pwc-archive/papers-with-abstracts huggingface dataset:
An Integrated Approach for Keyphrase Generation via Exploring the Power of Retrieval and Extraction
Authors: Wang Chen, Hou Pong Chan, Piji Li, Lidong Bing, Irwin King
Date: 2019-04-06 00:00:00
Proceeding: NAACL 2019 6
Tasks: Keyphrase Generation, Multi-Task Learning, Retrieval

In this paper, we present a novel integrated approach for keyphrase
generation (KG). Unlike previous works which are purely extractive or
generative, we first propose a new multi-task learning framework that jointly
learns an extractive model and a generative model. Besides extracting
keyphrases, the output of the extractive model is also employed to rectify the
copy probability distribution of the generative model, such that the generative
model can better identify important contents from the given document. Moreover,
we retrieve similar documents with the given document from training data and
use their associated keyphrases as external knowledge for the generative model
to produce more accurate keyphrases. For further exploiting the power of
extraction and retrieval, we propose a neural-based merging module to combine
and re-rank the predicted keyphrases from the enhanced generative model, the
extractive model, and the retrieved keyphrases. Experiments on the five KG
benchmarks demonstrate that our integrated approach outperforms the
state-of-the-art methods.
Doodle to Search: Practical Zero-Shot Sketch-based Image Retrieval
Authors: Sounak Dey, Pau Riba, Anjan Dutta, Josep Llados, Yi-Zhe Song
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Retrieval, Retrieval, Sketch-Based Image Retrieval

In this paper, we investigate the problem of zero-shot sketch-based image retrieval (ZS-SBIR), where human sketches are used as queries to conduct retrieval of photos from unseen categories. We importantly advance prior arts by proposing a novel ZS-SBIR scenario that represents a firm step forward in its practical application. The new setting uniquely recognizes two important yet often neglected challenges of practical ZS-SBIR, (i) the large domain gap between amateur sketch and photo, and (ii) the necessity for moving towards large-scale retrieval. We first contribute to the community a novel ZS-SBIR dataset, QuickDraw-Extended, that consists of 330,000 sketches and 204,000 photos spanning across 110 categories. Highly abstract amateur human sketches are purposefully sourced to maximize the domain gap, instead of ones included in existing datasets that can often be semi-photorealistic. We then formulate a ZS-SBIR framework to jointly model sketches and photos into a common embedding space. A novel strategy to mine the mutual information among domains is specifically engineered to alleviate the domain gap. External semantic knowledge is further embedded to aid semantic transfer. We show that, rather surprisingly, retrieval performance significantly outperforms that of state-of-the-art on existing datasets that can already be achieved using a reduced version of our model. We further demonstrate the superior performance of our full model by comparing with a number of alternatives on the newly proposed dataset. The new dataset, plus all training and testing code of our model, will be publicly released to facilitate future research
UM-IU@LING at SemEval-2019 Task 6: Identifying Offensive Tweets Using BERT and SVMs
Authors: Jian Zhu, Zuoyu Tian, Sandra Kübler
Date: 2019-04-06 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Abuse Detection

This paper describes the UM-IU@LING's system for the SemEval 2019 Task 6:
OffensEval. We take a mixed approach to identify and categorize hate speech in
social media. In subtask A, we fine-tuned a BERT based classifier to detect
abusive content in tweets, achieving a macro F1 score of 0.8136 on the test
data, thus reaching the 3rd rank out of 103 submissions. In subtasks B and C,
we used a linear SVM with selected character n-gram features. For subtask C,
our system could identify the target of abuse with a macro F1 score of 0.5243,
ranking it 27th out of 65 submissions.
Token-Level Ensemble Distillation for Grapheme-to-Phoneme Conversion
Authors: Hao Sun, Xu Tan, Jun-Wei Gan, Hongzhi Liu, Sheng Zhao, Tao Qin, Tie-Yan Liu
Date: 2019-04-06 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Grapheme-to-Phoneme Conversion, Knowledge Distillation, speech-recognition, Speech Recognition, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Grapheme-to-phoneme (G2P) conversion is an important task in automatic speech recognition and text-to-speech systems. Recently, G2P conversion is viewed as a sequence to sequence task and modeled by RNN or CNN based encoder-decoder framework. However, previous works do not consider the practical issues when deploying G2P model in the production system, such as how to leverage additional unlabeled data to boost the accuracy, as well as reduce model size for online deployment. In this work, we propose token-level ensemble distillation for G2P conversion, which can (1) boost the accuracy by distilling the knowledge from additional unlabeled data, and (2) reduce the model size but maintain the high accuracy, both of which are very practical and helpful in the online production system. We use token-level knowledge distillation, which results in better accuracy than the sequence-level counterpart. What is more, we adopt the Transformer instead of RNN or CNN based models to further boost the accuracy of G2P conversion. Experiments on the publicly available CMUDict dataset and an internal English dataset demonstrate the effectiveness of our proposed method. Particularly, our method achieves 19.88% WER on CMUDict dataset, outperforming the previous works by more than 4.22% WER, and setting the new state-of-the-art results.
Feature-Based Interpolation and Geodesics in the Latent Spaces of Generative Models
Authors: Łukasz Struski, Michał Sadowski, Tomasz Danel, Jacek Tabor, Igor T. Podolak
Date: 2019-04-06 00:00:00

Interpolating between points is a problem connected simultaneously with finding geodesics and study of generative models. In the case of geodesics, we search for the curves with the shortest length, while in the case of generative models we typically apply linear interpolation in the latent space. However, this interpolation uses implicitly the fact that Gaussian is unimodal. Thus the problem of interpolating in the case when the latent density is non-Gaussian is an open problem. In this paper, we present a general and unified approach to interpolation, which simultaneously allows us to search for geodesics and interpolating curves in latent space in the case of arbitrary density. Our results have a strong theoretical background based on the introduced quality measure of an interpolating curve. In particular, we show that maximising the quality measure of the curve can be equivalently understood as a search of geodesic for a certain redefinition of the Riemannian metric on the space. We provide examples in three important cases. First, we show that our approach can be easily applied to finding geodesics on manifolds. Next, we focus our attention in finding interpolations in pre-trained generative models. We show that our model effectively works in the case of arbitrary density. Moreover, we can interpolate in the subset of the space consisting of data possessing a given feature. The last case is focused on finding interpolation in the space of chemical compounds.
Iterative Normalization: Beyond Standardization towards Efficient Whitening
Authors: Lei Huang, Yi Zhou, Fan Zhu, Li Liu, Ling Shao
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Robust Object Detection

Batch Normalization (BN) is ubiquitously employed for accelerating neural
network training and improving the generalization capability by performing
standardization within mini-batches. Decorrelated Batch Normalization (DBN)
further boosts the above effectiveness by whitening. However, DBN relies
heavily on either a large batch size, or eigen-decomposition that suffers from
poor efficiency on GPUs. We propose Iterative Normalization (IterNorm), which
employs Newton's iterations for much more efficient whitening, while
simultaneously avoiding the eigen-decomposition. Furthermore, we develop a
comprehensive study to show IterNorm has better trade-off between optimization
and generalization, with theoretical and experimental support. To this end, we
exclusively introduce Stochastic Normalization Disturbance (SND), which
measures the inherent stochastic uncertainty of samples when applied to
normalization operations. With the support of SND, we provide natural
explanations to several phenomena from the perspective of optimization, e.g.,
why group-wise whitening of DBN generally outperforms full-whitening and why
the accuracy of BN degenerates with reduced batch sizes. We demonstrate the
consistently improved performance of IterNorm with extensive experiments on
CIFAR-10 and ImageNet over BN and DBN.
Reinforced Imitation in Heterogeneous Action Space
Authors: Konrad Zolna, Negar Rostamzadeh, Yoshua Bengio, Sungjin Ahn, Pedro O. Pinheiro
Date: 2019-04-06 00:00:00
Tasks: Imitation Learning, Reinforcement Learning, Reinforcement Learning (RL)

Imitation learning is an effective alternative approach to learn a policy when the reward function is sparse. In this paper, we consider a challenging setting where an agent and an expert use different actions from each other. We assume that the agent has access to a sparse reward function and state-only expert observations. We propose a method which gradually balances between the imitation learning cost and the reinforcement learning objective. In addition, this method adapts the agent's policy based on either mimicking expert behavior or maximizing sparse reward. We show, through navigation scenarios, that (i) an agent is able to efficiently leverage sparse rewards to outperform standard state-only imitation learning, (ii) it can learn a policy even when its actions are different from the expert, and (iii) the performance of the agent is not bounded by that of the expert, due to the optimized usage of sparse rewards.
Unsupervised Embedding Learning via Invariant and Spreading Instance Feature
Authors: Mang Ye, Xu Zhang, Pong C. Yuen, Shih-Fu Chang
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Data Augmentation

This paper studies the unsupervised embedding learning problem, which
requires an effective similarity measurement between samples in low-dimensional
embedding space. Motivated by the positive concentrated and negative separated
properties observed from category-wise supervised learning, we propose to
utilize the instance-wise supervision to approximate these properties, which
aims at learning data augmentation invariant and instance spread-out features.
To achieve this goal, we propose a novel instance based softmax embedding
method, which directly optimizes the `real' instance features on top of the
softmax function. It achieves significantly faster learning speed and higher
accuracy than all existing methods. The proposed method performs well for both
seen and unseen testing categories with cosine similarity. It also achieves
competitive performance even without pre-trained network over samples from
fine-grained categories.
A Novel Unsupervised Camera-aware Domain Adaptation Framework for Person Re-identification
Authors: Lei Qi, Lei Wang, Jing Huo, Luping Zhou, Yinghuan Shi, Yang Gao
Date: 2019-04-06 00:00:00
Proceeding: ICCV 2019 10
Tasks: Domain Adaptation, Person Re-Identification, Representation Learning, Unsupervised Domain Adaptation

Unsupervised cross-domain person re-identification (Re-ID) faces two key issues. One is the data distribution discrepancy between source and target domains, and the other is the lack of labelling information in target domain. They are addressed in this paper from the perspective of representation learning. For the first issue, we highlight the presence of camera-level sub-domains as a unique characteristic of person Re-ID, and develop camera-aware domain adaptation to reduce the discrepancy not only between source and target domains but also across these sub-domains. For the second issue, we exploit the temporal continuity in each camera of target domain to create discriminative information. This is implemented by dynamically generating online triplets within each batch, in order to maximally take advantage of the steadily improved feature representation in training process. Together, the above two methods give rise to a novel unsupervised deep domain adaptation framework for person Re-ID. Experiments and ablation studies on benchmark datasets demonstrate its superiority and interesting properties.
FILDNE: A Framework for Incremental Learning of Dynamic Networks Embeddings
Authors: Piotr Bielak, Kamil Tagowski, Maciej Falkiewicz, Tomasz Kajdanowicz, Nitesh V. Chawla
Date: 2019-04-06 00:00:00
Tasks: Dynamic graph embedding, Graph Embedding, Incremental Learning, Link Prediction, Representation Learning

Representation learning on graphs has emerged as a powerful mechanism to automate feature vector generation for downstream machine learning tasks. The advances in representation on graphs have centered on both homogeneous and heterogeneous graphs, where the latter presenting the challenges associated with multi-typed nodes and/or edges. In this paper, we consider the additional challenge of evolving graphs. We ask the question of whether the advances in representation learning for static graphs can be leveraged for dynamic graphs and how? It is important to be able to incorporate those advances to maximize the utility and generalization of methods. To that end, we propose the Framework for Incremental Learning of Dynamic Networks Embedding (FILDNE), which can utilize any existing static representation learning method for learning node embeddings, while keeping the computational costs low. FILDNE integrates the feature vectors computed using the standard methods over different timesteps into a single representation by developing a convex combination function and alignment mechanism. Experimental results on several downstream tasks, over seven real-world data sets, show that FILDNE is able to reduce memory and computational time costs while providing competitive quality measure gains with respect to the contemporary methods for representation learning on dynamic graphs.
Context-aware Human Motion Prediction
Authors: Enric Corona, Albert Pumarola, Guillem Alenyà, Francesc Moreno-Noguer
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2020 6
Tasks: Graph Attention, Human motion prediction, motion prediction, Prediction

The problem of predicting human motion given a sequence of past observations is at the core of many applications in robotics and computer vision. Current state-of-the-art formulate this problem as a sequence-to-sequence task, in which a historical of 3D skeletons feeds a Recurrent Neural Network (RNN) that predicts future movements, typically in the order of 1 to 2 seconds. However, one aspect that has been obviated so far, is the fact that human motion is inherently driven by interactions with objects and/or other humans in the environment. In this paper, we explore this scenario using a novel context-aware motion prediction architecture. We use a semantic-graph model where the nodes parameterize the human and objects in the scene and the edges their mutual interactions. These interactions are iteratively learned through a graph attention layer, fed with the past observations, which now include both object and human body motions. Once this semantic graph is learned, we inject it to a standard RNN to predict future movements of the human/s and object/s. We consider two variants of our architecture, either freezing the contextual interactions in the future of updating them. A thorough evaluation in the "Whole-Body Human Motion Database" shows that in both cases, our context-aware networks clearly outperform baselines in which the context information is not considered.
Towards Generalized Speech Enhancement with Generative Adversarial Networks
Authors: Santiago Pascual, Joan Serrà, Antonio Bonafonte
Date: 2019-04-06 00:00:00
Tasks: Generative Adversarial Network, Speech Enhancement

The speech enhancement task usually consists of removing additive noise or
reverberation that partially mask spoken utterances, affecting their
intelligibility. However, little attention is drawn to other, perhaps more
aggressive signal distortions like clipping, chunk elimination, or
frequency-band removal. Such distortions can have a large impact not only on
intelligibility, but also on naturalness or even speaker identity, and require
of careful signal reconstruction. In this work, we give full consideration to
this generalized speech enhancement task, and show it can be tackled with a
time-domain generative adversarial network (GAN). In particular, we extend a
previous GAN-based speech enhancement system to deal with mixtures of four
types of aggressive distortions. Firstly, we propose the addition of an
adversarial acoustic regression loss that promotes a richer feature extraction
at the discriminator. Secondly, we also make use of a two-step adversarial
training schedule, acting as a warm up-and-fine-tune sequence. Both objective
and subjective evaluations show that these two additions bring improved speech
reconstructions that better match the original speaker identity and
naturalness.
Speeding Up Natural Language Parsing by Reusing Partial Results
Authors: Michalina Strzyz, Carlos Gómez-Rodríguez
Date: 2019-04-06 00:00:00
Tasks: POS

This paper proposes a novel technique that applies case-based reasoning in
order to generate templates for reusable parse tree fragments, based on PoS
tags of bigrams and trigrams that demonstrate low variability in their
syntactic analyses from prior data. The aim of this approach is to improve the
speed of dependency parsers by avoiding redundant calculations. This can be
resolved by applying the predefined templates that capture results of previous
syntactic analyses and directly assigning the stored structure to a new n-gram
that matches one of the templates, instead of parsing a similar text fragment
again. The study shows that using a heuristic approach to select and reuse the
partial results increases parsing speed by reducing the input length to be
processed by a parser. The increase in parsing speed comes at some expense of
accuracy. Experiments on English show promising results: the input dimension
can be reduced by more than 20% at the cost of less than 3 points of Unlabeled
Attachment Score.
Learning Problem-agnostic Speech Representations from Multiple Self-supervised Tasks
Authors: Santiago Pascual, Mirco Ravanelli, Joan Serrà, Antonio Bonafonte, Yoshua Bengio
Date: 2019-04-06 00:00:00
Tasks: Distant Speech Recognition

Learning good representations without supervision is still an open issue in
machine learning, and is particularly challenging for speech signals, which are
often characterized by long sequences with a complex hierarchical structure.
Some recent works, however, have shown that it is possible to derive useful
speech representations by employing a self-supervised encoder-discriminator
approach. This paper proposes an improved self-supervised method, where a
single neural encoder is followed by multiple workers that jointly solve
different self-supervised tasks. The needed consensus across different tasks
naturally imposes meaningful constraints to the encoder, contributing to
discover general representations and to minimize the risk of learning
superficial ones. Experiments show that the proposed approach can learn
transferable, robust, and problem-agnostic features that carry on relevant
information from the speech signal, such as speaker identity, phonemes, and
even higher-level features such as emotional cues. In addition, a number of
design choices make the encoder easily exportable, facilitating its direct
usage or adaptation to different problems.
Parallelizable Stack Long Short-Term Memory
Authors: Shuoyang Ding, Philipp Koehn
Date: 2019-04-06 00:00:00
Proceeding: WS 2019 6
Tasks: GPU, Machine Translation, Translation

Stack Long Short-Term Memory (StackLSTM) is useful for various applications
such as parsing and string-to-tree neural machine translation, but it is also
known to be notoriously difficult to parallelize for GPU training due to the
fact that the computations are dependent on discrete operations. In this paper,
we tackle this problem by utilizing state access patterns of StackLSTM to
homogenize computations with regard to different discrete operations. Our
parsing experiments show that the method scales up almost linearly with
increasing batch size, and our parallelized PyTorch implementation trains
significantly faster compared to the Dynet C++ implementation.
Deep Surface Normal Estimation with Hierarchical RGB-D Fusion
Authors: Jin Zeng, Yanfeng Tong, Yunmu Huang, Qiong Yan, Wenxiu Sun, Jing Chen, Yongtian Wang
Date: 2019-04-06 00:00:00
Proceeding: CVPR 2019 6
Tasks: Scene Understanding, Surface Normal Estimation

The growing availability of commodity RGB-D cameras has boosted the applications in the field of scene understanding. However, as a fundamental scene understanding task, surface normal estimation from RGB-D data lacks thorough investigation. In this paper, a hierarchical fusion network with adaptive feature re-weighting is proposed for surface normal estimation from a single RGB-D image. Specifically, the features from color image and depth are successively integrated at multiple scales to ensure global surface smoothness while preserving visually salient details. Meanwhile, the depth features are re-weighted with a confidence map estimated from depth before merging into the color branch to avoid artifacts caused by input depth corruption. Additionally, a hybrid multi-scale loss function is designed to learn accurate normal estimation given noisy ground-truth dataset. Extensive experimental results validate the effectiveness of the fusion strategy and the loss design, outperforming state-of-the-art normal estimation schemes.
Idealize - A Notion of Idea Strength
Authors: Rui Portocarrero Sarmento
Date: 2019-04-06 00:00:00

Business Entrepreneurs frequently thrive on looking for ways to test business
ideas, without giving too much information. Recent techniques in startup
development promote the use of surveys to measure the potential client's
interest. In this preliminary report, we describe the concept behind Idealize,
a Shiny R application to measure the local trend strength of a potential idea.
Additionally, the system might provide a relative distance to the capital city
of the country. The tests were made for the United States of America, i.e.,
made available regarding native English language. This report shows some of the
tests results with this system.
Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation
Authors: Amit Moryossef, Yoav Goldberg, Ido Dagan
Date: 2019-04-06 00:00:00
Proceeding: NAACL 2019 6
Tasks: Data-to-Text Generation, Graph-to-Sequence, Text Generation

Data-to-text generation can be conceptually divided into two parts: ordering
and structuring the information (planning), and generating fluent language
describing the information (realization). Modern neural generation systems
conflate these two steps into a single end-to-end differentiable system. We
propose to split the generation process into a symbolic text-planning stage
that is faithful to the input, followed by a neural generation stage that
focuses only on realization. For training a plan-to-text generator, we present
a method for matching reference texts to their corresponding text plans. For
inference time, we describe a method for selecting high-quality text plans for
new inputs. We implement and evaluate our approach on the WebNLG benchmark. Our
results demonstrate that decoupling text planning from neural realization
indeed improves the system's reliability and adequacy while maintaining fluent
output. We observe improvements both in BLEU scores and in manual evaluations.
Another benefit of our approach is the ability to output diverse realizations
of the same input, paving the way to explicit control over the generated text
structure.