pwc-archive/papers-with-abstracts huggingface dataset:
Multi-Perspective Context Aggregation for Semi-supervised Cloze-style Reading Comprehension
Authors: Liang Wang, Sujian Li, Wei Zhao, Kewei Shen, Meng Sun, Ruoyu Jia, Jingming Liu
Date: 2018-08-20 00:00:00
Proceeding: COLING 2018 8
Tasks: Cloze Test, Natural Language Understanding, Reading Comprehension

Cloze-style reading comprehension has been a popular task for measuring the
progress of natural language understanding in recent years. In this paper, we
design a novel multi-perspective framework, which can be seen as the joint
training of heterogeneous experts and aggregate context information from
different perspectives. Each perspective is modeled by a simple aggregation
module. The outputs of multiple aggregation modules are fed into a one-timestep
pointer network to get the final answer. At the same time, to tackle the
problem of insufficient labeled data, we propose an efficient sampling
mechanism to automatically generate more training examples by matching the
distribution of candidates between labeled and unlabeled data. We conduct our
experiments on a recently released cloze-test dataset CLOTH (Xie et al., 2017),
which consists of nearly 100k questions designed by professional teachers.
Results show that our method achieves new state-of-the-art performance over
previous strong baselines.
Multimodal speech synthesis architecture for unsupervised speaker adaptation
Authors: Hieu-Thi Luong, Junichi Yamagishi
Date: 2018-08-20 00:00:00
Tasks: Speech Synthesis

This paper proposes a new architecture for speaker adaptation of
multi-speaker neural-network speech synthesis systems, in which an unseen
speaker's voice can be built using a relatively small amount of speech data
without transcriptions. This is sometimes called "unsupervised speaker
adaptation". More specifically, we concatenate the layers to the audio inputs
when performing unsupervised speaker adaptation while we concatenate them to
the text inputs when synthesizing speech from text. Two new training schemes
for the new architecture are also proposed in this paper. These training
schemes are not limited to speech synthesis, other applications are suggested.
Experimental results show that the proposed model not only enables adaptation
to unseen speakers using untranscribed speech but it also improves the
performance of multi-speaker modeling and speaker adaptation using transcribed
audio files.
Incremental Learning in Person Re-Identification
Authors: Prajjwal Bhargava
Date: 2018-08-20 00:00:00
Tasks: Incremental Learning, Person Re-Identification

Person Re-Identification is still a challenging task in Computer Vision due to a variety of reasons. On the other side, Incremental Learning is still an issue since deep learning models tend to face the problem of over catastrophic forgetting when trained on subsequent tasks. In this paper, we propose a model that can be used for multiple tasks in Person Re-Identification, provide state-of-the-art results on a variety of tasks and still achieve considerable accuracy subsequently. We evaluated our model on two datasets Market 1501 and Duke MTMC. Extensive experiments show that this method can achieve Incremental Learning in Person ReID efficiently as well as for other tasks in computer vision as well.
Person Re-Identification by Semantic Region Representation and Topology Constraint
Authors: Jianjun Lei, Lijie Niu, Huazhu Fu, Bo Peng, Qingming Huang, Chunping Hou
Date: 2018-08-20 00:00:00
Tasks: Metric Learning, Person Re-Identification

Person re-identification is a popular research topic which aims at matching
the specific person in a multi-camera network automatically. Feature
representation and metric learning are two important issues for person
re-identification. In this paper, we propose a novel person re-identification
method, which consists of a reliable representation called Semantic Region
Representation (SRR), and an effective metric learning with Mapping Space
Topology Constraint (MSTC). The SRR integrates semantic representations to
achieve effective similarity comparison between the corresponding regions via
parsing the body into multiple parts, which focuses on the foreground context
against the background interference. To learn a discriminant metric, the MSTC
is proposed to take into account the topological relationship among all samples
in the feature space. It considers two-fold constraints: the distribution of
positive pairs should be more compact than the average distribution of negative
pairs with regard to the same probe, while the average distance between
different classes should be larger than that between same classes. These two
aspects cooperate to maintain the compactness of the intra-class as well as the
sparsity of the inter-class. Extensive experiments conducted on five
challenging person re-identification datasets, VIPeR, SYSU-sReID, QUML GRID,
CUHK03, and Market-1501, show that the proposed method achieves competitive
performance with the state-of-the-art approaches.
Applying Machine Learning To Maize Traits Prediction
Authors: Binbin Shi, Xupeng Chen
Date: 2018-08-20 00:00:00
Tasks: BIG-bench Machine Learning, Prediction

Heterosis is the improved or increased function of any biological quality in a hybrid offspring. We have studied yet the largest maize SNP dataset for traits prediction. We develop linear and non-linear models which consider relationships between different hybrids as well as other effect. Specially designed model proved to be efficient and robust in prediction maize's traits.
Neural Machine Translation of Text from Non-Native Speakers
Authors: Antonios Anastasopoulos, Alison Lui, Toan Nguyen, David Chiang
Date: 2018-08-19 00:00:00
Proceeding: NAACL 2019 6
Tasks: Machine Translation, NMT, Translation

Neural Machine Translation (NMT) systems are known to degrade when confronted
with noisy data, especially when the system is trained only on clean data. In
this paper, we show that augmenting training data with sentences containing
artificially-introduced grammatical errors can make the system more robust to
such errors. In combination with an automatic grammar error correction system,
we can recover 1.5 BLEU out of 2.4 BLEU lost due to grammatical errors. We also
present a set of Spanish translations of the JFLEG grammar error correction
corpus, which allows for testing NMT robustness to real grammatical errors.
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery
Authors: Grégoire Payen de La Garanderie, Amir Atapour Abarghouei, Toby P. Breckon
Date: 2018-08-19 00:00:00
Proceeding: ECCV 2018
Tasks: 3D Object Detection, Autonomous Vehicles, Depth Estimation, Monocular Depth Estimation, object-detection, Object Detection

Recent automotive vision work has focused almost exclusively on processing
forward-facing cameras. However, future autonomous vehicles will not be viable
without a more comprehensive surround sensing, akin to a human driver, as can
be provided by 360{\deg} panoramic cameras. We present an approach to adapt
contemporary deep network architectures developed on conventional rectilinear
imagery to work on equirectangular 360{\deg} panoramic imagery. To address the
lack of annotated panoramic automotive datasets availability, we adapt a
contemporary automotive dataset, via style and projection transformations, to
facilitate the cross-domain retraining of contemporary algorithms for panoramic
imagery. Following this approach we retrain and adapt existing architectures to
recover scene depth and 3D pose of vehicles from monocular panoramic imagery
without any panoramic training labels or calibration parameters. Our approach
is evaluated qualitatively on crowd-sourced panoramic images and quantitatively
using an automotive environment simulator to provide the first benchmark for
such techniques within panoramic imagery.
Dynamic Temporal Alignment of Speech to Lips
Authors: Tavi Halperin, Ariel Ephrat, Shmuel Peleg
Date: 2018-08-19 00:00:00
Tasks: Constrained Lip-synchronization, Video Alignment

Many speech segments in movies are re-recorded in a studio during
postproduction, to compensate for poor sound quality as recorded on location.
Manual alignment of the newly-recorded speech with the original lip movements
is a tedious task. We present an audio-to-video alignment method for automating
speech to lips alignment, stretching and compressing the audio signal to match
the lip movements. This alignment is based on deep audio-visual features,
mapping the lips video and the speech signal to a shared representation. Using
this shared representation we compute the lip-sync error between every short
speech period and every video frame, followed by the determination of the
optimal corresponding frame for each short sound period over the entire video
clip. We demonstrate successful alignment both quantitatively, using a human
perception-inspired metric, as well as qualitatively. The strongest advantage
of our audio-to-video approach is in cases where the original voice in unclear,
and where a constant shift of the sound can not give a perfect alignment. In
these cases state-of-the-art methods will fail.
XL-NBT: A Cross-lingual Neural Belief Tracking Framework
Authors: Wenhu Chen, Jianshu Chen, Yu Su, Xin Wang, Dong Yu, Xifeng Yan, William Yang Wang
Date: 2018-08-19 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Transfer Learning

Task-oriented dialog systems are becoming pervasive, and many companies
heavily rely on them to complement human agents for customer service in call
centers. With globalization, the need for providing cross-lingual customer
support becomes more urgent than ever. However, cross-lingual support poses
great challenges---it requires a large amount of additional annotated data from
native speakers. In order to bypass the expensive human annotation and achieve
the first step towards the ultimate goal of building a universal dialog system,
we set out to build a cross-lingual state tracking framework. Specifically, we
assume that there exists a source language with dialog belief tracking
annotations while the target languages have no annotated dialog data of any
form. Then, we pre-train a state tracker for the source language as a teacher,
which is able to exploit easy-to-access parallel data. We then distill and
transfer its own knowledge to the student state tracker in target languages. We
specifically discuss two types of common parallel resources: bilingual corpus
and bilingual dictionary, and design different transfer learning strategies
accordingly. Experimentally, we successfully use English state tracker as the
teacher to transfer its knowledge to both Italian and German trackers and
achieve promising results.
Lexicosyntactic Inference in Neural Models
Authors: Aaron Steven White, Rachel Rudinger, Kyle Rawlins, Benjamin Van Durme
Date: 2018-08-19 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Prediction

We investigate neural models' ability to capture lexicosyntactic inferences:
inferences triggered by the interaction of lexical and syntactic information.
We take the task of event factuality prediction as a case study and build a
factuality judgment dataset for all English clause-embedding verbs in various
syntactic contexts. We use this dataset, which we make publicly available, to
probe the behavior of current state-of-the-art neural systems, showing that
these systems make certain systematic errors that are clearly visible through
the lens of factuality prediction.
SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing
Authors: Taku Kudo, John Richardson
Date: 2018-08-19 00:00:00
Proceeding: EMNLP 2018 11
Tasks: Machine Translation, NMT, Segmentation, Translation

This paper describes SentencePiece, a language-independent subword tokenizer
and detokenizer designed for Neural-based text processing, including Neural
Machine Translation. It provides open-source C++ and Python implementations for
subword units. While existing subword segmentation tools assume that the input
is pre-tokenized into word sequences, SentencePiece can train subword models
directly from raw sentences, which allows us to make a purely end-to-end and
language independent system. We perform a validation experiment of NMT on
English-Japanese machine translation, and find that it is possible to achieve
comparable accuracy to direct subword training from raw sentences. We also
compare the performance of subword training and segmentation with various
configurations. SentencePiece is available under the Apache 2 license at
https://github.com/google/sentencepiece.
Jointly Deep Multi-View Learning for Clustering Analysis
Authors: Bingqian Lin, Yuan Xie, Yanyun Qu, Cuihua Li, Xiaodan Liang
Date: 2018-08-19 00:00:00
Tasks: Clustering, Multiview Clustering, MULTI-VIEW LEARNING

In this paper, we propose a novel Joint framework for Deep Multi-view
Clustering (DMJC), where multiple deep embedded features, multi-view fusion
mechanism and clustering assignments can be learned simultaneously. Our key
idea is that the joint learning strategy can sufficiently exploit
clustering-friendly multi-view features and useful multi-view complementary
information to improve the clustering performance. How to realize the
multi-view fusion in such a joint framework is the primary challenge. To do so,
we design two ingenious variants of deep multi-view joint clustering models
under the proposed framework, where multi-view fusion is implemented by two
different schemes. The first model, called DMJC-S, performs multi-view fusion
in an implicit way via a novel multi-view soft assignment distribution. The
second model, termed DMJC-T, defines a novel multi-view auxiliary target
distribution to conduct the multi-view fusion explicitly. Both DMJC-S and
DMJC-T are optimized under a KL divergence like clustering objective.
Experiments on six challenging image datasets demonstrate the superiority of
both DMJC-S and DMJC-T over single/multi-view baselines and the
state-of-the-art multiview clustering methods, which proves the effectiveness
of the proposed DMJC framework. To our best knowledge, this is the first work
to model the multi-view clustering in a deep joint framework, which will
provide a meaningful thinking in unsupervised multi-view learning.
Automatic Detection of Vague Words and Sentences in Privacy Policies
Authors: Logan Lebanoff, Fei Liu
Date: 2018-08-19 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Sentence

Website privacy policies represent the single most important source of
information for users to gauge how their personal data are collected, used and
shared by companies. However, privacy policies are often vague and people
struggle to understand the content. Their opaqueness poses a significant
challenge to both users and policy regulators. In this paper, we seek to
identify vague content in privacy policies. We construct the first corpus of
human-annotated vague words and sentences and present empirical studies on
automatic vagueness detection. In particular, we investigate context-aware and
context-agnostic models for predicting vague words, and explore
auxiliary-classifier generative adversarial networks for characterizing
sentence vagueness. Our experimental results demonstrate the effectiveness of
proposed approaches. Finally, we provide suggestions for resolving vagueness
and improving the usability of privacy policies.
Adapting the Neural Encoder-Decoder Framework from Single to Multi-Document Summarization
Authors: Logan Lebanoff, Kaiqiang Song, Fei Liu
Date: 2018-08-19 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Document Summarization, Multi-Document Summarization

Generating a text abstract from a set of documents remains a challenging
task. The neural encoder-decoder framework has recently been exploited to
summarize single documents, but its success can in part be attributed to the
availability of large parallel data automatically acquired from the Web. In
contrast, parallel data for multi-document summarization are scarce and costly
to obtain. There is a pressing need to adapt an encoder-decoder model trained
on single-document summarization data to work with multiple-document input. In
this paper, we present an initial investigation into a novel adaptation method.
It exploits the maximal marginal relevance method to select representative
sentences from multi-document input, and leverages an abstractive
encoder-decoder model to fuse disparate sentences to an abstractive summary.
The adaptation method is robust and itself requires no training data. Our
system compares favorably to state-of-the-art extractive and abstractive
approaches judged by automatic metrics and human assessors.
Let CONAN tell you a story: Procedural quest generation
Authors: Vincent Breault, Sebastien Ouellet, Jim Davies
Date: 2018-08-19 00:00:00
Tasks: Story Generation

This work proposes an engine for the Creation Of Novel Adventure Narrative
(CONAN), which is a procedural quest generator. It uses a planning approach to
story generation. The engine is tested on its ability to create quests, which
are sets of actions that must be performed in order to achieve a certain goal,
usually for a reward. The engine takes in a world description represented as a
set of facts, including characters, locations, and items, and generates quests
according to the state of the world and the preferences of the characters. We
evaluate quests through the classification of the motivations behind the
quests, based on the sequences of actions required to complete the quests. We
also compare different world descriptions and analyze the difference in
motivations for the quests produced by the engine. Compared against human
structural quest analysis, the current engine was found to be able to replicate
the quest structures found in commercial video game quests.
GridFace: Face Rectification via Learning Local Homography Transformations
Authors: Erjin Zhou, Zhimin Cao, Jian Sun
Date: 2018-08-19 00:00:00
Proceeding: ECCV 2018 9
Tasks: Face Recognition, Image Generation

In this paper, we propose a method, called GridFace, to reduce facial
geometric variations and improve the recognition performance. Our method
rectifies the face by local homography transformations, which are estimated by
a face rectification network. To encourage the image generation with canonical
views, we apply a regularization based on the natural face distribution. We
learn the rectification network and recognition network in an end-to-end
manner. Extensive experiments show our method greatly reduces geometric
variations, and gains significant improvements in unconstrained face
recognition scenarios.
Haze Density Estimation via Modeling of Scattering Coefficients of Iso-depth Regions
Authors: Jie Chen, Cheen-Hau Tan, Lap-Pui Chau
Date: 2018-08-19 00:00:00
Tasks: Density Estimation

Vision based haze density estimation is of practical implications for the
purpose of precaution alarm and emergency reactions toward disastrous hazy
weathers. In this paper, we introduce a haze density estimation framework based
on modeling of scattering coefficients of iso-depth regions. A haze density
metric of Normalized Scattering Coefficient (NSC) is proposed to measure
current haze density level with reference to two reference scales. Iso-depth
regions are determined via superpixel segmentation. Efficient searching and
matching of iso-depth units could be carried out for measurements via
unstationary cameras. A robust dark SP selection method is used to produce
reliable predictions for most out-door scenarios.
TLR: Transfer Latent Representation for Unsupervised Domain Adaptation
Authors: Pan Xiao, Bo Du, Jia Wu, Lefei Zhang, Ruimin Hu, Xuelong. Li
Date: 2018-08-19 00:00:00
Tasks: Decoder, Domain Adaptation, Unsupervised Domain Adaptation

Domain adaptation refers to the process of learning prediction models in a
target domain by making use of data from a source domain. Many classic methods
solve the domain adaptation problem by establishing a common latent space,
which may cause the loss of many important properties across both domains. In
this manuscript, we develop a novel method, transfer latent representation
(TLR), to learn a better latent space. Specifically, we design an objective
function based on a simple linear autoencoder to derive the latent
representations of both domains. The encoder in the autoencoder aims to project
the data of both domains into a robust latent space. Besides, the decoder
imposes an additional constraint to reconstruct the original data, which can
preserve the common properties of both domains and reduce the noise that causes
domain shift. Experiments on cross-domain tasks demonstrate the advantages of
TLR over competing methods.
Fast and Robust Matching for Multimodal Remote Sensing Image Registration
Authors: Yuanxin Ye, Lorenzo Bruzzone, Jie Shan, Francesca Bovolo, Qing Zhu
Date: 2018-08-19 00:00:00
Tasks: Computational Efficiency, Image Registration, Template Matching

While image registration has been studied in remote sensing community for decades, registering multimodal data [e.g., optical, LiDAR, SAR, and map] remains a challenging problem because of significant nonlinear intensity differences between such data. To address this problem, this paper presents a fast and robust matching framework integrating local descriptors for multimodal registration. In the proposed framework, a local descriptor, such as Histogram of Oriented Gradient (HOG), Local Self Similarity (LSS), or Speeded-Up Robust Feature (SURF), is first extracted at each pixel to form a pixel-wise feature representation of an image. Then we define a similarity measure based on the feature representation in frequency domain using the 3 Dimensional Fast Fourier Transform (3DFFT) technique, followed by a template matching scheme to detect control points between images. In this procedure, we also propose a novel pixel-wise feature representation using orientated gradients of images, which is named channel features of orientated gradients (CFOG). This novel feature is an extension of the pixel-wise HOG descriptors, and outperforms that both in matching performance and computational efficiency. The major advantage of the proposed framework includes: (1) structural similarity representation using the pixel-wise feature description and (2) high computational efficiency due to the use of 3DFFT. Experimental results on different types of multimodal images show the superior matching performance of the proposed framework than the state-of-the-art methods.The proposed matching framework have been used in the software products of a Chinese listed company. The matlab code is available in this manuscript.