depends on the discovered
neurons, and find that many of them capture common linguistic phenomena.
Finally, we show how to control NMT translations in predictable ways, by
modifying activations of individual neurons.
SafeRoute: Learning to Navigate Streets Safely in an Urban Environment
Authors: Sharon Levy, Wenhan Xiong, Elizabeth Belding, William Yang Wang
Date: 2018-11-03 00:00:00
Tasks: Deep Reinforcement Learning, Navigate, Reinforcement Learning, Representation Learning

Recent studies show that 85% of women have changed their traveled route to
avoid harassment and assault. Despite this, current mapping tools do not
empower users with information to take charge of their personal safety. We
propose SafeRoute, a novel solution to the problem of navigating cities and
avoiding street harassment and crime. Unlike other street navigation
applications, SafeRoute introduces a new type of path generation via deep
reinforcement learning. This enables us to successfully optimize for
multi-criteria path-finding and incorporate representation learning within our
framework. Our agent learns to pick favorable streets to create a safe and
short path with a reward function that incorporates safety and efficiency.
Given access to recent crime reports in many urban cities, we train our model
for experiments in Boston, New York, and San Francisco. We test our model on
areas of these cities, specifically the populated downtown regions where
tourists and those unfamiliar with the streets walk. We evaluate SafeRoute and
successfully improve over state-of-the-art methods by up to 17% in local
average distance from crimes while decreasing path length by up to 7%.
Closed-Loop Memory GAN for Continual Learning
Authors: Amanda Rios, Laurent Itti
Date: 2018-11-03 00:00:00
Tasks: Continual Learning, Diversity

Sequential learning of tasks using gradient descent leads to an unremitting decline in the accuracy of tasks for which training data is no longer available, termed catastrophic forgetting. Generative models have been explored as a means to approximate the distribution of old tasks and bypass storage of real data. Here we propose a cumulative closed-loop memory replay GAN (CloGAN) provided with external regularization by a small memory unit selected for maximum sample diversity. We evaluate incremental class learning using a notoriously hard paradigm, single-headed learning, in which each task is a disjoint subset of classes in the overall dataset, and performance is evaluated on all previous classes. First, we show that when constructing a dynamic memory unit to preserve sample heterogeneity, model performance asymptotically approaches training on the full dataset. We then show that using a stochastic generator to continuously output fresh new images during training increases performance significantly further meanwhile generating quality images. We compare our approach to several baselines including fine-tuning by gradient descent (FGD), Elastic Weight Consolidation (EWC), Deep Generative Replay (DGR) and Memory Replay GAN (MeRGAN). Our method has very low long-term memory cost, the memory unit, as well as negligible intermediate memory storage.
Transfer Learning in Multilingual Neural Machine Translation with Dynamic Vocabulary
Authors: Surafel M. Lakew, Aliia Erofeeva, Matteo Negri, Marcello Federico, Marco Turchi
Date: 2018-11-03 00:00:00
Proceeding: IWSLT (EMNLP) 2018 10
Tasks: Machine Translation, NMT, Transfer Learning, Translation

We propose a method to transfer knowledge across neural machine translation
(NMT) models by means of a shared dynamic vocabulary. Our approach allows to
extend an initial model for a given language pair to cover new languages by
adapting its vocabulary as long as new data become available (i.e., introducing
new vocabulary items if they are not included in the initial model). The
parameter transfer mechanism is evaluated in two scenarios: i) to adapt a
trained single language NMT system to work with a new language pair and ii) to
continuously add new language pairs to grow to a multilingual NMT system. In
both the scenarios our goal is to improve the translation performance, while
minimizing the training convergence time. Preliminary experiments spanning five
languages with different training data sizes (i.e., 5k and 50k parallel
sentences) show a significant performance gain ranging from +3.85 up to +13.63
BLEU in different language directions. Moreover, when compared with training an
NMT model from scratch, our transfer-learning approach allows us to reach
higher performance after training up to 4% of the total training steps.
Margin-based Parallel Corpus Mining with Multilingual Sentence Embeddings
Authors: Mikel Artetxe, Holger Schwenk
Date: 2018-11-03 00:00:00
Proceeding: ACL 2019 7
Tasks: Cross-Lingual Bitext Mining, Machine Translation, Parallel Corpus Mining, Retrieval, Sentence, Sentence Embeddings, Translation

Machine translation is highly sensitive to the size and quality of the training data, which has led to an increasing interest in collecting and filtering large parallel corpora. In this paper, we propose a new method for this task based on multilingual sentence embeddings. In contrast to previous approaches, which rely on nearest neighbor retrieval with a hard threshold over cosine similarity, our proposed method accounts for the scale inconsistencies of this measure, considering the margin between a given sentence pair and its closest candidates instead. Our experiments show large improvements over existing methods. We outperform the best published results on the BUCC mining task and the UN reconstruction task by more than 10 F1 and 30 precision points, respectively. Filtering the English-German ParaCrawl corpus with our approach, we obtain 31.2 BLEU points on newstest2014, an improvement of more than one point over the best official filtered version.
VIREL: A Variational Inference Framework for Reinforcement Learning
Authors: Matthew Fellows, Anuj Mahajan, Tim G. J. Rudner, Shimon Whiteson
Date: 2018-11-03 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Variational Inference

Applying probabilistic models to reinforcement learning (RL) enables the application of powerful optimisation tools such as variational inference to RL. However, existing inference frameworks and their algorithms pose significant challenges for learning optimal policies, e.g., the absence of mode capturing behaviour in pseudo-likelihood methods and difficulties learning deterministic policies in maximum entropy RL based approaches. We propose VIREL, a novel, theoretically grounded probabilistic inference framework for RL that utilises a parametrised action-value function to summarise future dynamics of the underlying MDP. This gives VIREL a mode-seeking form of KL divergence, the ability to learn deterministic optimal polices naturally from inference and the ability to optimise value functions and policies in separate, iterative steps. In applying variational expectation-maximisation to VIREL we thus show that the actor-critic algorithm can be reduced to expectation-maximisation, with policy improvement equivalent to an E-step and policy evaluation to an M-step. We then derive a family of actor-critic methods from VIREL, including a scheme for adaptive exploration. Finally, we demonstrate that actor-critic algorithms from this family outperform state-of-the-art methods based on soft value functions in several domains.
Exploiting Explicit Paths for Multi-hop Reading Comprehension
Authors: Souvik Kundu, Tushar Khot, Ashish Sabharwal, Peter Clark
Date: 2018-11-02 00:00:00
Proceeding: ACL 2019 7
Tasks: Implicit Relations, Knowledge Graphs, Multi-Hop Reading Comprehension, Reading Comprehension

We propose a novel, path-based reasoning approach for the multi-hop reading comprehension task where a system needs to combine facts from multiple passages to answer a question. Although inspired by multi-hop reasoning over knowledge graphs, our proposed approach operates directly over unstructured text. It generates potential paths through passages and scores them without any direct path supervision. The proposed model, named PathNet, attempts to extract implicit relations from text through entity pair representations, and compose them to encode each path. To capture additional context, PathNet also composes the passage representations along each path to compute a passage-based representation. Unlike previous approaches, our model is then able to explain its reasoning via these explicit paths through the passages. We show that our approach outperforms prior models on the multi-hop Wikihop dataset, and also can be generalized to apply to the OpenBookQA dataset, matching state-of-the-art performance.
Unsupervised Hyperalignment for Multilingual Word Embeddings
Authors: Jean Alaux, Edouard Grave, Marco Cuturi, Armand Joulin
Date: 2018-11-02 00:00:00
Tasks: Multilingual Word Embeddings, Translation, Word Embeddings, Word Translation

We consider the problem of aligning continuous word representations, learned in multiple languages, to a common space. It was recently shown that, in the case of two languages, it is possible to learn such a mapping without supervision. This paper extends this line of work to the problem of aligning multiple languages to a common space. A solution is to independently map all languages to a pivot language. Unfortunately, this degrades the quality of indirect word translation. We thus propose a novel formulation that ensures composable mappings, leading to better alignments. We evaluate our method by jointly aligning word vectors in eleven languages, showing consistent improvement with indirect mappings while maintaining competitive performance on direct word translation.
Topological Approaches to Deep Learning
Authors: Gunnar Carlsson, Rickard Brüel Gabrielsson
Date: 2018-11-02 00:00:00
Tasks: Deep Learning, Topological Data Analysis

We perform topological data analysis on the internal states of convolutional
deep neural networks to develop an understanding of the computations that they
perform. We apply this understanding to modify the computations so as to (a)
speed up computations and (b) improve generalization from one data set of
digits to another. One byproduct of the analysis is the production of a
geometry on new sets of features on data sets of images, and use this
observation to develop a methodology for constructing analogues of CNN's for
many other geometries, including the graph structures constructed by
topological data analysis.
Learning to Rank Query Graphs for Complex Question Answering over Knowledge Graphs
Authors: Gaurav Maheshwari, Priyansh Trivedi, Denis Lukovnikov, Nilesh Chakraborty, Asja Fischer, Jens Lehmann
Date: 2018-11-02 00:00:00
Tasks: Graph Ranking, Knowledge Graphs, Learning-To-Rank, Question Answering, Transfer Learning

In this paper, we conduct an empirical investigation of neural query graph
ranking approaches for the task of complex question answering over knowledge
graphs. We experiment with six different ranking models and propose a novel
self-attention based slot matching model which exploits the inherent structure
of query graphs, our logical form of choice. Our proposed model generally
outperforms the other models on two QA datasets over the DBpedia knowledge
graph, evaluated in different settings. In addition, we show that transfer
learning from the larger of those QA datasets to the smaller dataset yields
substantial improvements, effectively offsetting the general lack of training
data.
Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation
Authors: Xing Niu, Weijia Xu, Marine Carpuat
Date: 2018-11-02 00:00:00
Proceeding: NAACL 2019 6
Tasks: Low Resource Neural Machine Translation, Low-Resource Neural Machine Translation, Machine Translation, NMT, Translation

We aim to better exploit the limited amounts of parallel text available in
low-resource settings by introducing a differentiable reconstruction loss for
neural machine translation (NMT). This loss compares original inputs to
reconstructed inputs, obtained by back-translating translation hypotheses into
the input language. We leverage differentiable sampling and bi-directional NMT
to train models end-to-end, without introducing additional parameters. This
approach achieves small but consistent BLEU improvements on four language pairs
in both translation directions, and outperforms an alternative differentiable
reconstruction strategy based on hidden states.
Neural Task Representations as Weak Supervision for Model Agnostic Cross-Lingual Transfer
Authors: Sujay Kumar Jauhar, Michael Gamon, Patrick Pantel
Date: 2018-11-02 00:00:00
Tasks: Cross-Lingual Transfer

Natural language processing is heavily Anglo-centric, while the demand for
models that work in languages other than English is greater than ever. Yet, the
task of transferring a model from one language to another can be expensive in
terms of annotation costs, engineering time and effort. In this paper, we
present a general framework for easily and effectively transferring neural
models from English to other languages. The framework, which relies on task
representations as a form of weak supervision, is model and task agnostic,
meaning that many existing neural architectures can be ported to other
languages with minimal effort. The only requirement is unlabeled parallel data,
and a loss defined over task representations. We evaluate our framework by
transferring an English sentiment classifier to three different languages. On a
battery of tests, we show that our models outperform a number of strong
baselines and rival state-of-the-art results, which rely on more complex
approaches and significantly more resources and data. Additionally, we find
that the framework proposed in this paper is able to capture semantically rich
and meaningful representations across languages, despite the lack of direct
supervision.
Machine learning architectures to predict motion sickness using a Virtual Reality rollercoaster simulation tool
Authors: Stefan Hell, Vasileios Argyriou
Date: 2018-11-02 00:00:00
Tasks: BIG-bench Machine Learning

Virtual Reality (VR) can cause an unprecedented immersion and feeling of
presence yet a lot of users experience motion sickness when moving through a
virtual environment. Rollercoaster rides are popular in Virtual Reality but
have to be well designed to limit the amount of nausea the user may feel. This
paper describes a novel framework to get automated ratings on motion sickness
using Neural Networks. An application that lets users create rollercoasters
directly in VR, share them with other users and ride and rate them is used to
gather real-time data related to the in-game behaviour of the player, the track
itself and users' ratings based on a Simulator Sickness Questionnaire (SSQ)
integrated into the application. Machine learning architectures based on deep
neural networks are trained using this data aiming to predict motion sickness
levels. While this paper focuses on rollercoasters this framework could help to
rate any VR application on motion sickness and intensity that involves camera
movement. A new well defined dataset is provided in this paper and the
performance of the proposed architectures are evaluated in a comparative study.
Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization
Authors: Jiacheng Zhang, Yang Liu, Huanbo Luan, Jingfang Xu, Maosong Sun
Date: 2018-11-02 00:00:00
Proceeding: ACL 2017 7
Tasks: Machine Translation, Translation

Although neural machine translation has made significant progress recently,
how to integrate multiple overlapping, arbitrary prior knowledge sources
remains a challenge. In this work, we propose to use posterior regularization
to provide a general framework for integrating prior knowledge into neural
machine translation. We represent prior knowledge sources as features in a
log-linear model, which guides the learning process of the neural translation
model. Experiments on Chinese-English translation show that our approach leads
to significant improvements.
Beyond Equal-Length Snippets: How Long is Sufficient to Recognize an Audio Scene?
Authors: Huy Phan, Oliver Y. Chén, Philipp Koch, Lam Pham, Ian McLoughlin, Alfred Mertins, Maarten De Vos
Date: 2018-11-02 00:00:00
Tasks: General Classification, Scene Classification

Due to the variability in characteristics of audio scenes, some scenes can naturally be recognized earlier than others. In this work, rather than using equal-length snippets for all scene categories, as is common in the literature, we study to which temporal extent an audio scene can be reliably recognized given state-of-the-art models. Moreover, as model fusion with deep network ensemble is prevalent in audio scene classification, we further study whether, and if so, when model fusion is necessary for this task. To achieve these goals, we employ two single-network systems relying on a convolutional neural network and a recurrent neural network for classification as well as early fusion and late fusion of these networks. Experimental results on the LITIS-Rouen dataset show that some scenes can be reliably recognized with a few seconds while other scenes require significantly longer durations. In addition, model fusion is shown to be the most beneficial when the signal length is short.
Unifying Isolated and Overlapping Audio Event Detection with Multi-Label Multi-Task Convolutional Recurrent Neural Networks
Authors: Huy Phan, Oliver Y. Chén, Philipp Koch, Lam Pham, Ian McLoughlin, Alfred Mertins, Maarten De Vos
Date: 2018-11-02 00:00:00
Tasks: Event Detection, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

We propose a multi-label multi-task framework based on a convolutional
recurrent neural network to unify detection of isolated and overlapping audio
events. The framework leverages the power of convolutional recurrent neural
network architectures; convolutional layers learn effective features over which
higher recurrent layers perform sequential modelling. Furthermore, the output
layer is designed to handle arbitrary degrees of event overlap. At each time
step in the recurrent output sequence, an output triple is dedicated to each
event category of interest to jointly model event occurrence and temporal
boundaries. That is, the network jointly determines whether an event of this
category occurs, and when it occurs, by estimating onset and offset positions
at each recurrent time step. We then introduce three sequential losses for
network training: multi-label classification loss, distance estimation loss,
and confidence loss. We demonstrate good generalization on two datasets:
ITC-Irst for isolated audio event detection, and TUT-SED-Synthetic-2016 for
overlapping audio event detection.
Value-based Search in Execution Space for Mapping Instructions to Programs
Authors: Dor Muhlgay, Jonathan Herzig, Jonathan Berant
Date: 2018-11-02 00:00:00
Proceeding: NAACL 2019 6

Training models to map natural language instructions to programs given target
world supervision only requires searching for good programs at training time.
Search is commonly done using beam search in the space of partial programs or
program trees, but as the length of the instructions grows finding a good
program becomes difficult. In this work, we propose a search algorithm that
uses the target world state, known at training time, to train a critic network
that predicts the expected reward of every search state. We then score search
states on the beam by interpolating their expected reward with the likelihood
of programs represented by the search state. Moreover, we search not in the
space of programs but in a more compressed state of program executions,
augmented with recent entities and actions. On the SCONE dataset, we show that
our algorithm dramatically improves performance on all three domains compared
to standard beam search and other baselines.
Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks
Authors: Jason Phang, Thibault Févry, Samuel R. Bowman
Date: 2018-11-02 00:00:00
Tasks: Language Modeling, Language Modelling, Natural Language Inference, Sentence, Transfer Learning

Pretraining sentence encoders with language modeling and related unsupervised
tasks has recently been shown to be very effective for language understanding
tasks. By supplementing language model-style pretraining with further training
on data-rich supervised tasks, such as natural language inference, we obtain
additional performance improvements on the GLUE benchmark. Applying
supplementary training on BERT (Devlin et al., 2018), we attain a GLUE score of
81.8---the state of the art (as of 02/24/2019) and a 1.4 point improvement over
BERT. We also observe reduced variance across random restarts in this setting.
Our approach yields similar improvements when applied to ELMo (Peters et al.,
2018a) and Radford et al. (2018)'s model. In addition, the benefits of
supplementary training are particularly pronounced in data-constrained regimes,
as we show in experiments with artificially limited training data.
Ischemic Stroke Lesion Segmentation in CT Perfusion Scans using Pyramid Pooling and Focal Loss
Authors: S. Mazdak Abulnaga, Jonathan Rubin
Date: 2018-11-02 00:00:00
Tasks: Ischemic Stroke Lesion Segmentation, Lesion Segmentation, Segmentation

We present a fully convolutional neural network for segmenting ischemic
stroke lesions in CT perfusion images for the ISLES 2018 challenge. Treatment
of stroke is time sensitive and current standards for lesion identification
require manual segmentation, a time consuming and challenging process.
Automatic segmentation methods present the possibility of accurately
identifying lesions and improving treatment planning. Our model is based on the
PSPNet, a network architecture that makes use of pyramid pooling to provide
global and local contextual information. To learn the varying shapes of the
lesions, we train our network using focal loss, a loss function designed for
the network to focus on learning the more difficult samples. We compare our
model to networks trained using the U-Net and V-Net architectures. Our approach
demonstrates effective performance in lesion segmentation and ranked among the
top performers at the challenge conclusion.
3D Pick & Mix: Object Part Blending in Joint Shape and Image Manifolds
Authors: Adrian Penate-Sanchez, Lourdes Agapito
Date: 2018-11-02 00:00:00
Tasks: 3D Shape Classification, 3D Shape Retrieval, Retrieval

We present 3D Pick & Mix, a new 3D shape retrieval system that provides users
with a new level of freedom to explore 3D shape and Internet image collections
by introducing the ability to reason about objects at the level of their
constituent parts. While classic retrieval systems can only formulate simple
searches such as "find the 3D model that is most similar to the input image"
our new approach can formulate advanced and semantically meaningful search
queries such as: "find me the 3D model that best combines the design of the
legs of the chair in image 1 but with no armrests, like the chair in image 2".
Many applications could benefit from such rich queries, users could browse
through catalogues of furniture and pick and mix parts, combining for example
the legs of a chair from one shop and the armrests from another shop.