pwc-archive/papers-with-abstracts huggingface dataset:
SARN: Relational Reasoning through Sequential Attention
Authors: Jinwon An, Sungwon Lyu, Sungzoon Cho
Date: 2018-11-01 00:00:00
Tasks: Relational Reasoning

This paper proposes an attention module augmented relational network called
SARN(Sequential Attention Relational Network) that can carry out relational
reasoning by extracting reference objects and making efficient pairing between
objects. SARN greatly reduces the computational and memory requirements of the
relational network, which computes all object pairs. It also shows high
accuracy on the Sort-of-CLEVR dataset compared to other models, especially on
relational questions.
On the End-to-End Solution to Mandarin-English Code-switching Speech Recognition
Authors: Zhiping Zeng, Yerbolat Khassanov, Van Tung Pham, Hai-Hua Xu, Eng Siong Chng, Haizhou Li
Date: 2018-11-01 00:00:00
Tasks: Data Augmentation, Language Identification, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Code-switching (CS) refers to a linguistic phenomenon where a speaker uses different languages in an utterance or between alternating utterances. In this work, we study end-to-end (E2E) approaches to the Mandarin-English code-switching speech recognition (CSSR) task. We first examine the effectiveness of using data augmentation and byte-pair encoding (BPE) subword units. More importantly, we propose a multitask learning recipe, where a language identification task is explicitly learned in addition to the E2E speech recognition task. Furthermore, we introduce an efficient word vocabulary expansion method for language modeling to alleviate data sparsity issues under the code-switching scenario. Experimental results on the SEAME data, a Mandarin-English CS corpus, demonstrate the effectiveness of the proposed methods.
GlobalTrait: Personality Alignment of Multilingual Word Embeddings
Authors: Farhad Bin Siddique, Dario Bertero, Pascale Fung
Date: 2018-11-01 00:00:00
Tasks: Multilingual Word Embeddings, Personality Alignment, Word Embeddings

We propose a multilingual model to recognize Big Five Personality traits from
text data in four different languages: English, Spanish, Dutch and Italian. Our
analysis shows that words having a similar semantic meaning in different
languages do not necessarily correspond to the same personality traits.
Therefore, we propose a personality alignment method, GlobalTrait, which has a
mapping for each trait from the source language to the target language
(English), such that words that correlate positively to each trait are close
together in the multilingual vector space. Using these aligned embeddings for
training, we can transfer personality related training features from
high-resource languages such as English to other low-resource languages, and
get better multilingual results, when compared to using simple monolingual and
unaligned multilingual embeddings. We achieve an average F-score increase
(across all three languages except English) from 65 to 73.4 (+8.4), when
comparing our monolingual model to multilingual using CNN with personality
aligned embeddings. We also show relatively good performance in the regression
tasks, and better classification results when evaluating our model on a
separate Chinese dataset.
Progressive Memory Banks for Incremental Domain Adaptation
Authors: Nabiha Asghar, Lili Mou, Kira A. Selby, Kevin D. Pantasdo, Pascal Poupart, Xin Jiang
Date: 2018-11-01 00:00:00
Proceeding: ICLR 2020 1
Tasks: Domain Adaptation

This paper addresses the problem of incremental domain adaptation (IDA) in natural language processing (NLP). We assume each domain comes one after another, and that we could only access data in the current domain. The goal of IDA is to build a unified model performing well on all the domains that we have encountered. We adopt the recurrent neural network (RNN) widely used in NLP, but augment it with a directly parameterized memory bank, which is retrieved by an attention mechanism at each step of RNN transition. The memory bank provides a natural way of IDA: when adapting our model to a new domain, we progressively add new slots to the memory bank, which increases the number of parameters, and thus the model capacity. We learn the new memory slots and fine-tune existing parameters by back-propagation. Experimental results show that our approach achieves significantly better performance than fine-tuning alone. Compared with expanding hidden states, our approach is more robust for old domains, shown by both empirical and theoretical results. Our model also outperforms previous work of IDA including elastic weight consolidation and progressive neural networks in the experiments.
Spelling Error Correction Using a Nested RNN Model and Pseudo Training Data
Authors: Hao Li, Yang Wang, Xinyu Liu, Zhichao Sheng, Si Wei
Date: 2018-11-01 00:00:00
Tasks: Feature Engineering

We propose a nested recurrent neural network (nested RNN) model for English
spelling error correction and generate pseudo data based on phonetic similarity
to train it. The model fuses orthographic information and context as a whole
and is trained in an end-to-end fashion. This avoids feature engineering and
does not rely on a noisy channel model as in traditional methods. Experiments
show that the proposed method is superior to existing systems in correcting
spelling errors.
Survey on Vision-based Path Prediction
Authors: Tsubasa Hirakawa, Takayoshi Yamashita, Toru Tamaki, Hironobu Fujiyoshi
Date: 2018-11-01 00:00:00
Tasks: Prediction, Survey

Path prediction is a fundamental task for estimating how pedestrians or
vehicles are going to move in a scene. Because path prediction as a task of
computer vision uses video as input, various information used for prediction,
such as the environment surrounding the target and the internal state of the
target, need to be estimated from the video in addition to predicting paths.
Many prediction approaches that include understanding the environment and the
internal state have been proposed. In this survey, we systematically summarize
methods of path prediction that take video as input and and extract features
from the video. Moreover, we introduce datasets used to evaluate path
prediction methods quantitatively.
Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension
Authors: Daesik Kim, Seonhoon Kim, Nojun Kwak
Date: 2018-11-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Open Set Learning, Question Answering, Reading Comprehension, Self-Supervised Learning

In this work, we introduce a novel algorithm for solving the textbook question answering (TQA) task which describes more realistic QA problems compared to other recent tasks. We mainly focus on two related issues with analysis of the TQA dataset. First, solving the TQA problems requires to comprehend multi-modal contexts in complicated input data. To tackle this issue of extracting knowledge features from long text lessons and merging them with visual features, we establish a context graph from texts and images, and propose a new module f-GCN based on graph convolutional networks (GCN). Second, scientific terms are not spread over the chapters and subjects are split in the TQA dataset. To overcome this so called "out-of-domain" issue, before learning QA problems, we introduce a novel self-supervised open-set learning process without any annotations. The experimental results show that our model significantly outperforms prior state-of-the-art methods. Moreover, ablation studies validate that both methods of incorporating f-GCN for extracting knowledge from multi-modal contexts and our newly proposed self-supervised learning process are effective for TQA problems.
Towards learning-to-learn
Authors: Benjamin James Lansdell, Konrad Paul Kording
Date: 2018-11-01 00:00:00
Tasks: BIG-bench Machine Learning

In good old-fashioned artificial intelligence (GOFAI), humans specified
systems that solved problems. Much of the recent progress in AI has come from
replacing human insights by learning. However, learning itself is still usually
built by humans -- specifically the choice that parameter updates should follow
the gradient of a cost function. Yet, in analogy with GOFAI, there is no reason
to believe that humans are particularly good at defining such learning systems:
we may expect learning itself to be better if we learn it. Recent research in
machine learning has started to realize the benefits of that strategy. We
should thus expect this to be relevant for neuroscience: how could the correct
learning rules be acquired? Indeed, cognitive science has long shown that
humans learn-to-learn, which is potentially responsible for their impressive
learning abilities. Here we discuss ideas across machine learning,
neuroscience, and cognitive science that matter for the principle of
learning-to-learn.
A sequential guiding network with attention for image captioning
Authors: Daouda Sow, Zengchang Qin, Mouhamed Niasse, Tao Wan
Date: 2018-11-01 00:00:00
Tasks: Decoder, Image Captioning

The recent advances of deep learning in both computer vision (CV) and natural
language processing (NLP) provide us a new way of understanding semantics, by
which we can deal with more challenging tasks such as automatic description
generation from natural images. In this challenge, the encoder-decoder
framework has achieved promising performance when a convolutional neural
network (CNN) is used as image encoder and a recurrent neural network (RNN) as
decoder. In this paper, we introduce a sequential guiding network that guides
the decoder during word generation. The new model is an extension of the
encoder-decoder framework with attention that has an additional guiding long
short-term memory (LSTM) and can be trained in an end-to-end manner by using
image/descriptions pairs. We validate our approach by conducting extensive
experiments on a benchmark dataset, i.e., MS COCO Captions. The proposed model
achieves significant improvement comparing to the other state-of-the-art deep
learning models.
Understanding Learning Dynamics Of Language Models with SVCCA
Authors: Naomi Saphra, Adam Lopez
Date: 2018-11-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Language Modeling, Language Modelling

Research has shown that neural models implicitly encode linguistic features,
but there has been no research showing \emph{how} these encodings arise as the
models are trained. We present the first study on the learning dynamics of
neural language models, using a simple and flexible analysis method called
Singular Vector Canonical Correlation Analysis (SVCCA), which enables us to
compare learned representations across time and across models, without the need
to evaluate directly on annotated data. We probe the evolution of syntactic,
semantic, and topic representations and find that part-of-speech is learned
earlier than topic; that recurrent layers become more similar to those of a
tagger during training; and embedding layers less similar. Our results and
methods could inform better learning algorithms for NLP models, possibly to
incorporate linguistic information more effectively.
Neural Music Synthesis for Flexible Timbre Control
Authors: Jong Wook Kim, Rachel Bittner, Aparna Kumar, Juan Pablo Bello
Date: 2018-11-01 00:00:00

The recent success of raw audio waveform synthesis models like WaveNet
motivates a new approach for music synthesis, in which the entire process ---
creating audio samples from a score and instrument information --- is modeled
using generative neural networks. This paper describes a neural music synthesis
model with flexible timbre controls, which consists of a recurrent neural
network conditioned on a learned instrument embedding followed by a WaveNet
vocoder. The learned embedding space successfully captures the diverse
variations in timbres within a large dataset and enables timbre control and
morphing by interpolating between instruments in the embedding space. The
synthesis quality is evaluated both numerically and perceptually, and an
interactive web demo is presented.
CariGANs: Unpaired Photo-to-Caricature Translation
Authors: Kaidi Cao, Jing Liao, Lu Yuan
Date: 2018-11-01 00:00:00
Tasks: Caricature, Generative Adversarial Network, Photo-To-Caricature Translation, Translation

Facial caricature is an art form of drawing faces in an exaggerated way to
convey humor or sarcasm. In this paper, we propose the first Generative
Adversarial Network (GAN) for unpaired photo-to-caricature translation, which
we call "CariGANs". It explicitly models geometric exaggeration and appearance
stylization using two components: CariGeoGAN, which only models the
geometry-to-geometry transformation from face photos to caricatures, and
CariStyGAN, which transfers the style appearance from caricatures to face
photos without any geometry deformation. In this way, a difficult cross-domain
translation problem is decoupled into two easier tasks. The perceptual study
shows that caricatures generated by our CariGANs are closer to the hand-drawn
ones, and at the same time better persevere the identity, compared to
state-of-the-art methods. Moreover, our CariGANs allow users to control the
shape exaggeration degree and change the color/texture style by tuning the
parameters or giving an example caricature.
Consistent estimation of the max-flow problem: Towards unsupervised image segmentation
Authors: Ashif Sikandar Iquebal, Satish Bukkapatnam
Date: 2018-11-01 00:00:00
Tasks: Brain Tumor Segmentation, Decision Making, Image Segmentation, Segmentation, Semantic Segmentation, Unsupervised Image Segmentation

Advances in the image-based diagnostics of complex biological and manufacturing processes have brought unsupervised image segmentation to the forefront of enabling automated, on the fly decision making. However, most existing unsupervised segmentation approaches are either computationally complex or require manual parameter selection (e.g., flow capacities in max-flow/min-cut segmentation). In this work, we present a fully unsupervised segmentation approach using a continuous max-flow formulation over the image domain while optimally estimating the flow parameters from the image characteristics. More specifically, we show that the maximum a posteriori estimate of the image labels can be formulated as a continuous max-flow problem given the flow capacities are known. The flow capacities are then iteratively obtained by employing a novel Markov random field prior over the image domain. We present theoretical results to establish the posterior consistency of the flow capacities. We compare the performance of our approach on two real-world case studies including brain tumor image segmentation and defect identification in additively manufactured components using electron microscopic images. Comparative results with several state-of-the-art supervised as well as unsupervised methods suggest that the present method performs statistically similar to the supervised methods, but results in more than 90% improvement in the Dice score when compared to the state-of-the-art unsupervised methods.
Tattoo Image Search at Scale: Joint Detection and Compact Representation Learning
Authors: Hu Han, Jie Li, Anil K. Jain, Shiguang Shan, Xilin Chen
Date: 2018-11-01 00:00:00
Tasks: Image Retrieval, Multi-Task Learning, Person Identification, Representation Learning, Retrieval

The explosive growth of digital images in video surveillance and social media
has led to the significant need for efficient search of persons of interest in
law enforcement and forensic applications. Despite tremendous progress in
primary biometric traits (e.g., face and fingerprint) based person
identification, a single biometric trait alone cannot meet the desired
recognition accuracy in forensic scenarios. Tattoos, as one of the important
soft biometric traits, have been found to be valuable for assisting in person
identification. However, tattoo search in a large collection of unconstrained
images remains a difficult problem, and existing tattoo search methods mainly
focus on matching cropped tattoos, which is different from real application
scenarios. To close the gap, we propose an efficient tattoo search approach
that is able to learn tattoo detection and compact representation jointly in a
single convolutional neural network (CNN) via multi-task learning. While the
features in the backbone network are shared by both tattoo detection and
compact representation learning, individual latent layers of each sub-network
optimize the shared features toward the detection and feature learning tasks,
respectively. We resolve the small batch size issue inside the joint tattoo
detection and compact representation learning network via random image stitch
and preceding feature buffering. We evaluate the proposed tattoo search system
using multiple public-domain tattoo benchmarks, and a gallery set with about
300K distracter tattoo images compiled from these datasets and images from the
Internet. In addition, we also introduce a tattoo sketch dataset containing 300
tattoos for sketch-based tattoo search. Experimental results show that the
proposed approach has superior performance in tattoo detection and tattoo
search at scale compared to several state-of-the-art tattoo retrieval
algorithms.
META-DES.Oracle: Meta-learning and feature selection for ensemble selection
Authors: Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
Date: 2018-11-01 00:00:00
Tasks: feature selection, General Classification, Meta-Learning

The key issue in Dynamic Ensemble Selection (DES) is defining a suitable
criterion for calculating the classifiers' competence. There are several
criteria available to measure the level of competence of base classifiers, such
as local accuracy estimates and ranking. However, using only one criterion may
lead to a poor estimation of the classifier's competence. In order to deal with
this issue, we have proposed a novel dynamic ensemble selection framework using
meta-learning, called META-DES. An important aspect of the META-DES framework
is that multiple criteria can be embedded in the system encoded as different
sets of meta-features. However, some DES criteria are not suitable for every
classification problem. For instance, local accuracy estimates may produce poor
results when there is a high degree of overlap between the classes. Moreover, a
higher classification accuracy can be obtained if the performance of the
meta-classifier is optimized for the corresponding data. In this paper, we
propose a novel version of the META-DES framework based on the formal
definition of the Oracle, called META-DES.Oracle. The Oracle is an abstract
method that represents an ideal classifier selection scheme. A meta-feature
selection scheme using an overfitting cautious Binary Particle Swarm
Optimization (BPSO) is proposed for improving the performance of the
meta-classifier. The difference between the outputs obtained by the
meta-classifier and those presented by the Oracle is minimized. Thus, the
meta-classifier is expected to obtain results that are similar to the Oracle.
Experiments carried out using 30 classification problems demonstrate that the
optimization procedure based on the Oracle definition leads to a significant
improvement in classification accuracy when compared to previous versions of
the META-DES framework and other state-of-the-art DES techniques.
Online Planner Selection with Graph Neural Networks and Adaptive Scheduling
Authors: Tengfei Ma, Patrick Ferber, Siyu Huo, Jie Chen, Michael Katz
Date: 2018-11-01 00:00:00
Tasks: Deep Learning, Graph Neural Network, Scheduling

Automated planning is one of the foundational areas of AI. Since no single planner can work well for all tasks and domains, portfolio-based techniques have become increasingly popular in recent years. In particular, deep learning emerges as a promising methodology for online planner selection. Owing to the recent development of structural graph representations of planning tasks, we propose a graph neural network (GNN) approach to selecting candidate planners. GNNs are advantageous over a straightforward alternative, the convolutional neural networks, in that they are invariant to node permutations and that they incorporate node labels for better inference. Additionally, for cost-optimal planning, we propose a two-stage adaptive scheduling method to further improve the likelihood that a given task is solved in time. The scheduler may switch at halftime to a different planner, conditioned on the observed performance of the first one. Experimental results validate the effectiveness of the proposed method against strong baselines, both deep learning and non-deep learning based. The code is available at \url{https://github.com/matenure/GNN_planner}.
Multi-Label Robust Factorization Autoencoder and its Application in Predicting Drug-Drug Interactions
Authors: Xu Chu, Yang Lin, Jingyue Gao, Jiangtao Wang, Yasha Wang, Leye Wang
Date: 2018-11-01 00:00:00
Tasks: Decoder

Drug-drug interactions (DDIs) are a major cause of preventable
hospitalizations and deaths. Predicting the occurrence of DDIs helps drug
safety professionals allocate investigative resources and take appropriate
regulatory action promptly. Traditional DDI prediction methods predict DDIs
based on the similarity between drugs. Recently, researchers revealed that
predictive performance can be improved by better modeling the interactions
between drug pairs with bilinear forms. However, the shallow models leveraging
bilinear forms suffer from limitations on capturing complicated nonlinear
interactions between drug pairs. To this end, we propose Multi-Label Robust
Factorization Autoencoder (abbreviated to MuLFA) for DDI prediction, which
learns a representation of interactions between drug pairs and has the
capability of characterizing complicated nonlinear interactions more precisely.
Moreover, a novel loss called CuXCov is designed to effectively learn the
parameters of MuLFA. Furthermore, the decoder is able to generate high-risk
chemical structures of drug pairs for specific DDIs, assisting pharmacists to
better understand the relationship between drug chemistry and DDI. Experimental
results on real-world datasets demonstrate that MuLFA consistently outperforms
state-of-the-art methods; particularly, it increases 21:3% predictive
performance compared to the best baseline for top 50 frequent DDIs.We also
illustrate various case studies to demonstrate the efficacy of the chemical
structures generated by MuLFA in DDI diagnosis.
Towards Empathetic Open-domain Conversation Models: a New Benchmark and Dataset
Authors: Hannah Rashkin, Eric Michael Smith, Margaret Li, Y-Lan Boureau
Date: 2018-11-01 00:00:00
Proceeding: ACL 2019 7
Tasks: Dialogue Generation

One challenge for dialogue agents is recognizing feelings in the conversation partner and replying accordingly, a key communicative skill. While it is straightforward for humans to recognize and acknowledge others' feelings in a conversation, this is a significant challenge for AI systems due to the paucity of suitable publicly-available datasets for training and evaluation. This work proposes a new benchmark for empathetic dialogue generation and EmpatheticDialogues, a novel dataset of 25k conversations grounded in emotional situations. Our experiments indicate that dialogue models that use our dataset are perceived to be more empathetic by human evaluators, compared to models merely trained on large-scale Internet conversation data. We also present empirical comparisons of dialogue model adaptations for empathetic responding, leveraging existing models or datasets without requiring lengthy re-training of the full model.