pwc-archive/papers-with-abstracts huggingface dataset:
Tetra-Tagging: Word-Synchronous Parsing with Linear-Time Inference
Authors: Nikita Kitaev, Dan Klein
Date: 2019-04-22 00:00:00
Proceeding: ACL 2020 6
Tasks: Constituency Parsing, Sentence

We present a constituency parsing algorithm that, like a supertagger, works by assigning labels to each word in a sentence. In order to maximally leverage current neural architectures, the model scores each word's tags in parallel, with minimal task-specific structure. After scoring, a left-to-right reconciliation phase extracts a tree in (empirically) linear time. Our parser achieves 95.4 F1 on the WSJ test set while also achieving substantial speedups compared to current state-of-the-art parsers with comparable accuracies.
Reliable Weakly Supervised Learning: Maximize Gain and Maintain Safeness
Authors: Lan-Zhe Guo, Yu-Feng Li, Ming Li, Jin-Feng Yi, Bo-Wen Zhou, Zhi-Hua Zhou
Date: 2019-04-22 00:00:00
Tasks: Weakly-supervised Learning

Weakly supervised data are widespread and have attracted much attention.
However, since label quality is often difficult to guarantee, sometimes the use
of weakly supervised data will lead to unsatisfactory performance, i.e.,
performance degradation or poor performance gains. Moreover, it is usually not
feasible to manually increase the label quality, which results in weakly
supervised learning being somewhat difficult to rely on. In view of this
crucial issue, this paper proposes a simple and novel weakly supervised
learning framework. We guide the optimization of label quality through a small
amount of validation data, and to ensure the safeness of performance while
maximizing performance gain. As validation set is a good approximation for
describing generalization risk, it can effectively avoid the unsatisfactory
performance caused by incorrect data distribution assumptions. We formalize
this underlying consideration into a novel Bi-Level optimization and give an
effective solution. Extensive experimental results verify that the new
framework achieves impressive performance on weakly supervised learning with a
small amount of validation data.
2D3D-MatchNet: Learning to Match Keypoints Across 2D Image and 3D Point Cloud
Authors: Mengdan Feng, Sixing Hu, Marcelo Ang, Gim Hee Lee
Date: 2019-04-22 00:00:00
Tasks: Pose Estimation

Large-scale point cloud generated from 3D sensors is more accurate than its
image-based counterpart. However, it is seldom used in visual pose estimation
due to the difficulty in obtaining 2D-3D image to point cloud correspondences.
In this paper, we propose the 2D3D-MatchNet - an end-to-end deep network
architecture to jointly learn the descriptors for 2D and 3D keypoint from image
and point cloud, respectively. As a result, we are able to directly match and
establish 2D-3D correspondences from the query image and 3D point cloud
reference map for visual pose estimation. We create our Oxford 2D-3D Patches
dataset from the Oxford Robotcar dataset with the ground truth camera poses and
2D-3D image to point cloud correspondences for training and testing the deep
network. Experimental results verify the feasibility of our approach.
NLP Driven Ensemble Based Automatic Subtitle Generation and Semantic Video Summarization Technique
Authors: VB Aswin, Mohammed Javed, Parag Parihar, K Aswanth, CR Druval, Anpam Dagar, CV Aravinda
Date: 2019-04-22 00:00:00
Tasks: speech-recognition, Speech Recognition, Text Summarization, Video Summarization

This paper proposes an automatic subtitle generation and semantic video
summarization technique. The importance of automatic video summarization is
vast in the present era of big data. Video summarization helps in efficient
storage and also quick surfing of large collection of videos without losing the
important ones. The summarization of the videos is done with the help of
subtitles which is obtained using several text summarization algorithms. The
proposed technique generates the subtitle for videos with/without subtitles
using speech recognition and then applies NLP based Text summarization
algorithms on the subtitles. The performance of subtitle generation and video
summarization is boosted through Ensemble method with two approaches such as
Intersection method and Weight based learning method Experimental results
reported show the satisfactory performance of the proposed method
Switchable Whitening for Deep Representation Learning
Authors: Xingang Pan, Xiaohang Zhan, Jianping Shi, Xiaoou Tang, Ping Luo
Date: 2019-04-22 00:00:00
Proceeding: ICCV 2019 10
Tasks: Domain Adaptation, image-classification, Image Classification, Representation Learning, Robust Object Detection, Semantic Segmentation, Style Transfer

Normalization methods are essential components in convolutional neural networks (CNNs). They either standardize or whiten data using statistics estimated in predefined sets of pixels. Unlike existing works that design normalization techniques for specific tasks, we propose Switchable Whitening (SW), which provides a general form unifying different whitening methods as well as standardization methods. SW learns to switch among these operations in an end-to-end manner. It has several advantages. First, SW adaptively selects appropriate whitening or standardization statistics for different tasks (see Fig.1), making it well suited for a wide range of tasks without manual design. Second, by integrating benefits of different normalizers, SW shows consistent improvements over its counterparts in various challenging benchmarks. Third, SW serves as a useful tool for understanding the characteristics of whitening and standardization techniques. We show that SW outperforms other alternatives on image classification (CIFAR-10/100, ImageNet), semantic segmentation (ADE20K, Cityscapes), domain adaptation (GTA5, Cityscapes), and image style transfer (COCO). For example, without bells and whistles, we achieve state-of-the-art performance with 45.33% mIoU on the ADE20K dataset. Code is available at https://github.com/XingangPan/Switchable-Whitening.
Facial Expression Recognition Research Based on Deep Learning
Authors: Yongpei Zhu, Hongwei Fan, Kehong Yuan
Date: 2019-04-22 00:00:00
Tasks: Deep Learning, Facial Expression Recognition, Facial Expression Recognition (FER), General Classification, Object Recognition

With the development of deep learning, the structure of convolution neural network is becoming more and more complex and the performance of object recognition is getting better. However, the classification mechanism of convolution neural networks is still an unsolved core problem. The main problem is that convolution neural networks have too many parameters, which makes it difficult to analyze them. In this paper, we design and train a convolution neural network based on the expression recognition, and explore the classification mechanism of the network. By using the Deconvolution visualization method, the extremum point of the convolution neural network is projected back to the pixel space of the original image, and we qualitatively verify that the trained expression recognition convolution neural network forms a detector for the specific facial action unit. At the same time, we design the distance function to measure the distance between the presence of facial feature unit and the maximal value of the response on the feature map of convolution neural network. The greater the distance, the more sensitive the feature map is to the facial feature unit. By comparing the maximum distance of all facial feature elements in the feature graph, the mapping relationship between facial feature element and convolution neural network feature map is determined. Therefore, we have verified that the convolution neural network has formed a detector for the facial Action unit in the training process to realize the expression recognition.
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
Authors: Youngwan Lee, Joong-won Hwang, Sangrok Lee, Yuseok Bae, Jongyoul Park
Date: 2019-04-22 00:00:00
Tasks: GPU, object-detection, Object Detection, Real-Time Object Detection, Semantic Segmentation, Small Object Detection

As DenseNet conserves intermediate features with diverse receptive fields by
aggregating them with dense connection, it shows good performance on the object
detection task. Although feature reuse enables DenseNet to produce strong
features with a small number of model parameters and FLOPs, the detector with
DenseNet backbone shows rather slow speed and low energy efficiency. We find
the linearly increasing input channel by dense connection leads to heavy memory
access cost, which causes computation overhead and more energy consumption. To
solve the inefficiency of DenseNet, we propose an energy and computation
efficient architecture called VoVNet comprised of One-Shot Aggregation (OSA).
The OSA not only adopts the strength of DenseNet that represents diversified
features with multi receptive fields but also overcomes the inefficiency of
dense connection by aggregating all features only once in the last feature
maps. To validate the effectiveness of VoVNet as a backbone network, we design
both lightweight and large-scale VoVNet and apply them to one-stage and
two-stage object detectors. Our VoVNet based detectors outperform DenseNet
based ones with 2x faster speed and the energy consumptions are reduced by 1.6x
- 4.1x. In addition to DenseNet, VoVNet also outperforms widely used ResNet
backbone with faster speed and better energy efficiency. In particular, the
small object detection performance has been significantly improved over
DenseNet and ResNet.
SocialIQA: Commonsense Reasoning about Social Interactions
Authors: Maarten Sap, Hannah Rashkin, Derek Chen, Ronan LeBras, Yejin Choi
Date: 2019-04-22 00:00:00
Tasks: Common Sense Reasoning, Coreference Resolution, Multiple-choice, Question Answering, Transfer Learning

We introduce Social IQa, the first largescale benchmark for commonsense reasoning about social situations. Social IQa contains 38,000 multiple choice questions for probing emotional and social intelligence in a variety of everyday situations (e.g., Q: "Jordan wanted to tell Tracy a secret, so Jordan leaned towards Tracy. Why did Jordan do this?" A: "Make sure no one else could hear"). Through crowdsourcing, we collect commonsense questions along with correct and incorrect answers about social interactions, using a new framework that mitigates stylistic artifacts in incorrect answers by asking workers to provide the right answer to a different but related question. Empirical results show that our benchmark is challenging for existing question-answering models based on pretrained language models, compared to human performance (>20% gap). Notably, we further establish Social IQa as a resource for transfer learning of commonsense knowledge, achieving state-of-the-art performance on multiple commonsense reasoning tasks (Winograd Schemas, COPA).
FishNet: A Camera Localizer using Deep Recurrent Networks
Authors: Hsin-I Chen, Sebastian Agethen, Chiamin Wu, Winston Hsu, Bing-Yu Chen
Date: 2019-04-22 00:00:00
Tasks: Camera Pose Estimation, Deep Learning, Pose Estimation

This paper proposes a robust localization system that employs deep learning
for better scene representation, and enhances the accuracy of 6-DOF camera pose
estimation. Inspired by the fact that global scene structure can be revealed by
wide field-of-view, we leverage the large overlap of a fisheye camera between
adjacent frames, and the powerful high-level feature representations of deep
learning. Our main contribution is the novel network architecture that extracts
both temporal and spatial information using a Recurrent Neural Network.
Specifically, we propose a novel pose regularization term combined with LSTM.
This leads to smoother pose estimation, especially for large outdoor scenery.
Promising experimental results on three benchmark datasets manifest the
effectiveness of the proposed approach.
Understanding Roles and Entities: Datasets and Models for Natural Language Inference
Authors: Arindam Mitra, Ishan Shrivastava, Chitta Baral
Date: 2019-04-22 00:00:00
Tasks: Natural Language Inference

We present two new datasets and a novel attention mechanism for Natural
Language Inference (NLI). Existing neural NLI models, even though when trained
on existing large datasets, do not capture the notion of entity and role well
and often end up making mistakes such as "Peter signed a deal" can be inferred
from "John signed a deal". The two datasets have been developed to mitigate
such issues and make the systems better at understanding the notion of
"entities" and "roles". After training the existing architectures on the new
dataset we observe that the existing architectures does not perform well on one
of the new benchmark. We then propose a modification to the "word-to-word"
attention function which has been uniformly reused across several popular NLI
architectures. The resulting architectures perform as well as their unmodified
counterparts on the existing benchmarks and perform significantly well on the
new benchmark for "roles" and "entities".
Provable Bregman-divergence based Methods for Nonconvex and Non-Lipschitz Problems
Authors: Qiuwei Li, Zhihui Zhu, Gongguo Tang, Michael B. Wakin
Date: 2019-04-22 00:00:00

The (global) Lipschitz smoothness condition is crucial in establishing the
convergence theory for most optimization methods. Unfortunately, most machine
learning and signal processing problems are not Lipschitz smooth. This
motivates us to generalize the concept of Lipschitz smoothness condition to the
relative smoothness condition, which is satisfied by any finite-order
polynomial objective function. Further, this work develops new
Bregman-divergence based algorithms that are guaranteed to converge to a
second-order stationary point for any relatively smooth problem. In addition,
the proposed optimization methods cover both the proximal alternating
minimization and the proximal alternating linearized minimization when we
specialize the Bregman divergence to the Euclidian distance. Therefore, this
work not only develops guaranteed optimization methods for non-Lipschitz smooth
problems but also solves an open problem of showing the second-order
convergence guarantees for these alternating minimization methods.
STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing
Authors: Ming Liu, Yukang Ding, Min Xia, Xiao Liu, Errui Ding, WangMeng Zuo, Shilei Wen
Date: 2019-04-22 00:00:00
Proceeding: CVPR 2019 6
Tasks: Attribute, Decoder, Translation

Arbitrary attribute editing generally can be tackled by incorporating
encoder-decoder and generative adversarial networks. However, the bottleneck
layer in encoder-decoder usually gives rise to blurry and low quality editing
result. And adding skip connections improves image quality at the cost of
weakened attribute manipulation ability. Moreover, existing methods exploit
target attribute vector to guide the flexible translation to desired target
domain. In this work, we suggest to address these issues from selective
transfer perspective. Considering that specific editing task is certainly only
related to the changed attributes instead of all target attributes, our model
selectively takes the difference between target and source attribute vectors as
input. Furthermore, selective transfer units are incorporated with
encoder-decoder to adaptively select and modify encoder feature for enhanced
attribute editing. Experiments show that our method (i.e., STGAN)
simultaneously improves attribute manipulation accuracy as well as perception
quality, and performs favorably against state-of-the-arts in arbitrary facial
attribute editing and season translation.
Compositional generalization in a deep seq2seq model by separating syntax and semantics
Authors: Jake Russin, Jason Jo, Randall C. O'Reilly, Yoshua Bengio
Date: 2019-04-22 00:00:00
Tasks: Deep Learning, Machine Translation, Systematic Generalization, Translation

Standard methods in deep learning for natural language processing fail to capture the compositional structure of human language that allows for systematic generalization outside of the training distribution. However, human learners readily generalize in this way, e.g. by applying known grammatical rules to novel words. Inspired by work in neuroscience suggesting separate brain systems for syntactic and semantic processing, we implement a modification to standard approaches in neural machine translation, imposing an analogous separation. The novel model, which we call Syntactic Attention, substantially outperforms standard methods in deep learning on the SCAN dataset, a compositional generalization task, without any hand-engineered features or additional supervision. Our work suggests that separating syntactic from semantic learning may be a useful heuristic for capturing compositional structure.
Exploring Unsupervised Pretraining and Sentence Structure Modelling for Winograd Schema Challenge
Authors: Yu-Ping Ruan, Xiaodan Zhu, Zhen-Hua Ling, Zhan Shi, Quan Liu, Si Wei
Date: 2019-04-22 00:00:00
Tasks: Common Sense Reasoning, Sentence

Winograd Schema Challenge (WSC) was proposed as an AI-hard problem in testing
computers' intelligence on common sense representation and reasoning. This
paper presents the new state-of-theart on WSC, achieving an accuracy of 71.1%.
We demonstrate that the leading performance benefits from jointly modelling
sentence structures, utilizing knowledge learned from cutting-edge pretraining
models, and performing fine-tuning. We conduct detailed analyses, showing that
fine-tuning is critical for achieving the performance, but it helps more on the
simpler associative problems. Modelling sentence dependency structures,
however, consistently helps on the harder non-associative subset of WSC.
Analysis also shows that larger fine-tuning datasets yield better performances,
suggesting the potential benefit of future work on annotating more Winograd
schema sentences.
Fine-Grained Argument Unit Recognition and Classification
Authors: Dietrich Trautmann, Johannes Daxenberger, Christian Stab, Hinrich Schütze, Iryna Gurevych
Date: 2019-04-22 00:00:00
Tasks: Argument Mining, Argument Retrieval, Classification, General Classification, Retrieval, Sentence, Sentence segmentation

Prior work has commonly defined argument retrieval from heterogeneous document collections as a sentence-level classification task. Consequently, argument retrieval suffers both from low recall and from sentence segmentation errors making it difficult for humans and machines to consume the arguments. In this work, we argue that the task should be performed on a more fine-grained level of sequence labeling. For this, we define the task as Argument Unit Recognition and Classification (AURC). We present a dataset of arguments from heterogeneous sources annotated as spans of tokens within a sentence, as well as with a corresponding stance. We show that and how such difficult argument annotations can be effectively collected through crowdsourcing with high interannotator agreement. The new benchmark, AURC-8, contains up to 15% more arguments per topic as compared to annotations on the sentence level. We identify a number of methods targeted at AURC sequence labeling, achieving close to human performance on known domains. Further analysis also reveals that, contrary to previous approaches, our methods are more robust against sentence segmentation errors. We publicly release our code and the AURC-8 dataset.
Learning Fast Matching Models from Weak Annotations
Authors: Xue Li, Zhipeng Luo, Hao Sun, Jianjin Zhang, Weihao Han, Xianqi Chu, Liangjie Zhang, Qi Zhang
Date: 2019-01-30 00:00:00

This paper proposes a novel training scheme for fast matching models in
Search Ads, which is motivated by the real challenges in model training. The
first challenge stems from the pursuit of high throughput, which prohibits the
deployment of inseparable architectures, and hence greatly limits the model
accuracy. The second problem arises from the heavy dependency on human provided
labels, which are expensive and time-consuming to collect, yet how to leverage
unlabeled search log data is rarely studied. The proposed training framework
targets on mitigating both issues, by treating the stronger but undeployable
models as annotators, and learning a deployable model from both human provided
relevance labels and weakly annotated search log data. Specifically, we first
construct multiple auxiliary tasks from the enumerated relevance labels, and
train the annotators by jointly learning from those related tasks. The
annotation models are then used to assign scores to both labeled and unlabeled
training samples. The deployable model is firstly learnt on the scored
unlabeled data, and then fine-tuned on scored labeled data, by leveraging both
labels and scores via minimizing the proposed label-aware weighted loss.
According to our experiments, compared with the baseline that directly learns
from relevance labels, training by the proposed framework outperforms it by a
large margin, and improves data efficiency substantially by dispensing with 80%
labeled samples. The proposed framework allows us to improve the fast matching
model by learning from stronger annotators while keeping its architecture
unchanged. Meanwhile, our training framework offers a principled manner to
leverage search log data in the training phase, which could effectively
alleviate our dependency on human provided labels.
Structural Self-adaptation for Decentralized Pervasive Intelligence
Authors: Jovan Nikolic, Evangelos Pournaras
Date: 2019-04-21 00:00:00
Tasks: Combinatorial Optimization, Decision Making

Communication structure plays a key role in the learning capability of
decentralized systems. Structural self-adaptation, by means of
self-organization, changes the order as well as the input information of the
agents' collective decision-making. This paper studies the role of agents'
repositioning on the same communication structure, i.e. a tree, as the means to
expand the learning capacity in complex combinatorial optimization problems,
for instance, load-balancing power demand to prevent blackouts or efficient
utilization of bike sharing stations. The optimality of structural
self-adaptations is rigorously studied by constructing a novel large-scale
benchmark that consists of 4000 agents with synthetic and real-world data
performing 4 million structural self-adaptations during which almost 320
billion learning messages are exchanged. Based on this benchmark dataset, 124
deterministic structural criteria, applied as learning meta-features, are
systematically evaluated as well as two online structural self-adaptation
strategies designed to expand learning capacity. Experimental evaluation
identifies metrics that capture agents with influential information and their
optimal positioning. Significant gain in learning performance is observed for
the two strategies especially under low-performing initialization. Strikingly,
the strategy that triggers structural self-adaptation in a more exploratory
fashion is the most cost-effective.