pwc-archive/papers-with-abstracts huggingface dataset:
It’s quality and quantity: the effect of the amount of comments on online suicidal posts
Authors: Daniel Low, Kelly Zuromski, Daniel Kessler, Satrajit S. Ghosh, Matthew K. Nock, Walter Dempsey
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (CINLP) 2021 11
Tasks: Causal Inference

Every day, individuals post suicide notes on social media asking for support, resources, and reasons to live. Some posts receive few comments while others receive many. While prior studies have analyzed whether specific responses are more or less helpful, it is not clear if the quantity of comments received is beneficial in reducing symptoms or in keeping the user engaged with the platform and hence with life. In the present study, we create a large dataset of users’ first r/SuicideWatch (SW) posts from Reddit (N=21,274), collect the comments as well as the user’s subsequent posts (N=1,615,699) to determine whether they post in SW again in the future. We use propensity score stratification, a causal inference method for observational data, and estimate whether the amount of comments —as a measure of social support— increases or decreases the likelihood of posting again on SW. One hypothesis is that receiving more comments may decrease the likelihood of the user posting in SW in the future, either by reducing symptoms or because comments from untrained peers may be harmful. On the contrary, we find that receiving more comments increases the likelihood a user will post in SW again. We discuss how receiving more comments is helpful, not by permanently relieving symptoms since users make another SW post and their second posts have similar mentions of suicidal ideation, but rather by reinforcing users to seek support and remain engaged with the platform. Furthermore, since receiving only 1 comment —the most common case— decreases the likelihood of posting again by 14% on average depending on the time window, it is important to develop systems that encourage more commenting.
Crowdsourcing Beyond Annotation: Case Studies in Benchmark Data Collection
Authors: Alane Suhr, Clara Vania, Nikita Nangia, Maarten Sap, Mark Yatskar, Samuel R. Bowman, Yoav Artzi
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11

Crowdsourcing from non-experts is one of the most common approaches to collecting data and annotations in NLP. Even though it is such a fundamental tool in NLP, crowdsourcing use is largely guided by common practices and the personal experience of researchers. Developing a theory of crowdsourcing use for practical language problems remains an open challenge. However, there are various principles and practices that have proven effective in generating high quality and diverse data. This tutorial exposes NLP researchers to such data collection crowdsourcing methods and principles through a detailed discussion of a diverse set of case studies. The selection of case studies focuses on challenging settings where crowdworkers are asked to write original text or otherwise perform relatively unconstrained work. Through these case studies, we discuss in detail processes that were carefully designed to achieve data with specific properties, for example to require logical inference, grounded reasoning or conversational understanding. Each case study focuses on data collection crowdsourcing protocol details that often receive limited attention in research presentations, for example in conferences, but are critical for research success.
Financial Opinion Mining
Authors: Chung-Chi Chen, Hen-Hsen Huang, Hsin-Hsi Chen
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: Opinion Mining

In this tutorial, we will show where we are and where we will be to those researchers interested in this topic. We divide this tutorial into three parts, including coarse-grained financial opinion mining, fine-grained financial opinion mining, and possible research directions. This tutorial starts by introducing the components in a financial opinion proposed in our research agenda and summarizes their related studies. We also highlight the task of mining customers’ opinions toward financial services in the FinTech industry, and compare them with usual opinions. Several potential research questions will be addressed. We hope the audiences of this tutorial will gain an overview of financial opinion mining and figure out their research directions.
Knowledge-Enriched Natural Language Generation
Authors: Wenhao Yu, Meng Jiang, Zhiting Hu, Qingyun Wang, Heng Ji, Nazneen Rajani
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: Text Generation

Knowledge-enriched text generation poses unique challenges in modeling and learning, driving active research in several core directions, ranging from integrated modeling of neural representations and symbolic information in the sequential/hierarchical/graphical structures, learning without direct supervisions due to the cost of structured annotation, efficient optimization and inference with massive and global constraints, to language grounding on multiple modalities, and generative reasoning with implicit commonsense knowledge and background knowledge. In this tutorial we will present a roadmap to line up the state-of-the-art methods to tackle these challenges on this cutting-edge problem. We will dive deep into various technical components: how to represent knowledge, how to feed knowledge into a generation model, how to evaluate generation results, and what are the remaining challenges?
Multi-Domain Multilingual Question Answering
Authors: Sebastian Ruder, Avi Sil
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: Cross-Lingual Transfer, Domain Adaptation, Multi-Task Learning, Question Answering, Transfer Learning

Question answering (QA) is one of the most challenging and impactful tasks in natural language processing. Most research in QA, however, has focused on the open-domain or monolingual setting while most real-world applications deal with specific domains or languages. In this tutorial, we attempt to bridge this gap. Firstly, we introduce standard benchmarks in multi-domain and multilingual QA. In both scenarios, we discuss state-of-the-art approaches that achieve impressive performance, ranging from zero-shot transfer learning to out-of-the-box training with open-domain QA systems. Finally, we will present open research problems that this new research agenda poses such as multi-task learning, cross-lingual transfer learning, domain adaptation and training large scale pre-trained multilingual language models.
Robustness and Adversarial Examples in Natural Language Processing
Authors: Kai-Wei Chang, He He, Robin Jia, Sameer Singh
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11

Recent studies show that many NLP systems are sensitive and vulnerable to a small perturbation of inputs and do not generalize well across different datasets. This lack of robustness derails the use of NLP systems in real-world applications. This tutorial aims at bringing awareness of practical concerns about NLP robustness. It targets NLP researchers and practitioners who are interested in building reliable NLP systems. In particular, we will review recent studies on analyzing the weakness of NLP systems when facing adversarial inputs and data with a distribution shift. We will provide the audience with a holistic view of 1) how to use adversarial examples to examine the weakness of NLP models and facilitate debugging; 2) how to enhance the robustness of existing NLP models and defense against adversarial inputs; and 3) how the consideration of robustness affects the real-world NLP applications used in our daily lives. We will conclude the tutorial by outlining future research directions in this area.
Syntax in End-to-End Natural Language Processing
Authors: Hai Zhao, Rui Wang, Kehai Chen
Date: 2021-11-01 00:00:00
Proceeding: EMNLP (ACL) 2021 11
Tasks: Machine Translation, NMT, Semantic Role Labeling, Translation

This tutorial surveys the latest technical progress of syntactic parsing and the role of syntax in end-to-end natural language processing (NLP) tasks, in which semantic role labeling (SRL) and machine translation (MT) are the representative NLP tasks that have always been beneficial from informative syntactic clues since a long time ago, though the advance from end-to-end deep learning models shows new results. In this tutorial, we will first introduce the background and the latest progress of syntactic parsing and SRL/NMT. Then, we will summarize the key evidence about the syntactic impacts over these two concerning tasks, and explore the behind reasons from both computational and linguistic backgrounds.
Lexically Aware Semi-Supervised Learning for OCR Post-Correction
Authors: Shruti Rijhwani, Daisy Rosenblum, Antonios Anastasopoulos, Graham Neubig
Date: 2021-11-04 00:00:00
Tasks: Language Modelling, Optical Character Recognition, Optical Character Recognition (OCR)

Much of the existing linguistic data in many languages of the world is locked away in non-digitized books and documents. Optical character recognition (OCR) can be used to produce digitized text, and previous work has demonstrated the utility of neural post-correction methods that improve the results of general-purpose OCR systems on recognition of less-well-resourced languages. However, these methods rely on manually curated post-correction data, which are relatively scarce compared to the non-annotated raw images that need to be digitized. In this paper, we present a semi-supervised learning method that makes it possible to utilize these raw images to improve performance, specifically through the use of self-training, a technique where a model is iteratively trained on its own outputs. In addition, to enforce consistency in the recognized vocabulary, we introduce a lexically-aware decoding method that augments the neural post-correction model with a count-based language model constructed from the recognized texts, implemented using weighted finite-state automata (WFSA) for efficient and effective decoding. Results on four endangered languages demonstrate the utility of the proposed method, with relative error reductions of 15-29%, where we find the combination of self-training and lexically-aware decoding essential for achieving consistent improvements. Data and code are available at https://shrutirij.github.io/ocr-el/.
DEEP COMPLEX-VALUED NEURAL BEAMFORMERS
Authors: Lukas Pfeifenberger, Franz Pernkopg
Date: 2019-05-01 00:00:00
Proceeding: ICASSP 2019 5
Tasks: Speech Enhancement

We propose a complex-valued deep neural network (cDNN) for speech enhancement and source separation. While existing end-to-end systems use complex-valued gradients to pass the training error to a real-valued DNN used for gain mask estimation, we use the full potential of complex-valued LSTMs, MLPs and activation functions to estimate complex-valued beamforming weights directly from complex-valued microphone array data. By doing so, our cDNN is able to locate and track different moving sources by exploiting the phase information in the data. In our experiments, we use a typical living room environment, mixtures of the WallStreet Journal
corpus, and YouTube noise. We compare our cDNN against the BeamformIt toolkit as a baseline, and a mask-based beamformer as a state-of-the-art reference system. We observed a signiﬁcant improvement in terms of PESQ, STOI and WER.
Acoustic Echo Cancellation with Cross-Domain Learning
Authors: Lukas Pfeifenberger, Matthias Zoehrer, Franz Pernkopf
Date: 2021-08-30 00:00:00
Proceeding: Interspeech 2021 8
Tasks: Acoustic echo cancellation

This   paper   proposes   the   Cross-Domain   Echo-Controller(CDEC),  submitted  to  the  Interspeech  2021  AEC-Challenge.The  algorithm  consists  of  three  building  blocks:  (i)  a  Time-Delay Compensation (TDC) module,  (ii) a frequency-domainblock-based Acoustic Echo Canceler (AEC), and (iii) a Time-Domain  Neural-Network  (TD-NN)  used  as  a  post-processor.Our system achieves an overall MOS score of 3.80, while onlyusing 2.1 million parameters at a system latency of 32ms.
Nonlinear Residual Echo Suppression using a Recurrent Neural Network
Authors: Lukas Pfeifenberger, Franz Pernkopf
Date: 2020-10-25 00:00:00
Proceeding: Interspeech 2020 10
Tasks: Acoustic echo cancellation

The  acoustic  front-end  of  hands-free  communication  de-vices introduces a variety of distortions to the linear echo pathbetween the loudspeaker and the microphone. While the ampli-fiers may introduce a memory-less non-linearity, mechanical vi-brations transmitted from the loudspeaker to the microphone viathe housing of the device introduce non-linarities with memory,which are much harder to compensate. These distortions signif-icantly limit the performance of linear Acoustic Echo Cancella-tion (AEC) algorithms. While there already exists a wide rangeof Residual Echo Suppressor (RES) techniques for individualuse cases, our contribution specifically aims at a low-resourceimplementation  that  is  also  real-time  capable.   The  proposedapproach is based on a small Recurrent Neural Network (RNN)which adds memory to the residual echo suppressor, enabling itto compensate both types of non-linear distortions. We evaluatethe performance of our system in terms of Echo Return Loss En-hancement (ERLE), Signal to Distortion Ratio (SDR) and WordError Rate (WER), obtained during realistic double-talk situa-tions.  Further, we compare the postfilter against a state-of-theart implementation. Finally, we analyze the numerical complex-ity of the overall system.
iVPF: Numerical Invertible Volume Preserving Flow for Efficient Lossless Compression
Authors: Changsu Cao, Jiaqi Hu, Wengang Zhang, Xusheng Xu, Dechin Chen, Fan Yu, Jun Li, Hanshi Hu, Dingshun Lv, Man-Hong Yung
Date: 2021-09-05 00:00:00
Proceeding: CVPR 2021 9

It is nontrivial to store rapidly growing big data nowadays, which demands high-performance lossless compression techniques. Likelihood-based generative models have witnessed their success on lossless compression, where flow based models are desirable in allowing exact data likelihood optimisation with bijective mappings. However, common continuous flows are in contradiction with the discreteness of coding schemes, which requires either 1) imposing strict constraints on flow models that degrades the performance or 2) coding numerous bijective mapping errors which reduces the efficiency. In this paper, we investigate volume preserving flows for lossless compression and show that a bijective mapping without error is possible. We propose Numerical Invertible Volume Preserving Flow (iVPF) which is derived from the general volume preserving flows. By introducing novel computation algorithms on flow models, an exact bijective mapping is achieved without any numerical error. We also propose a lossless compression algorithm based on iVPF. Experiments on various datasets show that the algorithm based on iVPF achieves state-of-the-art compression ratio over lightweight compression algorithms.
Transparency of Deep Neural Networks for Medical Image Analysis: A Review of Interpretability Methods
Authors: Zohaib Salahuddin, Henry C Woodruff, Avishek Chatterjee, Philippe Lambin
Date: 2021-11-01 00:00:00
Tasks: Decision Making, Medical Image Analysis

Artificial Intelligence has emerged as a useful aid in numerous clinical applications for diagnosis and treatment decisions. Deep neural networks have shown same or better performance than clinicians in many tasks owing to the rapid increase in the available data and computational power. In order to conform to the principles of trustworthy AI, it is essential that the AI system be transparent, robust, fair and ensure accountability. Current deep neural solutions are referred to as black-boxes due to a lack of understanding of the specifics concerning the decision making process. Therefore, there is a need to ensure interpretability of deep neural networks before they can be incorporated in the routine clinical workflow. In this narrative review, we utilized systematic keyword searches and domain expertise to identify nine different types of interpretability methods that have been used for understanding deep learning models for medical image analysis applications based on the type of generated explanations and technical similarities. Furthermore, we report the progress made towards evaluating the explanations produced by various interpretability methods. Finally we discuss limitations, provide guidelines for using interpretability methods and future directions concerning the interpretability of deep neural networks for medical imaging analysis.
Obstacle Avoidance for Drones Using a 3DVFH* Algorithm
Authors: Tanja Baumann
Date: 2018-04-04 00:00:00
Proceeding: not clear 2018 4

Unmanned Aerial Vehicles (UA V) possess a vast potential for various autonomous
applications such as surveying or delivery. When considering autonomous flight
operations one of the most essential requirements certainly is a reliable obstacle
avoidance mechanism. Obstacle avoidance is a very active field of research without
any conclusive solutions so far. This thesis introduces the 3DVFH* obstacle avoid-
ance algorithm suitable for real-time application on UAVs. The algorithm combines
the ideas behind the previously presented 3DVFH+ and the VFH* algorithm with
a novel memory strategy. The 3DVFH* algorithm computes obstacle avoidance
maneuvers in a purely reactive manner without the need to build a global map of
the environment. The memory strategy keeps track of previously seen obstacles by
propagating the previous polar histogram to the current location. Various features,
such as ground detection and safety mechanisms, have been implemented to increase
the robustness and render application on UAVs possible. The 3DVFH* algorithm
has been shown to effectively avoid obstacles in complex simulation scenarios ex-
hibiting a look-ahead capability. The ground detection was shown to be able to keep
a specified minimum distance to the ground. Real-world flight tests were performed
by running the 3DVFH* algorithm on-board the Intel®Aero Ready to Fly drone
equipped with only one forward facing camera. The drone was able to successfully
avoid an obstacle in all of the 26 test flights.
Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning
Authors: Wenlong Huang, Igor Mordatch, Pieter Abbeel, Deepak Pathak
Date: 2021-11-04 00:00:00
Tasks: Multi-Task Learning, Object, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Dexterous manipulation of arbitrary objects, a fundamental daily task for humans, has been a grand challenge for autonomous robotic systems. Although data-driven approaches using reinforcement learning can develop specialist policies that discover behaviors to control a single object, they often exhibit poor generalization to unseen ones. In this work, we show that policies learned by existing reinforcement learning algorithms can in fact be generalist when combined with multi-task learning and a well-chosen object representation. We show that a single generalist policy can perform in-hand manipulation of over 100 geometrically-diverse real-world objects and generalize to new objects with unseen shape or size. Interestingly, we find that multi-task learning with object point cloud representations not only generalizes better but even outperforms the single-object specialist policies on both training as well as held-out test objects. Video results at https://huangwl18.github.io/geometry-dex
Engagement Decision Support for Beyond Visual Range Air Combat
Authors: Joao P. A. Dantas, Andre N. Costa, Diego Geraldo, Marcos R. O. A. Maximo, Takashi Yoneyama
Date: 2021-11-04 00:00:00

This work aims to provide an engagement decision support tool for Beyond Visual Range (BVR) air combat in the context of Defensive Counter Air (DCA) missions. In BVR air combat, engagement decision refers to the choice of the moment the pilot engages a target by assuming an offensive stance and executing corresponding maneuvers. To model this decision, we use the Brazilian Air Force's Aerospace Simulation Environment (Ambiente de Simula\c{c}\~ao Aeroespacial - ASA in Portuguese), which generated 3,729 constructive simulations lasting 12 minutes each and a total of 10,316 engagements. We analyzed all samples by an operational metric called the DCA index, which represents, based on the experience of subject matter experts, the degree of success in this type of mission. This metric considers the distances of the aircraft of the same team and the opposite team, the point of Combat Air Patrol, and the number of missiles used. By defining the engagement status right before it starts and the average of the DCA index throughout the engagement, we create a supervised learning model to determine the quality of a new engagement. An algorithm based on decision trees, working with the XGBoost library, provides a regression model to predict the DCA index with a coefficient of determination close to 0.8 and a Root Mean Square Error of 0.05 that can furnish parameters to the BVR pilot to decide whether or not to engage. Thus, using data obtained through simulations, this work contributes by building a decision support system based on machine learning for BVR air combat.
Imagine Networks
Authors: Seokjun Kim, Jaeeun Jang, Hyeoncheol Kim
Date: 2021-11-04 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we introduce an imagine network that can simulate itself through artificial association networks. Association, deduction, and memory networks are learned, and a network is created by combining the discriminator and reinforcement learning models. This model can learn various datasets or data samples generated in environments and generate new data samples.
A System for General In-Hand Object Re-Orientation
Authors: Tao Chen, Jie Xu, Pulkit Agrawal
Date: 2021-11-04 00:00:00
Tasks: Object

In-hand object reorientation has been a challenging problem in robotics due to high dimensional actuation space and the frequent change in contact state between the fingers and the objects. We present a simple model-free framework that can learn to reorient objects with both the hand facing upwards and downwards. We demonstrate the capability of reorienting over 2000 geometrically different objects in both cases. The learned policies show strong zero-shot transfer performance on new objects. We provide evidence that these policies are amenable to real-world operation by distilling them to use observations easily available in the real world. The videos of the learned policies are available at: https://taochenshh.github.io/projects/in-hand-reorientation.
Unsupervised Learning of Compositional Energy Concepts
Authors: Yilun Du, Shuang Li, Yash Sharma, Joshua B. Tenenbaum, Igor Mordatch
Date: 2021-11-04 00:00:00
Proceeding: NeurIPS 2021 12
Tasks: Disentanglement, Unsupervised Image Decomposition

Humans are able to rapidly understand scenes by utilizing concepts extracted from prior experience. Such concepts are diverse, and include global scene descriptors, such as the weather or lighting, as well as local scene descriptors, such as the color or size of a particular object. So far, unsupervised discovery of concepts has focused on either modeling the global scene-level or the local object-level factors of variation, but not both. In this work, we propose COMET, which discovers and represents concepts as separate energy functions, enabling us to represent both global concepts as well as objects under a unified framework. COMET discovers energy functions through recomposing the input image, which we find captures independent factors without additional supervision. Sample generation in COMET is formulated as an optimization process on underlying energy functions, enabling us to generate images with permuted and composed concepts. Finally, discovered visual concepts in COMET generalize well, enabling us to compose concepts between separate modalities of images as well as with other concepts discovered by a separate instance of COMET trained on a different dataset. Code and data available at https://energy-based-model.github.io/comet/.