pwc-archive/papers-with-abstracts huggingface dataset:
Global Convergence Analysis of the Flower Pollination Algorithm: A Discrete-Time Markov Chain Approach
Authors: Xingshi He, Xin-She Yang, Mehmet Karamanoglu, Yuxin Zhao
Date: 2018-04-21 00:00:00
Tasks: global-optimization, Multiobjective Optimization

Flower pollination algorithm is a recent metaheuristic algorithm for solving
nonlinear global optimization problems. The algorithm has also been extended to
solve multiobjective optimization with promising results. In this work, we
analyze this algorithm mathematically and prove its convergence properties by
using Markov chain theory. By constructing the appropriate transition
probability for a population of flower pollen and using the homogeneity
property, it can be shown that the constructed stochastic sequences can
converge to the optimal set. Under the two proper conditions for convergence,
it is proved that the simplified flower pollination algorithm can indeed
satisfy these convergence conditions and thus the global convergence of this
algorithm can be guaranteed. Numerical experiments are used to demonstrate that
the flower pollination algorithm can converge quickly in practice and can thus
achieve global optimality efficiently.
Multi-Objective De Novo Drug Design with Conditional Graph Generative Model
Authors: Yibo Li, Liangren Zhang, Zhenming Liu
Date: 2018-01-18 00:00:00
Tasks: Drug Design, valid

Recently, deep generative models have revealed itself as a promising way of
performing de novo molecule design. However, previous research has focused
mainly on generating SMILES strings instead of molecular graphs. Although
current graph generative models are available, they are often too general and
computationally expensive, which restricts their application to molecules with
small sizes. In this work, a new de novo molecular design framework is proposed
based on a type sequential graph generators that do not use atom level
recurrent units. Compared with previous graph generative models, the proposed
method is much more tuned for molecule generation and have been scaled up to
cover significantly larger molecules in the ChEMBL database. It is shown that
the graph-based model outperforms SMILES based models in a variety of metrics,
especially in the rate of valid outputs. For the application of drug design
tasks, conditional graph generative model is employed. This method offers
higher flexibility compared to previous fine-tuning based approach and is
suitable for generation based on multiple objectives. This approach is applied
to solve several drug design problems, including the generation of compounds
containing a given scaffold, generation of compounds with specific
drug-likeness and synthetic accessibility requirements, as well as generating
dual inhibitors against JNK3 and GSK3$\beta$. Results show high enrichment
rates for outputs satisfying the given requirements.
Dynamic Meta-Embeddings for Improved Sentence Representations
Authors: Douwe Kiela, Changhan Wang, Kyunghyun Cho
Date: 2018-04-21 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Sentence, Word Embeddings

While one of the first steps in many NLP systems is selecting what
pre-trained word embeddings to use, we argue that such a step is better left
for neural networks to figure out by themselves. To that end, we introduce
dynamic meta-embeddings, a simple yet effective method for the supervised
learning of embedding ensembles, which leads to state-of-the-art performance
within the same model class on a variety of tasks. We subsequently show how the
technique can be used to shed new light on the usage of word embeddings in NLP
systems.
End-to-end Video-level Representation Learning for Action Recognition
Authors: Jiagang Zhu, Wei Zou, Zheng Zhu
Date: 2017-11-11 00:00:00
Tasks: Action Recognition, Optical Flow Estimation, Representation Learning, Temporal Action Localization

From the frame/clip-level feature learning to the video-level representation
building, deep learning methods in action recognition have developed rapidly in
recent years. However, current methods suffer from the confusion caused by
partial observation training, or without end-to-end learning, or restricted to
single temporal scale modeling and so on. In this paper, we build upon
two-stream ConvNets and propose Deep networks with Temporal Pyramid Pooling
(DTPP), an end-to-end video-level representation learning approach, to address
these problems. Specifically, at first, RGB images and optical flow stacks are
sparsely sampled across the whole video. Then a temporal pyramid pooling layer
is used to aggregate the frame-level features which consist of spatial and
temporal cues. Lastly, the trained model has compact video-level representation
with multiple temporal scales, which is both global and sequence-aware.
Experimental results show that DTPP achieves the state-of-the-art performance
on two challenging video action datasets: UCF101 and HMDB51, either by ImageNet
pre-training or Kinetics pre-training.
Neural-Davidsonian Semantic Proto-role Labeling
Authors: Rachel Rudinger, Adam Teichert, Ryan Culkin, Sheng Zhang, Benjamin Van Durme
Date: 2018-04-21 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Attribute

We present a model for semantic proto-role labeling (SPRL) using an adapted bidirectional LSTM encoding strategy that we call "Neural-Davidsonian": predicate-argument structure is represented as pairs of hidden states corresponding to predicate and argument head tokens of the input sequence. We demonstrate: (1) state-of-the-art results in SPRL, and (2) that our network naturally shares parameters between attributes, allowing for learning new attribute types with limited added supervision.
Eval all, trust a few, do wrong to none: Comparing sentence generation models
Authors: Ondřej Cífka, Aliaksei Severyn, Enrique Alfonseca, Katja Filippova
Date: 2018-04-21 00:00:00
Tasks: All, Sentence, Text Generation

In this paper, we study recent neural generative models for text generation
related to variational autoencoders. Previous works have employed various
techniques to control the prior distribution of the latent codes in these
models, which is important for sampling performance, but little attention has
been paid to reconstruction error. In our study, we follow a rigorous
evaluation protocol using a large set of previously used and novel automatic
and human evaluation metrics, applied to both generated samples and
reconstructions. We hope that it will become the new evaluation standard when
comparing neural generative models for text.
Faster Shift-Reduce Constituent Parsing with a Non-Binary, Bottom-Up Strategy
Authors: Daniel Fernández-González, Carlos Gómez-Rodríguez
Date: 2018-04-21 00:00:00
Tasks: Binarization, Machine Translation, Sentence

An increasingly wide range of artificial intelligence applications rely on syntactic information to process and extract meaning from natural language text or speech, with constituent trees being one of the most widely used syntactic formalisms. To produce these phrase-structure representations from sentences in natural language, shift-reduce constituent parsers have become one of the most efficient approaches. Increasing their accuracy and speed is still one of the main objectives pursued by the research community so that artificial intelligence applications that make use of parsing outputs, such as machine translation or voice assistant services, can improve their performance. With this goal in mind, we propose in this article a novel non-binary shift-reduce algorithm for constituent parsing. Our parser follows a classical bottom-up strategy but, unlike others, it straightforwardly creates non-binary branchings with just one Reduce transition, instead of requiring prior binarization or a sequence of binary transitions, allowing its direct application to any language without the need of further resources such as percolation tables. As a result, it uses fewer transitions per sentence than existing transition-based constituent parsers, becoming the fastest such system and, as a consequence, speeding up downstream applications. Using static oracle training and greedy search, the accuracy of this novel approach is on par with state-of-the-art transition-based constituent parsers and outperforms all top-down and bottom-up greedy shift-reduce systems on the Wall Street Journal section from the English Penn Treebank and the Penn Chinese Treebank. Additionally, we develop a dynamic oracle for training the proposed transition-based algorithm, achieving further improvements in both benchmarks and obtaining the best accuracy to date on the Penn Chinese Treebank among greedy shift-reduce parsers.
Expert Finding in Community Question Answering: A Review
Authors: Sha Yuan, Yu Zhang, Jie Tang, Juan Bautista Cabotà
Date: 2018-04-21 00:00:00
Tasks: Community Question Answering, Ensemble Learning, Model Selection, Question Answering, Recommendation Systems

The rapid development recently of Community Question Answering (CQA)
satisfies users quest for professional and personal knowledge about anything.
In CQA, one central issue is to find users with expertise and willingness to
answer the given questions. Expert finding in CQA often exhibits very different
challenges compared to traditional methods. Sparse data and new features
violate fundamental assumptions of traditional recommendation systems. This
paper focuses on reviewing and categorizing the current progress on expert
finding in CQA. We classify all the existing solutions into four different
categories: matrix factorization based models (MF-based models), gradient
boosting tree based models (GBT-based models), deep learning based models
(DL-based models) and ranking based models (R-based models). We find that
MF-based models outperform other categories of models in the field of expert
finding in CQA. Moreover, we use innovative diagrams to clarify several
important concepts of ensemble learning, and find that ensemble models with
several specific single models can further boosting the performance. Further,
we compare the performance of different models on different types of matching
tasks, including text vs. text, graph vs. text, audio vs. text and video vs.
text. The results can help the model selection of expert finding in practice.
Finally, we explore some potential future issues in expert finding research in
CQA.
Automated essay scoring with string kernels and word embeddings
Authors: Mădălina Cozma, Andrei M. Butnaru, Radu Tudor Ionescu
Date: 2018-04-21 00:00:00
Proceeding: ACL 2018 7
Tasks: Automated Essay Scoring, Dialect Identification, General Classification, Language Identification, Native Language Identification, text-classification, Text Classification, Word Embeddings

In this work, we present an approach based on combining string kernels and
word embeddings for automatic essay scoring. String kernels capture the
similarity among strings based on counting common character n-grams, which are
a low-level yet powerful type of feature, demonstrating state-of-the-art
results in various text classification tasks such as Arabic dialect
identification or native language identification. To our best knowledge, we are
the first to apply string kernels to automatically score essays. We are also
the first to combine them with a high-level semantic feature representation,
namely the bag-of-super-word-embeddings. We report the best performance on the
Automated Student Assessment Prize data set, in both in-domain and cross-domain
settings, surpassing recent state-of-the-art deep learning approaches.
Variational Inference In Pachinko Allocation Machines
Authors: Akash Srivastava, Charles Sutton
Date: 2018-04-21 00:00:00
Tasks: Variational Inference

The Pachinko Allocation Machine (PAM) is a deep topic model that allows
representing rich correlation structures among topics by a directed acyclic
graph over topics. Because of the flexibility of the model, however,
approximate inference is very difficult. Perhaps for this reason, only a small
number of potential PAM architectures have been explored in the literature. In
this paper we present an efficient and flexible amortized variational inference
method for PAM, using a deep inference network to parameterize the approximate
posterior distribution in a manner similar to the variational autoencoder. Our
inference method produces more coherent topics than state-of-art inference
methods for PAM while being an order of magnitude faster, which allows
exploration of a wider range of PAM architectures than have previously been
studied.
Generative Stock Question Answering
Authors: Zhaopeng Tu, Yong Jiang, Xiaojiang Liu, Lei Shu, Shuming Shi
Date: 2018-04-21 00:00:00
Tasks: Decoder, Question Answering, Retrieval

We study the problem of stock related question answering (StockQA):
automatically generating answers to stock related questions, just like
professional stock analysts providing action recommendations to stocks upon
user's requests. StockQA is quite different from previous QA tasks since (1)
the answers in StockQA are natural language sentences (rather than entities or
values) and due to the dynamic nature of StockQA, it is scarcely possible to
get reasonable answers in an extractive way from the training data; and (2)
StockQA requires properly analyzing the relationship between keywords in QA
pair and the numerical features of a stock. We propose to address the problem
with a memory-augmented encoder-decoder architecture, and integrate different
mechanisms of number understanding and generation, which is a critical
component of StockQA.
  We build a large-scale dataset containing over 180K StockQA instances, based
on which various technique combinations are extensively studied and compared.
Experimental results show that a hybrid word-character model with separate
character components for number processing, achieves the best performance. By
analyzing the results, we found that 44.8% of answers generated by our best
model still suffer from the generic answer problem, which can be alleviated by
a straightforward hybrid retrieval-generation model.
Is feature selection secure against training data poisoning?
Authors: Huang Xiao, Battista Biggio, Gavin Brown, Giorgio Fumera, Claudia Eckert, Fabio Roli
Date: 2018-04-21 00:00:00
Tasks: Computational Efficiency, Data Poisoning, feature selection, Malware Detection

Learning in adversarial settings is becoming an important task for
application domains where attackers may inject malicious data into the training
set to subvert normal operation of data-driven technologies. Feature selection
has been widely used in machine learning for security applications to improve
generalization and computational efficiency, although it is not clear whether
its use may be beneficial or even counterproductive when training data are
poisoned by intelligent attackers. In this work, we shed light on this issue by
providing a framework to investigate the robustness of popular feature
selection methods, including LASSO, ridge regression and the elastic net. Our
results on malware detection show that feature selection methods can be
significantly compromised under attack (we can reduce LASSO to almost random
choices of feature sets by careful insertion of less than 5% poisoned training
samples), highlighting the need for specific countermeasures.
Multi-view registration of unordered range scans by fast correspondence propagation of multi-scale descriptors
Authors: Jihua Zhu, Siyu Xu, Zutao Jiang, Shanmin Pang, Jun Wang, Zhongyu Li
Date: 2018-04-21 00:00:00

This paper proposes a global approach for the multi-view registration of
unordered range scans. As the basis of multi-view registration, pair-wise
registration is very pivotal. Therefore, we first select a good descriptor and
accelerate its correspondence propagation for the pair-wise registration. Then,
we design an effective rule to judge the reliability of pair-wise registration
results. Subsequently, we propose a model augmentation method, which can
utilize reliable results of pair-wise registration to augment the model shape.
Finally, multi-view registration can be accomplished by operating the pair-wise
registration and judgment, and model augmentation alternately. Experimental
results on public available data sets show, that this approach can
automatically achieve the multi-view registration of unordered range scans with
good accuracy and effectiveness.
Decoupling Structure and Lexicon for Zero-Shot Semantic Parsing
Authors: Jonathan Herzig, Jonathan Berant
Date: 2018-04-21 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Form, Semantic Parsing

Building a semantic parser quickly in a new domain is a fundamental challenge
for conversational interfaces, as current semantic parsers require expensive
supervision and lack the ability to generalize to new domains. In this paper,
we introduce a zero-shot approach to semantic parsing that can parse utterances
in unseen domains while only being trained on examples in other source domains.
First, we map an utterance to an abstract, domain-independent, logical form
that represents the structure of the logical form, but contains slots instead
of KB constants. Then, we replace slots with KB constants via lexical alignment
scores and global inference. Our model reaches an average accuracy of 53.4% on
7 domains in the Overnight dataset, substantially better than other zero-shot
baselines, and performs as good as a parser trained on over 30% of the target
domain examples.
A Stable and Effective Learning Strategy for Trainable Greedy Decoding
Authors: Yun Chen, Victor O. K. Li, Kyunghyun Cho, Samuel R. Bowman
Date: 2018-04-21 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Decoder, Machine Translation, Reinforcement Learning, Translation

Beam search is a widely used approximate search strategy for neural network
decoders, and it generally outperforms simple greedy decoding on tasks like
machine translation. However, this improvement comes at substantial
computational cost. In this paper, we propose a flexible new method that allows
us to reap nearly the full benefits of beam search with nearly no additional
computational cost. The method revolves around a small neural network actor
that is trained to observe and manipulate the hidden state of a
previously-trained decoder. To train this actor network, we introduce the use
of a pseudo-parallel corpus built using the output of beam search on a base
model, ranked by a target quality metric like BLEU. Our method is inspired by
earlier work on this problem, but requires no reinforcement learning, and can
be trained reliably on a range of models. Experiments on three parallel corpora
and three architectures show that the method yields substantial improvements in
translation quality and speed over each base system.
Multi-task Learning for Universal Sentence Embeddings: A Thorough Evaluation using Transfer and Auxiliary Tasks
Authors: Wasi Uddin Ahmad, Xueying Bai, Zhechao Huang, Chao Jiang, Nanyun Peng, Kai-Wei Chang
Date: 2018-04-21 00:00:00
Tasks: Multi-Task Learning, Natural Language Inference, Sentence, Sentence Embeddings, Transfer Learning

Learning distributed sentence representations is one of the key challenges in
natural language processing. Previous work demonstrated that a recurrent neural
network (RNNs) based sentence encoder trained on a large collection of
annotated natural language inference data, is efficient in the transfer
learning to facilitate other related tasks. In this paper, we show that joint
learning of multiple tasks results in better generalizable sentence
representations by conducting extensive experiments and analysis comparing the
multi-task and single-task learned sentence encoders. The quantitative analysis
using auxiliary tasks show that multi-task learning helps to embed better
semantic information in the sentence representations compared to single-task
learning. In addition, we compare multi-task sentence encoders with
contextualized word representations and show that combining both of them can
further boost the performance of transfer learning.
Learning to Refine Human Pose Estimation
Authors: Mihai Fieraru, Anna Khoreva, Leonid Pishchulin, Bernt Schiele
Date: 2018-04-21 00:00:00
Tasks: Data Augmentation, Keypoint Detection, Multi-Person Pose Estimation, Multi-Person Pose Estimation and Tracking, Pose Estimation, Pose Tracking

Multi-person pose estimation in images and videos is an important yet
challenging task with many applications. Despite the large improvements in
human pose estimation enabled by the development of convolutional neural
networks, there still exist a lot of difficult cases where even the
state-of-the-art models fail to correctly localize all body joints. This
motivates the need for an additional refinement step that addresses these
challenging cases and can be easily applied on top of any existing method. In
this work, we introduce a pose refinement network (PoseRefiner) which takes as
input both the image and a given pose estimate and learns to directly predict a
refined pose by jointly reasoning about the input-output space. In order for
the network to learn to refine incorrect body joint predictions, we employ a
novel data augmentation scheme for training, where we model "hard" human pose
cases. We evaluate our approach on four popular large-scale pose estimation
benchmarks such as MPII Single- and Multi-Person Pose Estimation, PoseTrack
Pose Estimation, and PoseTrack Pose Tracking, and report systematic improvement
over the state of the art.
Unsupervised Natural Language Generation with Denoising Autoencoders
Authors: Markus Freitag, Scott Roy
Date: 2018-04-21 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Denoising, Question Answering, Sentence, Text Generation

Generating text from structured data is important for various tasks such as
question answering and dialog systems. We show that in at least one domain,
without any supervision and only based on unlabeled text, we are able to build
a Natural Language Generation (NLG) system with higher performance than
supervised approaches. In our approach, we interpret the structured data as a
corrupt representation of the desired output and use a denoising auto-encoder
to reconstruct the sentence. We show how to introduce noise into training
examples that do not contain structured data, and that the resulting denoising
auto-encoder generalizes to generate correct sentences when given structured
data.
Entity-aware Image Caption Generation
Authors: Di Lu, Spencer Whitehead, Lifu Huang, Heng Ji, Shih-Fu Chang
Date: 2018-04-21 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Caption Generation, Image Captioning

Current image captioning approaches generate descriptions which lack specific
information, such as named entities that are involved in the images. In this
paper we propose a new task which aims to generate informative image captions,
given images and hashtags as input. We propose a simple but effective approach
to tackle this problem. We first train a convolutional neural networks - long
short term memory networks (CNN-LSTM) model to generate a template caption
based on the input image. Then we use a knowledge graph based collective
inference algorithm to fill in the template with specific named entities
retrieved via the hashtags. Experiments on a new benchmark dataset collected
from Flickr show that our model generates news-style image descriptions with
much richer information. Our model outperforms unimodal baselines significantly
with various evaluation metrics.
Stochastic Answer Networks for Natural Language Inference
Authors: Xiaodong Liu, Kevin Duh, Jianfeng Gao
Date: 2018-04-21 00:00:00
Tasks: Natural Language Inference

We propose a stochastic answer network (SAN) to explore multi-step inference
strategies in Natural Language Inference. Rather than directly predicting the
results given the inputs, the model maintains a state and iteratively refines
its predictions. Our experiments show that SAN achieves the state-of-the-art
results on three benchmarks: Stanford Natural Language Inference (SNLI)
dataset, MultiGenre Natural Language Inference (MultiNLI) dataset and Quora
Question Pairs dataset.