pwc-archive/papers-with-abstracts huggingface dataset:
Multi-lingual Dialogue Act Recognition with Deep Learning Methods
Authors: Jiří Martínek, Pavel Král, Ladislav Lenc, Christophe Cerisara
Date: 2019-04-11 00:00:00
Tasks: Deep Learning

This paper deals with multi-lingual dialogue act (DA) recognition. The
proposed approaches are based on deep neural networks and use word2vec
embeddings for word representation. Two multi-lingual models are proposed for
this task. The first approach uses one general model trained on the embeddings
from all available languages. The second method trains the model on a single
pivot language and a linear transformation method is used to project other
languages onto the pivot language. The popular convolutional neural network and
LSTM architectures with different set-ups are used as classifiers. To the best
of our knowledge this is the first attempt at multi-lingual DA recognition
using neural networks. The multi-lingual models are validated experimentally on
two languages from the Verbmobil corpus.
Ontologies-based Architecture for Sociocultural Knowledge Co-Construction Systems
Authors: Guidedi Kaladzavi, Papa Fary Diallo, Cedric Béré, Olivier Corby, Isabelle Mirbel, Moussa Lo, Dina Taiwe Kolyang
Date: 2019-04-11 00:00:00

Considering the evolution of the semantic wiki engine based platforms, two
main approaches could be distinguished: Ontologies for Wikis (OfW) and Wikis
for Ontologies (WfO). OfW vision requires existing ontologies to be imported.
Most of them use the RDF-based (Resource Description Framework) systems in
conjunction with the standard SQL (Structured Query Language) database to
manage and query semantic data. But, relational database is not an ideal type
of storage for semantic data. A more natural data model for SMW (Semantic
MediaWiki) is RDF, a data format that organizes information in graphs rather
than in fixed database tables. This paper presents an ontology based
architecture, which aims to implement this idea. The architecture mainly
includes three layered functional architectures: Web User Interface Layer,
Semantic Layer and Persistence Layer.
Modeling the Complexity and Descriptive Adequacy of Construction Grammars
Authors: Jonathan Dunn
Date: 2019-04-11 00:00:00
Proceeding: WS 2018 1
Tasks: Descriptive

This paper uses the Minimum Description Length paradigm to model the
complexity of CxGs (operationalized as the encoding size of a grammar)
alongside their descriptive adequacy (operationalized as the encoding size of a
corpus given a grammar). These two quantities are combined to measure the
quality of potential CxGs against unannotated corpora, supporting
discovery-device CxGs for English, Spanish, French, German, and Italian. The
results show (i) that these grammars provide significant generalizations as
measured using compression and (ii) that more complex CxGs with access to
multiple levels of representation provide greater generalizations than
single-representation CxGs.
Black-Box Decision based Adversarial Attack with Symmetric $α$-stable Distribution
Authors: Vignesh Srinivasan, Ercan E. Kuruoglu, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima
Date: 2019-04-11 00:00:00
Tasks: Adversarial Attack

Developing techniques for adversarial attack and defense is an important
research field for establishing reliable machine learning and its applications.
Many existing methods employ Gaussian random variables for exploring the data
space to find the most adversarial (for attacking) or least adversarial (for
defense) point. However, the Gaussian distribution is not necessarily the
optimal choice when the exploration is required to follow the complicated
structure that most real-world data distributions exhibit. In this paper, we
investigate how statistics of random variables affect such random walk
exploration. Specifically, we generalize the Boundary Attack, a
state-of-the-art black-box decision based attacking strategy, and propose the
L\'evy-Attack, where the random walk is driven by symmetric $\alpha$-stable
random variables. Our experiments on MNIST and CIFAR10 datasets show that the
L\'evy-Attack explores the image data space more efficiently, and significantly
improves the performance. Our results also give an insight into the recently
found fact in the whitebox attacking scenario that the choice of the norm for
measuring the amplitude of the adversarial patterns is essential.
Gating Mechanisms for Combining Character and Word-level Word Representations: An Empirical Study
Authors: Jorge A. Balazs, Yutaka Matsuo
Date: 2019-04-11 00:00:00
Proceeding: NAACL 2019 6
Tasks: Semantic Similarity, Semantic Textual Similarity, Sentence, Word Similarity

In this paper we study how different ways of combining character and
word-level representations affect the quality of both final word and sentence
representations. We provide strong empirical evidence that modeling characters
improves the learned representations at the word and sentence levels, and that
doing so is particularly useful when representing less frequent words. We
further show that a feature-wise sigmoid gating mechanism is a robust method
for creating representations that encode semantic similarity, as it performed
reasonably well in several word similarity datasets. Finally, our findings
suggest that properly capturing semantic similarity at the word level does not
consistently yield improved performance in downstream sentence-level tasks. Our
code is available at https://github.com/jabalazs/gating
Recurrent Space-time Graph Neural Networks
Authors: Andrei Nicolicioiu, Iulia Duta, Marius Leordeanu
Date: 2019-04-11 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Action Recognition, Human-Object Interaction Detection, Video Understanding

Learning in the space-time domain remains a very challenging problem in machine learning and computer vision. Current computational models for understanding spatio-temporal visual data are heavily rooted in the classical single-image based paradigm. It is not yet well understood how to integrate information in space and time into a single, general model. We propose a neural graph model, recurrent in space and time, suitable for capturing both the local appearance and the complex higher-level interactions of different entities and objects within the changing world scene. Nodes and edges in our graph have dedicated neural networks for processing information. Nodes operate over features extracted from local parts in space and time and previous memory states. Edges process messages between connected nodes at different locations and spatial scales or between past and present time. Messages are passed iteratively in order to transmit information globally and establish long range interactions. Our model is general and could learn to recognize a variety of high level spatio-temporal concepts and be applied to different learning tasks. We demonstrate, through extensive experiments and ablation studies, that our model outperforms strong baselines and top published methods on recognizing complex activities in video. Moreover, we obtain state-of-the-art performance on the challenging Something-Something human-object interaction dataset.
FRNET: Flattened Residual Network for Infant MRI Skull Stripping
Authors: Qian Zhang, Li Wang, Xiaopeng Zong, Weili Lin, Gang Li, Dinggang Shen
Date: 2019-04-11 00:00:00
Tasks: Data Augmentation, Skull Stripping

Skull stripping for brain MR images is a basic segmentation task. Although
many methods have been proposed, most of them focused mainly on the adult MR
images. Skull stripping for infant MR images is more challenging due to the
small size and dynamic intensity changes of brain tissues during the early
ages. In this paper, we propose a novel CNN based framework to robustly extract
brain region from infant MR image without any human assistance. Specifically,
we propose a simplified but more robust flattened residual network architecture
(FRnet). We also introduce a new boundary loss function to highlight ambiguous
and low contrast regions between brain and non-brain regions. To make the whole
framework more robust to MR images with different imaging quality, we further
introduce an artifact simulator for data augmentation. We have trained and
tested our proposed framework on a large dataset (N=343), covering newborns to
48-month-olds, and obtained performance better than the state-of-the-art
methods in all age groups.
STC Antispoofing Systems for the ASVspoof2019 Challenge
Authors: Galina Lavrentyeva, Sergey Novoselov, Andzhukaev Tseren, Marina Volkova, Artem Gorlanov, Alexandr Kozlov
Date: 2019-04-11 00:00:00
Tasks: Speech Synthesis, Voice Conversion

This paper describes the Speech Technology Center (STC) antispoofing systems
submitted to the ASVspoof 2019 challenge. The ASVspoof2019 is the extended
version of the previous challenges and includes 2 evaluation conditions:
logical access use-case scenario with speech synthesis and voice conversion
attack types and physical access use-case scenario with replay attacks. During
the challenge we developed anti-spoofing solutions for both scenarios. The
proposed systems are implemented using deep learning approach and are based on
different types of acoustic features. We enhanced Light CNN architecture
previously considered by the authors for replay attacks detection and which
performed high spoofing detection quality during the ASVspoof2017 challenge. In
particular here we investigate the efficiency of angular margin based softmax
activation for training robust deep Light CNN classifier to solve the
mentioned-above tasks. Submitted systems achieved EER of 1.86% in logical
access scenario and 0.54% in physical access scenario on the evaluation part of
the Challenge corpora. High performance obtained for the unknown types of
spoofing attacks demonstrates the stability of the offered approach in both
evaluation conditions.
A high quality and phonetic balanced speech corpus for Vietnamese
Authors: Pham Ngoc Phuong, Quoc Truong Do, Luong Chi Mai
Date: 2019-04-11 00:00:00
Tasks: Speech Synthesis, Vocal Bursts Intensity Prediction

This paper presents a high quality Vietnamese speech corpus that can be used
for analyzing Vietnamese speech characteristic as well as building speech
synthesis models. The corpus consists of 5400 clean-speech utterances spoken by
12 speakers including 6 males and 6 females. The corpus is designed with
phonetic balanced in mind so that it can be used for speech synthesis,
especially, speech adaptation approaches. Specifically, all speakers utter a
common dataset contains 250 phonetic balanced sentences. To increase the
variety of speech context, each speaker also utters another 200 non-shared,
phonetic-balanced sentences. The speakers are selected to cover a wide range of
age and come from different regions of the North of Vietnam. The audios are
recorded in a soundproof studio room, they are sampling at 48 kHz, 16 bits PCM,
mono channel.
3D Dense Face Alignment via Graph Convolution Networks
Authors: Huawei Wei, Shuang Liang, Yichen Wei
Date: 2019-04-11 00:00:00
Tasks: 3D Face Reconstruction, Face Alignment, Face Reconstruction

Recently, 3D face reconstruction and face alignment tasks are gradually
combined into one task: 3D dense face alignment. Its goal is to reconstruct the
3D geometric structure of face with pose information. In this paper, we propose
a graph convolution network to regress 3D face coordinates. Our method directly
performs feature learning on the 3D face mesh, where the geometric structure
and details are well preserved. Extensive experiments show that our approach
gains superior performance over state-of-the-art methods on several challenging
datasets.
Searching News Articles Using an Event Knowledge Graph Leveraged by Wikidata
Authors: Charlotte Rudnik, Thibault Ehrhart, Olivier Ferret, Denis Teyssou, Raphaël Troncy, Xavier Tannier
Date: 2019-04-11 00:00:00
Tasks: Articles

News agencies produce thousands of multimedia stories describing events
happening in the world that are either scheduled such as sports competitions,
political summits and elections, or breaking events such as military conflicts,
terrorist attacks, natural disasters, etc. When writing up those stories,
journalists refer to contextual background and to compare with past similar
events. However, searching for precise facts described in stories is hard. In
this paper, we propose a general method that leverages the Wikidata knowledge
base to produce semantic annotations of news articles. Next, we describe a
semantic search engine that supports both keyword based search in news articles
and structured data search providing filters for properties belonging to
specific event schemas that are automatically inferred.
Reasoning Visual Dialogs with Structural and Partial Observations
Authors: Zilong Zheng, Wenguan Wang, Siyuan Qi, Song-Chun Zhu
Date: 2019-04-11 00:00:00
Proceeding: CVPR 2019 6
Tasks: Graph Neural Network, Visual Dialog

We propose a novel model to address the task of Visual Dialog which exhibits complex dialog structures. To obtain a reasonable answer based on the current question and the dialog history, the underlying semantic dependencies between dialog entities are essential. In this paper, we explicitly formalize this task as inference in a graphical model with partially observed nodes and unknown graph structures (relations in dialog). The given dialog entities are viewed as the observed nodes. The answer to a given question is represented by a node with missing value. We first introduce an Expectation Maximization algorithm to infer both the underlying dialog structures and the missing node values (desired answers). Based on this, we proceed to propose a differentiable graph neural network (GNN) solution that approximates this process. Experiment results on the VisDial and VisDial-Q datasets show that our model outperforms comparative methods. It is also observed that our method can infer the underlying dialog structure for better dialog reasoning.
Generating Multiple Hypotheses for 3D Human Pose Estimation with Mixture Density Network
Authors: Chen Li, Gim Hee Lee
Date: 2019-04-11 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Human Pose Estimation, Monocular 3D Human Pose Estimation, Multi-Hypotheses 3D Human Pose Estimation, Pose Estimation

3D human pose estimation from a monocular image or 2D joints is an ill-posed
problem because of depth ambiguity and occluded joints. We argue that 3D human
pose estimation from a monocular input is an inverse problem where multiple
feasible solutions can exist. In this paper, we propose a novel approach to
generate multiple feasible hypotheses of the 3D pose from 2D joints.In contrast
to existing deep learning approaches which minimize a mean square error based
on an unimodal Gaussian distribution, our method is able to generate multiple
feasible hypotheses of 3D pose based on a multimodal mixture density networks.
Our experiments show that the 3D poses estimated by our approach from an input
of 2D joints are consistent in 2D reprojections, which supports our argument
that multiple solutions exist for the 2D-to-3D inverse problem. Furthermore, we
show state-of-the-art performance on the Human3.6M dataset in both best
hypothesis and multi-view settings, and we demonstrate the generalization
capacity of our model by testing on the MPII and MPI-INF-3DHP datasets. Our
code is available at the project website.
FrameRank: A Text Processing Approach to Video Summarization
Authors: Zhuo Lei, Chao Zhang, Qian Zhang, Guoping Qiu
Date: 2019-04-11 00:00:00
Tasks: Unsupervised Video Summarization, Video Summarization

Video summarization has been extensively studied in the past decades.
However, user-generated video summarization is much less explored since there
lack large-scale video datasets within which human-generated video summaries
are unambiguously defined and annotated. Toward this end, we propose a
user-generated video summarization dataset - UGSum52 - that consists of 52
videos (207 minutes). In constructing the dataset, because of the subjectivity
of user-generated video summarization, we manually annotate 25 summaries for
each video, which are in total 1300 summaries. To the best of our knowledge, it
is currently the largest dataset for user-generated video summarization.
  Based on this dataset, we present FrameRank, an unsupervised video
summarization method that employs a frame-to-frame level affinity graph to
identify coherent and informative frames to summarize a video. We use the
Kullback-Leibler(KL)-divergence-based graph to rank temporal segments according
to the amount of semantic information contained in their frames. We illustrate
the effectiveness of our method by applying it to three datasets SumMe, TVSum
and UGSum52 and show it achieves state-of-the-art results.
Scalable Cross-Lingual Transfer of Neural Sentence Embeddings
Authors: Hanan Aldarmaki, Mona Diab
Date: 2019-04-11 00:00:00
Proceeding: SEMEVAL 2019 6
Tasks: Cross-Lingual Transfer, Decoder, Sentence, Sentence Embedding, Sentence-Embedding, Sentence Embeddings, Transfer Learning

We develop and investigate several cross-lingual alignment approaches for
neural sentence embedding models, such as the supervised inference classifier,
InferSent, and sequential encoder-decoder models. We evaluate three alignment
frameworks applied to these models: joint modeling, representation transfer
learning, and sentence mapping, using parallel text to guide the alignment. Our
results support representation transfer as a scalable approach for modular
cross-lingual alignment of neural sentence embeddings, where we observe better
performance compared to joint models in intrinsic and extrinsic evaluations,
particularly with smaller sets of parallel data.
Improvisation through Physical Understanding: Using Novel Objects as Tools with Visual Foresight
Authors: Annie Xie, Frederik Ebert, Sergey Levine, Chelsea Finn
Date: 2019-04-11 00:00:00
Tasks: Imitation Learning, Self-Supervised Learning

Machine learning techniques have enabled robots to learn narrow, yet complex
tasks and also perform broad, yet simple skills with a wide variety of objects.
However, learning a model that can both perform complex tasks and generalize to
previously unseen objects and goals remains a significant challenge. We study
this challenge in the context of "improvisational" tool use: a robot is
presented with novel objects and a user-specified goal (e.g., sweep some
clutter into the dustpan), and must figure out, using only raw image
observations, how to accomplish the goal using the available objects as tools.
We approach this problem by training a model with both a visual and physical
understanding of multi-object interactions, and develop a sampling-based
optimizer that can leverage these interactions to accomplish tasks. We do so by
combining diverse demonstration data with self-supervised interaction data,
aiming to leverage the interaction data to build generalizable models and the
demonstration data to guide the model-based RL planner to solve complex tasks.
Our experiments show that our approach can solve a variety of complex tool use
tasks from raw pixel inputs, outperforming both imitation learning and
self-supervised learning individually. Furthermore, we show that the robot can
perceive and use novel objects as tools, including objects that are not
conventional tools, while also choosing dynamically to use or not use tools
depending on whether or not they are required.
Direct Fitting of Gaussian Mixture Models
Authors: Leonid Keselman, Martial Hebert
Date: 2019-04-11 00:00:00
Tasks: 3D geometry

When fitting Gaussian Mixture Models to 3D geometry, the model is typically fit to point clouds, even when the shapes were obtained as 3D meshes. Here we present a formulation for fitting Gaussian Mixture Models (GMMs) directly to a triangular mesh instead of using points sampled from its surface. Part of this work analyzes a general formulation for evaluating likelihood of geometric objects. This modification enables fitting higher-quality GMMs under a wider range of initialization conditions. Additionally, models obtained from this fitting method are shown to produce an improvement in 3D registration for both meshes and RGB-D frames. This result is general and applicable to arbitrary geometric objects, including representing uncertainty from sensor measurements.
Recurrent Event Network: Autoregressive Structure Inference over Temporal Knowledge Graphs
Authors: Woojeong Jin, Meng Qu, Xisen Jin, Xiang Ren
Date: 2019-04-11 00:00:00
Tasks: Knowledge Graphs, Link Prediction, Temporal Sequences

Knowledge graph reasoning is a critical task in natural language processing. The task becomes more challenging on temporal knowledge graphs, where each fact is associated with a timestamp. Most existing methods focus on reasoning at past timestamps and they are not able to predict facts happening in the future. This paper proposes Recurrent Event Network (RE-NET), a novel autoregressive architecture for predicting future interactions. The occurrence of a fact (event) is modeled as a probability distribution conditioned on temporal sequences of past knowledge graphs. Specifically, our RE-NET employs a recurrent event encoder to encode past facts and uses a neighborhood aggregator to model the connection of facts at the same timestamp. Future facts can then be inferred in a sequential manner based on the two modules. We evaluate our proposed method via link prediction at future times on five public datasets. Through extensive experiments, we demonstrate the strength of RENET, especially on multi-step inference over future timestamps, and achieve state-of-the-art performance on all five datasets. Code and data can be found at https://github.com/INK-USC/RE-Net.
Frequency vs. Association for Constraint Selection in Usage-Based Construction Grammar
Authors: Jonathan Dunn
Date: 2019-04-11 00:00:00
Proceeding: WS 2019 6

A usage-based Construction Grammar (CxG) posits that slot-constraints
generalize from common exemplar constructions. But what is the best model of
constraint generalization? This paper evaluates competing frequency-based and
association-based models across eight languages using a metric derived from the
Minimum Description Length paradigm. The experiments show that
association-based models produce better generalizations across all languages by
a significant margin.
Modeling Global Syntactic Variation in English Using Dialect Classification
Authors: Jonathan Dunn
Date: 2019-04-11 00:00:00
Proceeding: WS 2019 6
Tasks: Classification, Dialect Identification, General Classification

This paper evaluates global-scale dialect identification for 14 national
varieties of English as a means for studying syntactic variation. The paper
makes three main contributions: (i) introducing data-driven language mapping as
a method for selecting the inventory of national varieties to include in the
task; (ii) producing a large and dynamic set of syntactic features using
grammar induction rather than focusing on a few hand-selected features such as
function words; and (iii) comparing models across both web corpora and social
media corpora in order to measure the robustness of syntactic variation across
registers.
UniVSE: Robust Visual Semantic Embeddings via Structured Semantic Representations
Authors: Hao Wu, Jiayuan Mao, Yufeng Zhang, Yuning Jiang, Lei LI, Weiwei Sun, Wei-Ying Ma
Date: 2019-04-11 00:00:00
Tasks: Contrastive Learning, Cross-Modal Retrieval, Retrieval, Sentence

We propose Unified Visual-Semantic Embeddings (UniVSE) for learning a joint
space of visual and textual concepts. The space unifies the concepts at
different levels, including objects, attributes, relations, and full scenes. A
contrastive learning approach is proposed for the fine-grained alignment from
only image-caption pairs. Moreover, we present an effective approach for
enforcing the coverage of semantic components that appear in the sentence. We
demonstrate the robustness of Unified VSE in defending text-domain adversarial
attacks on cross-modal retrieval tasks. Such robustness also empowers the use
of visual cues to resolve word dependencies in novel sentences.