pwc-archive/papers-with-abstracts huggingface dataset:
Improv Chat: Second Response Generation for Chatbot
Authors: Furu Wei
Date: 2018-05-10 00:00:00
Tasks: Chatbot, Response Generation, Retrieval, Sentence

Existing research on response generation for chatbot focuses on \textbf{First
Response Generation} which aims to teach the chatbot to say the first response
(e.g. a sentence) appropriate to the conversation context (e.g. the user's
query). In this paper, we introduce a new task \textbf{Second Response
Generation}, termed as Improv chat, which aims to teach the chatbot to say the
second response after saying the first response with respect the conversation
context, so as to lighten the burden on the user to keep the conversation
going. Specifically, we propose a general learning based framework and develop
a retrieval based system which can generate the second responses with the
users' query and the chatbot's first response as input. We present the approach
to building the conversation corpus for Improv chat from public forums and
social networks, as well as the neural networks based models for response
matching and ranking. We include the preliminary experiments and results in
this paper. This work could be further advanced with better deep matching
models for retrieval base systems or generative models for generation based
systems as well as extensive evaluations in real-life applications.
ANNETT-O: An Ontology for Describing Artificial Neural Network Evaluation, Topology and Training
Authors: Iraklis A. Klampanos, Athanasios Davvetas, Antonis Koukourikos, Vangelis Karkaletsis
Date: 2018-04-07 00:00:00
Tasks: Deep Learning

Deep learning models, while effective and versatile, are becoming
increasingly complex, often including multiple overlapping networks of
arbitrary depths, multiple objectives and non-intuitive training methodologies.
This makes it increasingly difficult for researchers and practitioners to
design, train and understand them. In this paper we present ANNETT-O, a
much-needed, generic and computer-actionable vocabulary for researchers and
practitioners to describe their deep learning configurations, training
procedures and experiments. The proposed ontology focuses on topological,
training and evaluation aspects of complex deep neural configurations, while
keeping peripheral entities more succinct. Knowledge bases implementing
ANNETT-O can support a wide variety of queries, providing relevant insights to
users. In addition to a detailed description of the ontology, we demonstrate
its suitability to the task via a number of hypothetical use-cases of
increasing complexity.
Dealing with sequences in the RGBDT space
Authors: Gabriel Moyà, Antoni Jaume-i-Capó, Javier Varona
Date: 2018-05-10 00:00:00

Most of the current research in computer vision is focused on working with
single images without taking in account temporal information. We present a
probabilistic non-parametric model that mixes multiple information cues from
devices to segment regions that contain moving objects in image sequences. We
prepared an experimental setup to show the importance of using previous
information for obtaining an accurate segmentation result, using a novel
dataset that provides sequences in the RGBDT space. We label the detected
regions ts with a state-of-the-art human detector. Each one of the detected
regions is at least marked as human once.
Detecting Heads using Feature Refine Net and Cascaded Multi-Scale Architecture
Authors: Dezhi Peng, Zikai Sun, Zirong Chen, Zirui Cai, Lele Xie, Lianwen Jin
Date: 2018-03-25 00:00:00
Tasks: Head Detection

This paper presents a method that can accurately detect heads especially
small heads under the indoor scene. To achieve this, we propose a novel method,
Feature Refine Net (FRN), and a cascaded multi-scale architecture. FRN exploits
the multi-scale hierarchical features created by deep convolutional neural
networks. The proposed channel weighting method enables FRN to make use of
features alternatively and effectively. To improve the performance of small
head detection, we propose a cascaded multi-scale architecture which has two
detectors. One called global detector is responsible for detecting large
objects and acquiring the global distribution information. The other called
local detector is designed for small objects detection and makes use of the
information provided by global detector. Due to the lack of head detection
datasets, we have collected and labeled a new large dataset named SCUT-HEAD
which includes 4405 images with 111251 heads annotated. Experiments show that
our method has achieved state-of-the-art performance on SCUT-HEAD.
Scaling associative classification for very large datasets
Authors: Luca Venturini, Elena Baralis, Paolo Garza
Date: 2018-05-10 00:00:00
Tasks: Classification, Ensemble Learning, General Classification

Supervised learning algorithms are nowadays successfully scaling up to
datasets that are very large in volume, leveraging the potential of in-memory
cluster-computing Big Data frameworks. Still, massive datasets with a number of
large-domain categorical features are a difficult challenge for any classifier.
Most off-the-shelf solutions cannot cope with this problem. In this work we
introduce DAC, a Distributed Associative Classifier. DAC exploits ensemble
learning to distribute the training of an associative classifier among parallel
workers and improve the final quality of the model. Furthermore, it adopts
several novel techniques to reach high scalability without sacrificing quality,
among which a preventive pruning of classification rules in the extraction
phase based on Gini impurity. We ran experiments on Apache Spark, on a real
large-scale dataset with more than 4 billion records and 800 million distinct
categories. The results showed that DAC improves on a state-of-the-art solution
in both prediction quality and execution time. Since the generated model is
human-readable, it can not only classify new records, but also allow
understanding both the logic behind the prediction and the properties of the
model, becoming a useful aid for decision makers.
Effect of dilution in asymmetric recurrent neural networks
Authors: Viola Folli, Giorgio Gosti, Marco Leonetti, Giancarlo Ruocco
Date: 2018-05-10 00:00:00

We study with numerical simulation the possible limit behaviors of
synchronous discrete-time deterministic recurrent neural networks composed of N
binary neurons as a function of a network's level of dilution and asymmetry.
The network dilution measures the fraction of neuron couples that are
connected, and the network asymmetry measures to what extent the underlying
connectivity matrix is asymmetric. For each given neural network, we study the
dynamical evolution of all the different initial conditions, thus
characterizing the full dynamical landscape without imposing any learning rule.
Because of the deterministic dynamics, each trajectory converges to an
attractor, that can be either a fixed point or a limit cycle. These attractors
form the set of all the possible limit behaviors of the neural network. For
each network, we then determine the convergence times, the limit cycles'
length, the number of attractors, and the sizes of the attractors' basin. We
show that there are two network structures that maximize the number of possible
limit behaviors. The first optimal network structure is fully-connected and
symmetric. On the contrary, the second optimal network structure is highly
sparse and asymmetric. The latter optimal is similar to what observed in
different biological neuronal circuits. These observations lead us to
hypothesize that independently from any given learning model, an efficient and
effective biologic network that stores a number of limit behaviors close to its
maximum capacity tends to develop a connectivity structure similar to one of
the optimal networks we found.
Learning Robust Search Strategies Using a Bandit-Based Approach
Authors: Wei Xia, Roland H. C. Yap
Date: 2018-05-10 00:00:00

Effective solving of constraint problems often requires choosing good or
specific search heuristics. However, choosing or designing a good search
heuristic is non-trivial and is often a manual process. In this paper, rather
than manually choosing/designing search heuristics, we propose the use of
bandit-based learning techniques to automatically select search heuristics. Our
approach is online where the solver learns and selects from a set of heuristics
during search. The goal is to obtain automatic search heuristics which give
robust performance. Preliminary experiments show that our adaptive technique is
more robust than the original search heuristics. It can also outperform the
original heuristics.
Obligation and Prohibition Extraction Using Hierarchical RNNs
Authors: Ilias Chalkidis, Ion Androutsopoulos, Achilleas Michos
Date: 2018-05-10 00:00:00
Proceeding: ACL 2018 7
Tasks: Sentence, Sentence Embeddings

We consider the task of detecting contractual obligations and prohibitions.
We show that a self-attention mechanism improves the performance of a BILSTM
classifier, the previous state of the art for this task, by allowing it to
focus on indicative tokens. We also introduce a hierarchical BILSTM, which
converts each sentence to an embedding, and processes the sentence embeddings
to classify each sentence. Apart from being faster to train, the hierarchical
BILSTM outperforms the flat one, even when the latter considers surrounding
sentences, because the hierarchical model has a broader discourse view.
Deep Covariance Descriptors for Facial Expression Recognition
Authors: Naima Otberdout, Anis Kacem, Mohamed Daoudi, Lahoucine Ballihi, Stefano Berretti
Date: 2018-05-10 00:00:00
Tasks: Classification, Facial Expression Recognition, Facial Expression Recognition (FER), General Classification

In this paper, covariance matrices are exploited to encode the deep
convolutional neural networks (DCNN) features for facial expression
recognition. The space geometry of the covariance matrices is that of Symmetric
Positive Definite (SPD) matrices. By performing the classification of the
facial expressions using Gaussian kernel on SPD manifold, we show that the
covariance descriptors computed on DCNN features are more efficient than the
standard classification with fully connected layers and softmax. By
implementing our approach using the VGG-face and ExpNet architectures with
extensive experiments on the Oulu-CASIA and SFEW datasets, we show that the
proposed approach achieves performance at the state of the art for facial
expression recognition.
On the uniqueness and stability of dictionaries for sparse representation of noisy signals
Authors: Charles J. Garfinkle, Christopher J. Hillar
Date: 2016-06-22 00:00:00
Tasks: Dictionary Learning

Learning optimal dictionaries for sparse coding has exposed characteristic sparse features of many natural signals. However, universal guarantees of the stability of such features in the presence of noise are lacking. Here, we provide very general conditions guaranteeing when dictionaries yielding the sparsest encodings are unique and stable with respect to measurement or modeling error. We demonstrate that some or all original dictionary elements are recoverable from noisy data even if the dictionary fails to satisfy the spark condition, its size is overestimated, or only a polynomial number of distinct sparse supports appear in the data. Importantly, we derive these guarantees without requiring any constraints on the recovered dictionary beyond a natural upper bound on its size. Our results also yield an effective procedure sufficient to affirm if a proposed solution to the dictionary learning problem is unique within bounds commensurate with the noise. We suggest applications to data analysis, engineering, and neuroscience and close with some remaining challenges left open by our work.
Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification
Authors: Yizhong Wang, Kai Liu, Jing Liu, wei he, Yajuan Lyu, Hua Wu, Sujian Li, Haifeng Wang
Date: 2018-05-06 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Reading Comprehension, Question Answering, Reading Comprehension

Machine reading comprehension (MRC) on real web data usually requires the
machine to answer a question by analyzing multiple passages retrieved by search
engine. Compared with MRC on a single passage, multi-passage MRC is more
challenging, since we are likely to get multiple confusing answer candidates
from different passages. To address this problem, we propose an end-to-end
neural model that enables those answer candidates from different passages to
verify each other based on their content representations. Specifically, we
jointly train three modules that can predict the final answer based on three
factors: the answer boundary, the answer content and the cross-passage answer
verification. The experimental results show that our method outperforms the
baseline by a large margin and achieves the state-of-the-art performance on the
English MS-MARCO dataset and the Chinese DuReader dataset, both of which are
designed for MRC in real-world settings.
When Names Are Not Commonly Known: Epistemic Logic with Assignments
Authors: Yanjing Wang, Jeremy Seligman
Date: 2018-05-10 00:00:00

In standard epistemic logic, agent names are usually assumed to be common
knowledge implicitly. This is unreasonable for various applications. Inspired
by term modal logic and assignment operators in dynamic logic, we introduce a
lightweight modal predicate logic where names can be non-rigid. The language
can handle various de dicto and de re distinctions in a natural way. The main
technical result is a complete axiomatisation of this logic over S5 models.
Hybrid semi-Markov CRF for Neural Sequence Labeling
Authors: Zhi-Xiu Ye, Zhen-Hua Ling
Date: 2018-05-10 00:00:00
Proceeding: ACL 2018 7
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

This paper proposes hybrid semi-Markov conditional random fields (SCRFs) for
neural sequence labeling in natural language processing. Based on conventional
conditional random fields (CRFs), SCRFs have been designed for the tasks of
assigning labels to segments by extracting features from and describing
transitions between segments instead of words. In this paper, we improve the
existing SCRF methods by employing word-level and segment-level information
simultaneously. First, word-level labels are utilized to derive the segment
scores in SCRFs. Second, a CRF output layer and an SCRF output layer are
integrated into an unified neural network and trained jointly. Experimental
results on CoNLL 2003 named entity recognition (NER) shared task show that our
model achieves state-of-the-art performance when no external knowledge is used.
Hybrid Adaptive Fuzzy Extreme Learning Machine for text classification
Authors: Ming Li, Peilun Xiao, Ju Zhang
Date: 2018-05-10 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

In traditional ELM and its improved versions suffer from the problems of
outliers or noises due to overfitting and imbalance due to distribution. We
propose a novel hybrid adaptive fuzzy ELM(HA-FELM), which introduces a fuzzy
membership function to the traditional ELM method to deal with the above
problems. We define the fuzzy membership function not only basing on the
distance between each sample and the center of the class but also the density
among samples which based on the quantum harmonic oscillator model. The
proposed fuzzy membership function overcomes the shortcoming of the traditional
fuzzy membership function and could make itself adjusted according to the
specific distribution of different samples adaptively. Experiments show the
proposed HA-FELM can produce better performance than SVM, ELM, and RELM in text
classification.
Text classification based on ensemble extreme learning machine
Authors: Ming Li, Peilun Xiao, Ju Zhang
Date: 2018-05-10 00:00:00
Tasks: Classification, General Classification, text-classification, Text Classification

In this paper, we propose a novel approach based on cost-sensitive ensemble
weighted extreme learning machine; we call this approach AE1-WELM. We apply
this approach to text classification. AE1-WELM is an algorithm including
balanced and imbalanced multiclassification for text classification. Weighted
ELM assigning the different weights to the different samples improves the
classification accuracy to a certain extent, but weighted ELM considers the
differences between samples in the different categories only and ignores the
differences between samples within the same categories. We measure the
importance of the documents by the sample information entropy, and generate
cost-sensitive matrix and factor based on the document importance, then embed
the cost-sensitive weighted ELM into the AdaBoost.M1 framework seamlessly.
Vector space model(VSM) text representation produces the high dimensions and
sparse features which increase the burden of ELM. To overcome this problem, we
develop a text classification framework combining the word vector and AE1-WELM.
The experimental results show that our method provides an accurate, reliable
and effective solution for text classification.
Towards Inference-Oriented Reading Comprehension: ParallelQA
Authors: Soumya Wadhwa, Varsha Embar, Matthias Grabmair, Eric Nyberg
Date: 2018-05-10 00:00:00
Proceeding: WS 2018 6
Tasks: Machine Reading Comprehension, Reading Comprehension

In this paper, we investigate the tendency of end-to-end neural Machine
Reading Comprehension (MRC) models to match shallow patterns rather than
perform inference-oriented reasoning on RC benchmarks. We aim to test the
ability of these systems to answer questions which focus on referential
inference. We propose ParallelQA, a strategy to formulate such questions using
parallel passages. We also demonstrate that existing neural models fail to
generalize well to this setting.
Attention Focusing for Neural Machine Translation by Bridging Source and Target Embeddings
Authors: Shaohui Kuang, Junhui Li, António Branco, Weihua Luo, Deyi Xiong
Date: 2017-11-15 00:00:00
Proceeding: ACL 2018 7
Tasks: Machine Translation, Sentence, Translation, Word Embeddings

In neural machine translation, a source sequence of words is encoded into a
vector from which a target sequence is generated in the decoding phase.
Differently from statistical machine translation, the associations between
source words and their possible target counterparts are not explicitly stored.
Source and target words are at the two ends of a long information processing
procedure, mediated by hidden states at both the source encoding and the target
decoding phases. This makes it possible that a source word is incorrectly
translated into a target word that is not any of its admissible equivalent
counterparts in the target language.
  In this paper, we seek to somewhat shorten the distance between source and
target words in that procedure, and thus strengthen their association, by means
of a method we term bridging source and target word embeddings. We experiment
with three strategies: (1) a source-side bridging model, where source word
embeddings are moved one step closer to the output target sequence; (2) a
target-side bridging model, which explores the more relevant source word
embeddings for the prediction of the target sequence; and (3) a direct bridging
model, which directly connects source and target word embeddings seeking to
minimize errors in the translation of ones by the others.
  Experiments and analysis presented in this paper demonstrate that the
proposed bridging models are able to significantly improve quality of both
sentence translation, in general, and alignment and translation of individual
source words with target words, in particular.
Single Day Outdoor Photometric Stereo
Authors: Yannick Hold-Geoffroy, Paulo F. U. Gotardo, Jean-François Lalonde
Date: 2018-03-28 00:00:00

Photometric Stereo (PS) under outdoor illumination remains a challenging, ill-posed problem due to insufficient variability in illumination. Months-long capture sessions are typically used in this setup, with little success on shorter, single-day time intervals. In this paper, we investigate the solution of outdoor PS over a single day, under different weather conditions. First, we investigate the relationship between weather and surface reconstructability in order to understand when natural lighting allows existing PS algorithms to work. Our analysis reveals that partially cloudy days improve the conditioning of the outdoor PS problem while sunny days do not allow the unambiguous recovery of surface normals from photometric cues alone. We demonstrate that calibrated PS algorithms can thus be employed to reconstruct Lambertian surfaces accurately under partially cloudy days. Second, we solve the ambiguity arising in clear days by combining photometric cues with prior knowledge on material properties, local surface geometry and the natural variations in outdoor lighting through a CNN-based, weakly-calibrated PS technique. Given a sequence of outdoor images captured during a single sunny day, our method robustly estimates the scene surface normals with unprecedented quality for the considered scenario. Our approach does not require precise geolocation and significantly outperforms several state-of-the-art methods on images with real lighting, showing that our CNN can combine efficiently learned priors and photometric cues available during a single sunny day.
Learning Matching Models with Weak Supervision for Response Selection in Retrieval-based Chatbots
Authors: Yu Wu, Wei Wu, Zhoujun Li, Ming Zhou
Date: 2018-05-07 00:00:00
Proceeding: ACL 2018 7
Tasks: Retrieval

We propose a method that can leverage unlabeled data to learn a matching
model for response selection in retrieval-based chatbots. The method employs a
sequence-to-sequence architecture (Seq2Seq) model as a weak annotator to judge
the matching degree of unlabeled pairs, and then performs learning with both
the weak signals and the unlabeled data. Experimental results on two public
data sets indicate that matching models get significant improvements when they
are learned with the proposed method.
A Unified Framework of Deep Neural Networks by Capsules
Authors: Yujian Li, Chuanhui Shan
Date: 2018-05-09 00:00:00
Tasks: Deep Learning

With the growth of deep learning, how to describe deep neural networks
unifiedly is becoming an important issue. We first formalize neural networks
mathematically with their directed graph representations, and prove a
generation theorem about the induced networks of connected directed acyclic
graphs. Then, we set up a unified framework for deep learning with capsule
networks. This capsule framework could simplify the description of existing
deep neural networks, and provide a theoretical basis of graphic designing and
programming techniques for deep learning models, thus would be of great
significance to the advancement of deep learning.
Improved Techniques For Weakly-Supervised Object Localization
Authors: Junsuk Choe, Joo Hyun Park, Hyunjung Shim
Date: 2018-02-22 00:00:00
Tasks: Data Augmentation, Object, Object Localization, Weakly-Supervised Object Localization

We propose an improved technique for weakly-supervised object localization.
Conventional methods have a limitation that they focus only on most
discriminative parts of the target objects. The recent study addressed this
issue and resolved this limitation by augmenting the training data for less
discriminative parts. To this end, we employ an effective data augmentation for
improving the accuracy of the object localization. In addition, we introduce
improved learning techniques by optimizing Convolutional Neural Networks (CNN)
based on the state-of-the-art model. Based on extensive experiments, we
evaluate the effectiveness of the proposed approach both qualitatively and
quantitatively. Especially, we observe that our method improves the Top-1
localization accuracy by 21.4 - 37.3% depending on configurations, compared to
the current state-of-the-art technique of the weakly-supervised object
localization.