pwc-archive/papers-with-abstracts huggingface dataset:
Learning Programmatic Idioms for Scalable Semantic Parsing
Authors: Srinivasan Iyer, Alvin Cheung, Luke Zettlemoyer
Date: 2019-04-19 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Code Generation, Semantic Parsing

Programmers typically organize executable source code using high-level coding patterns or idiomatic structures such as nested loops, exception handlers and recursive blocks, rather than as individual code tokens. In contrast, state of the art (SOTA) semantic parsers still map natural language instructions to source code by building the code syntax tree one node at a time. In this paper, we introduce an iterative method to extract code idioms from large source code corpora by repeatedly collapsing most-frequent depth-2 subtrees of their syntax trees, and train semantic parsers to apply these idioms during decoding. Applying idiom-based decoding on a recent context-dependent semantic parsing task improves the SOTA by 2.2\% BLEU score while reducing training time by more than 50\%. This improved speed enables us to scale up the model by training on an extended training set that is 5$\times$ larger, to further move up the SOTA by an additional 2.3\% BLEU and 0.9\% exact match. Finally, idioms also significantly improve accuracy of semantic parsing to SQL on the ATIS-SQL dataset, when training data is limited.
LATTE: Accelerating LiDAR Point Cloud Annotation via Sensor Fusion, One-Click Annotation, and Tracking
Authors: Bernie Wang, Virginia Wu, Bichen Wu, Kurt Keutzer
Date: 2019-04-19 00:00:00
Tasks: Autonomous Vehicles, Sensor Fusion

LiDAR (Light Detection And Ranging) is an essential and widely adopted sensor
for autonomous vehicles, particularly for those vehicles operating at higher
levels (L4-L5) of autonomy. Recent work has demonstrated the promise of
deep-learning approaches for LiDAR-based detection. However, deep-learning
algorithms are extremely data hungry, requiring large amounts of labeled
point-cloud data for training and evaluation. Annotating LiDAR point cloud data
is challenging due to the following issues: 1) A LiDAR point cloud is usually
sparse and has low resolution, making it difficult for human annotators to
recognize objects. 2) Compared to annotation on 2D images, the operation of
drawing 3D bounding boxes or even point-wise labels on LiDAR point clouds is
more complex and time-consuming. 3) LiDAR data are usually collected in
sequences, so consecutive frames are highly correlated, leading to repeated
annotations. To tackle these challenges, we propose LATTE, an open-sourced
annotation tool for LiDAR point clouds. LATTE features the following
innovations: 1) Sensor fusion: We utilize image-based detection algorithms to
automatically pre-label a calibrated image, and transfer the labels to the
point cloud. 2) One-click annotation: Instead of drawing 3D bounding boxes or
point-wise labels, we simplify the annotation to just one click on the target
object, and automatically generate the bounding box for the target. 3)
Tracking: we integrate tracking into sequence annotation such that we can
transfer labels from one frame to subsequent ones and therefore significantly
reduce repeated labeling. Experiments show the proposed features accelerate the
annotation speed by 6.2x and significantly improve label quality with 23.6% and
2.2% higher instance-level precision and recall, and 2.0% higher bounding box
IoU. LATTE is open-sourced at https://github.com/bernwang/latte.
Hierarchical Meta Learning
Authors: Yingtian Zou, Jiashi Feng
Date: 2019-04-19 00:00:00
Tasks: Few-Shot Learning, Meta-Learning

Meta learning is a promising solution to few-shot learning problems. However,
existing meta learning methods are restricted to the scenarios where training
and application tasks share the same out-put structure. To obtain a meta model
applicable to the tasks with new structures, it is required to collect new
training data and repeat the time-consuming meta training procedure. This makes
them inefficient or even inapplicable in learning to solve heterogeneous
few-shot learning tasks. We thus develop a novel and principled
HierarchicalMeta Learning (HML) method. Different from existing methods that
only focus on optimizing the adaptability of a meta model to similar tasks, HML
also explicitly optimizes its generalizability across heterogeneous tasks. To
this end, HML first factorizes a set of similar training tasks into
heterogeneous ones and trains the meta model over them at two levels to
maximize adaptation and generalization performance respectively. The resultant
model can then directly generalize to new tasks. Extensive experiments on
few-shot classification and regression problems clearly demonstrate the
superiority of HML over fine-tuning and state-of-the-art meta learning
approaches in terms of generalization across heterogeneous tasks.
Implicit regularization for deep neural networks driven by an Ornstein-Uhlenbeck like process
Authors: Guy Blanc, Neha Gupta, Gregory Valiant, Paul Valiant
Date: 2019-04-19 00:00:00

We consider networks, trained via stochastic gradient descent to minimize $\ell_2$ loss, with the training labels perturbed by independent noise at each iteration. We characterize the behavior of the training dynamics near any parameter vector that achieves zero training error, in terms of an implicit regularization term corresponding to the sum over the data points, of the squared $\ell_2$ norm of the gradient of the model with respect to the parameter vector, evaluated at each data point. This holds for networks of any connectivity, width, depth, and choice of activation function. We interpret this implicit regularization term for three simple settings: matrix sensing, two layer ReLU networks trained on one-dimensional data, and two layer networks with sigmoid activations trained on a single datapoint. For these settings, we show why this new and general implicit regularization effect drives the networks towards "simple" models.
EmbraceNet: A robust deep learning architecture for multimodal classification
Authors: Jun-Ho Choi, Jong-Seok Lee
Date: 2019-04-19 00:00:00
Tasks: Classification, Deep Learning, General Classification

Classification using multimodal data arises in many machine learning
applications. It is crucial not only to model cross-modal relationship
effectively but also to ensure robustness against loss of part of data or
modalities. In this paper, we propose a novel deep learning-based multimodal
fusion architecture for classification tasks, which guarantees compatibility
with any kind of learning models, deals with cross-modal information carefully,
and prevents performance degradation due to partial absence of data. We employ
two datasets for multimodal classification tasks, build models based on our
architecture and other state-of-the-art models, and analyze their performance
on various situations. The results show that our architecture outperforms the
other multimodal fusion architectures when some parts of data are not
available.
Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT
Authors: Shijie Wu, Mark Dredze
Date: 2019-04-19 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Cross-Lingual NER, Cross-Lingual Transfer, Dependency Parsing, Document Classification, Natural Language Inference, NER, POS, POS Tagging, Zero-Shot Cross-Lingual Transfer

Pretrained contextual representation models (Peters et al., 2018; Devlin et al., 2018) have pushed forward the state-of-the-art on many NLP tasks. A new release of BERT (Devlin, 2018) includes a model simultaneously pretrained on 104 languages with impressive performance for zero-shot cross-lingual transfer on a natural language inference task. This paper explores the broader cross-lingual potential of mBERT (multilingual) as a zero shot language transfer model on 5 NLP tasks covering a total of 39 languages from various language families: NLI, document classification, NER, POS tagging, and dependency parsing. We compare mBERT with the best-published methods for zero-shot cross-lingual transfer and find mBERT competitive on each task. Additionally, we investigate the most effective strategy for utilizing mBERT in this manner, determine to what extent mBERT generalizes away from language specific features, and measure factors that influence cross-lingual transfer.
Suggestion Mining from Online Reviews using ULMFiT
Authors: Sarthak Anand, Debanjan Mahata, Kartik Aggarwal, Laiba Mehnaz, Simra Shahid, Haimin Zhang, Yaman Kumar, Rajiv Ratn Shah, Karan Uppal
Date: 2019-04-19 00:00:00
Tasks: Classification, General Classification, Language Modeling, Language Modelling, Sentence, Suggestion mining, text-classification, Text Classification

In this paper we present our approach and the system description for Sub Task
A of SemEval 2019 Task 9: Suggestion Mining from Online Reviews and Forums.
Given a sentence, the task asks to predict whether the sentence consists of a
suggestion or not. Our model is based on Universal Language Model Fine-tuning
for Text Classification. We apply various pre-processing techniques before
training the language and the classification model. We further provide detailed
analysis of the results obtained using the trained model. Our team ranked 10th
out of 34 participants, achieving an F1 score of 0.7011. We publicly share our
implementation at https://github.com/isarth/SemEval9_MIDAS
Advanced Deep Convolutional Neural Network Approaches for Digital Pathology Image Analysis: a comprehensive evaluation with different use cases
Authors: Md Zahangir Alom, Theus Aspiras, Tarek M. Taha, Vijayan K. Asari, TJ Bowen, Dave Billiter, Simon Arkell
Date: 2019-04-19 00:00:00
Tasks: Classification, General Classification, Medical Image Analysis, Mitosis Detection, Segmentation, Specificity

Deep Learning (DL) approaches have been providing state-of-the-art
performance in different modalities in the field of medical imagining including
Digital Pathology Image Analysis (DPIA). Out of many different DL approaches,
Deep Convolutional Neural Network (DCNN) technique provides superior
performance for classification, segmentation, and detection tasks. Most of the
task in DPIA problems are somehow possible to solve with classification,
segmentation, and detection approaches. In addition, sometimes pre and
post-processing methods are applied for solving some specific type of problems.
Recently, different DCNN models including Inception residual recurrent CNN
(IRRCNN), Densely Connected Recurrent Convolution Network (DCRCN), Recurrent
Residual U-Net (R2U-Net), and R2U-Net based regression model (UD-Net) have
proposed and provide state-of-the-art performance for different computer vision
and medical image analysis tasks. However, these advanced DCNN models have not
been explored for solving different problems related to DPIA. In this study, we
have applied these DCNN techniques for solving different DPIA problems and
evaluated on different publicly available benchmark datasets for seven
different tasks in digital pathology including lymphoma classification,
Invasive Ductal Carcinoma (IDC) detection, nuclei segmentation, epithelium
segmentation, tubule segmentation, lymphocyte detection, and mitosis detection.
The experimental results are evaluated with different performance metrics such
as sensitivity, specificity, accuracy, F1-score, Receiver Operating
Characteristics (ROC) curve, dice coefficient (DC), and Means Squired Errors
(MSE). The results demonstrate superior performance for classification,
segmentation, and detection tasks compared to existing machine learning and
DCNN based approaches.
Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts
Authors: Julia Kruk, Jonah Lubin, Karan Sikka, Xiao Lin, Dan Jurafsky, Ajay Divakaran
Date: 2019-04-19 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Intent Detection

Computing author intent from multimodal data like Instagram posts requires modeling a complex relationship between text and image. For example, a caption might evoke an ironic contrast with the image, so neither caption nor image is a mere transcript of the other. Instead they combine -- via what has been called meaning multiplication -- to create a new meaning that has a more complex relation to the literal meanings of text and image. Here we introduce a multimodal dataset of 1299 Instagram posts labeled for three orthogonal taxonomies: the authorial intent behind the image-caption pair, the contextual relationship between the literal meanings of the image and caption, and the semiotic relationship between the signified meanings of the image and caption. We build a baseline deep multimodal classifier to validate the taxonomy, showing that employing both text and image improves intent detection by 9.6% compared to using only the image modality, demonstrating the commonality of non-intersective meaning multiplication. The gain with multimodality is greatest when the image and caption diverge semiotically. Our dataset offers a new resource for the study of the rich meanings that result from pairing text and image.
Identifying Offensive Posts and Targeted Offense from Twitter
Authors: Haimin Zhang, Debanjan Mahata, Simra Shahid, Laiba Mehnaz, Sarthak Anand, Yaman Singla, Rajiv Ratn Shah, Karan Uppal
Date: 2019-04-19 00:00:00

In this paper we present our approach and the system description for Sub-task
A and Sub Task B of SemEval 2019 Task 6: Identifying and Categorizing Offensive
Language in Social Media. Sub-task A involves identifying if a given tweet is
offensive or not, and Sub Task B involves detecting if an offensive tweet is
targeted towards someone (group or an individual). Our models for Sub-task A is
based on an ensemble of Convolutional Neural Network, Bidirectional LSTM with
attention, and Bidirectional LSTM + Bidirectional GRU, whereas for Sub-task B,
we rely on a set of heuristics derived from the training data and manual
observation. We provide detailed analysis of the results obtained using the
trained models. Our team ranked 5th out of 103 participants in Sub-task A,
achieving a macro F1 score of 0.807, and ranked 8th out of 75 participants in
Sub Task B achieving a macro F1 of 0.695.
A Hybrid Retrieval-Generation Neural Conversation Model
Authors: Liu Yang, Junjie Hu, Minghui Qiu, Chen Qu, Jianfeng Gao, W. Bruce Croft, Xiaodong Liu, Yelong Shen, Jingjing Liu
Date: 2019-04-19 00:00:00
Tasks: Diversity, model, Retrieval, Text Generation, Text Retrieval

Intelligent personal assistant systems that are able to have multi-turn conversations with human users are becoming increasingly popular. Most previous research has been focused on using either retrieval-based or generation-based methods to develop such systems. Retrieval-based methods have the advantage of returning fluent and informative responses with great diversity. However, the performance of the methods is limited by the size of the response repository. On the other hand, generation-based methods can produce highly coherent responses on any topics. But the generated responses are often generic and not informative due to the lack of grounding knowledge. In this paper, we propose a hybrid neural conversation model that combines the merits of both response retrieval and generation methods. Experimental results on Twitter and Foursquare data show that the proposed model outperforms both retrieval-based methods and generation-based methods (including a recently proposed knowledge-grounded neural conversation model) under both automatic evaluation metrics and human evaluation. We hope that the findings in this study provide new insights on how to integrate text retrieval and text generation models for building conversation systems.
Emergence of Compositional Language with Deep Generational Transmission
Authors: Michael Cogswell, Jiasen Lu, Stefan Lee, Devi Parikh, Dhruv Batra
Date: 2019-04-19 00:00:00
Proceeding: ICLR 2020 1
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent work has studied the emergence of language among deep reinforcement learning agents that must collaborate to solve a task. Of particular interest are the factors that cause language to be compositional -- i.e., express meaning by combining words which themselves have meaning. Evolutionary linguists have found that in addition to structural priors like those already studied in deep learning, the dynamics of transmitting language from generation to generation contribute significantly to the emergence of compositionality. In this paper, we introduce these cultural evolutionary dynamics into language emergence by periodically replacing agents in a population to create a knowledge gap, implicitly inducing cultural transmission of language. We show that this implicit cultural transmission encourages the resulting languages to exhibit better compositional generalization.
Semi-Supervised First-Person Activity Recognition in Body-Worn Video
Authors: Honglin Chen, Hao Li, Alexander Song, Matt Haberland, Osman Akar, Adam Dhillon, Tiankuang Zhou, Andrea L. Bertozzi, P. Jeffrey Brantingham
Date: 2019-04-19 00:00:00
Tasks: Activity Recognition

Body-worn cameras are now commonly used for logging daily life, sports, and
law enforcement activities, creating a large volume of archived footage. This
paper studies the problem of classifying frames of footage according to the
activity of the camera-wearer with an emphasis on application to real-world
police body-worn video. Real-world datasets pose a different set of challenges
from existing egocentric vision datasets: the amount of footage of different
activities is unbalanced, the data contains personally identifiable
information, and in practice it is difficult to provide substantial training
footage for a supervised approach. We address these challenges by extracting
features based exclusively on motion information then segmenting the video
footage using a semi-supervised classification algorithm. On publicly available
datasets, our method achieves results comparable to, if not better than,
supervised and/or deep learning methods using a fraction of the training data.
It also shows promising results on real-world police body-worn video.
Random Fragments Classification of Microbial Marker Clades with Multi-class SVM and N-Best Algorithm
Authors: Jingwei Liu
Date: 2019-04-19 00:00:00
Tasks: General Classification, Time Series, Time Series Analysis

Microbial clades modeling is a challenging problem in biology based on
microarray genome sequences, especially in new species gene isolates discovery
and category. Marker family genome sequences play important roles in describing
specific microbial clades within species, a framework of support vector machine
(SVM) based microbial species classification with N-best algorithm is
constructed to classify the centroid marker genome fragments randomly generated
from marker genome sequences on MetaRef. A time series feature extraction
method is proposed by segmenting the centroid gene sequences and mapping into
different dimensional spaces. Two ways of data splitting are investigated
according to random splitting fragments along genome sequence (DI) , or
separating genome sequences into two parts (DII).Two strategies of fragments
recognition tasks, dimension-by-dimension and sequence--by--sequence, are
investigated. The k-mer size selection, overlap of segmentation and effects of
random split percents are also discussed. Experiments on 12390 maker genome
sequences belonging to marker families of 17 species from MetaRef show that,
both for DI and DII in dimension-by-dimension and sequence-by-sequence
recognition, the recognition accuracy rates can achieve above 28\% in top-1
candidate, and above 91\% in top-10 candidate both on training and testing sets
overall.
Feature Forwarding for Efficient Single Image Dehazing
Authors: Peter Morales, Tzofi Klinghoffer, Seung Jae Lee
Date: 2019-04-19 00:00:00
Tasks: Decision Making, Decoder, Image Cropping, Image Dehazing, Single Image Dehazing, Super-Resolution

Haze degrades content and obscures information of images, which can negatively impact vision-based decision-making in real-time systems. In this paper, we propose an efficient fully convolutional neural network (CNN) image dehazing method designed to run on edge graphical processing units (GPUs). We utilize three variants of our architecture to explore the dependency of dehazed image quality on parameter count and model design. The first two variants presented, a small and big version, make use of a single efficient encoder-decoder convolutional feature extractor. The final variant utilizes a pair of encoder-decoders for atmospheric light and transmission map estimation. Each variant ends with an image refinement pyramid pooling network to form the final dehazed image. For the big variant of the single-encoder network, we demonstrate state-of-the-art performance on the NYU Depth dataset. For the small variant, we maintain competitive performance on the super-resolution O/I-HAZE datasets without the need for image cropping. Finally, we examine some challenges presented by the Dense-Haze dataset when leveraging CNN architectures for dehazing of dense haze imagery and examine the impact of loss function selection on image quality. Benchmarks are included to show the feasibility of introducing this approach into real-time systems.
Feature Fusion for Online Mutual Knowledge Distillation
Authors: Jangho Kim, Minsung Hyun, Inseop Chung, Nojun Kwak
Date: 2019-04-19 00:00:00
Tasks: Knowledge Distillation

We propose a learning framework named Feature Fusion Learning (FFL) that efficiently trains a powerful classifier through a fusion module which combines the feature maps generated from parallel neural networks. Specifically, we train a number of parallel neural networks as sub-networks, then we combine the feature maps from each sub-network using a fusion module to create a more meaningful feature map. The fused feature map is passed into the fused classifier for overall classification. Unlike existing feature fusion methods, in our framework, an ensemble of sub-network classifiers transfers its knowledge to the fused classifier and then the fused classifier delivers its knowledge back to each sub-network, mutually teaching one another in an online-knowledge distillation manner. This mutually teaching system not only improves the performance of the fused classifier but also obtains performance gain in each sub-network. Moreover, our model is more beneficial because different types of network can be used for each sub-network. We have performed a variety of experiments on multiple datasets such as CIFAR-10, CIFAR-100 and ImageNet and proved that our method is more effective than other alternative methods in terms of performance of both sub-networks and the fused classifier.
Disagreement-based Active Learning in Online Settings
Authors: Boshuang Huang, Sudeep Salgia, Qing Zhao
Date: 2019-04-19 00:00:00
Tasks: Active Learning, General Classification, Learning Theory

We study online active learning for classifying streaming instances within the framework of statistical learning theory. At each time, the learner either queries the label of the current instance or predicts the label based on past seen examples. The objective is to minimize the number of queries while constraining the number of prediction errors over a horizon of length $T$. We develop a disagreement-based online learning algorithm for a general hypothesis space and under the Tsybakov noise. We show that the proposed algorithm has a label complexity of $O(dT^{\frac{2-2\alpha}{2-\alpha}}\log^2 T)$ under a constraint of bounded regret in terms of classification errors, where $d$ is the VC dimension of the hypothesis space and $\alpha$ is the Tsybakov noise parameter. We further establish a matching (up to a poly-logarithmic factor) lower bound, demonstrating the order optimality of the proposed algorithm. We address the tradeoff between label complexity and regret and show that the algorithm can be modified to operate at a different point on the tradeoff curve.