October 15, 2019

2303 words 11 mins read

Paper Group NANR 191

Investigating Audio, Video, and Text Fusion Methods for End-to-End Automatic Personality Prediction. Aspect and Sentiment Aware Abstractive Review Summarization. Annotating If the Authors of a Tweet are Located at the Locations They Tweet About. Context-sensitive Natural Language Generation for robot-assisted second language tutoring. Shaping a soc …

Investigating Audio, Video, and Text Fusion Methods for End-to-End Automatic Personality Prediction


Title	Investigating Audio, Video, and Text Fusion Methods for End-to-End Automatic Personality Prediction
Authors	Onno Kampman, Elham J. Barezi, Dario Bertero, Pascale Fung
Abstract	We propose a tri-modal architecture to predict Big Five personality trait scores from video clips with different channels for audio, text, and video data. For each channel, stacked Convolutional Neural Networks are employed. The channels are fused both on decision-level and by concatenating their respective fully connected layers. It is shown that a multimodal fusion approach outperforms each single modality channel, with an improvement of 9.4{%} over the best individual modality (video). Full backpropagation is also shown to be better than a linear combination of modalities, meaning complex interactions between modalities can be leveraged to build better models. Furthermore, we can see the prediction relevance of each modality for each trait. The described model can be used to increase the emotional intelligence of virtual agents.
Tasks
Published	2018-07-01
URL	https://www.aclweb.org/anthology/P18-2096/
PDF	https://www.aclweb.org/anthology/P18-2096
PWC	https://paperswithcode.com/paper/investigating-audio-video-and-text-fusion
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Aspect and Sentiment Aware Abstractive Review Summarization


Title	Aspect and Sentiment Aware Abstractive Review Summarization
Authors	Min Yang, Qiang Qu, Ying Shen, Qiao Liu, Wei Zhao, Jia Zhu
Abstract	Review text has been widely studied in traditional tasks such as sentiment analysis and aspect extraction. However, to date, no work is towards the abstractive review summarization that is essential for business organizations and individual consumers to make informed decisions. This work takes the lead to study the aspect/sentiment-aware abstractive review summarization by exploring multi-factor attentions. Specifically, we propose an interactive attention mechanism to interactively learns the representations of context words, sentiment words and aspect words within the reviews, acted as an encoder. The learned sentiment and aspect representations are incorporated into the decoder to generate aspect/sentiment-aware review summaries via an attention fusion network. In addition, the abstractive summarizer is jointly trained with the text categorization task, which helps learn a category-specific text encoder, locating salient aspect information and exploring the variations of style and wording of content with respect to different text categories. The experimental results on a real-life dataset demonstrate that our model achieves impressive results compared to other strong competitors.
Tasks	Abstractive Text Summarization, Aspect Extraction, Machine Translation, Sentiment Analysis, Text Categorization, Text Summarization
Published	2018-08-01
URL	https://www.aclweb.org/anthology/C18-1095/
PDF	https://www.aclweb.org/anthology/C18-1095
PWC	https://paperswithcode.com/paper/aspect-and-sentiment-aware-abstractive-review
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Annotating If the Authors of a Tweet are Located at the Locations They Tweet About


Title	Annotating If the Authors of a Tweet are Located at the Locations They Tweet About
Authors	Vivek Doudagiri, Alakan Vempala, a, Eduardo Blanco
Abstract
Tasks	Named Entity Recognition
Published	2018-05-01
URL	https://www.aclweb.org/anthology/L18-1280/
PDF	https://www.aclweb.org/anthology/L18-1280
PWC	https://paperswithcode.com/paper/annotating-if-the-authors-of-a-tweet-are
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Context-sensitive Natural Language Generation for robot-assisted second language tutoring


Title	Context-sensitive Natural Language Generation for robot-assisted second language tutoring
Authors	Bram Willemsen, Jan de Wit, Emiel Krahmer, Mirjam de Haas, Paul Vogt
Abstract	This paper describes the L2TOR intelligent tutoring system (ITS), focusing primarily on its output generation module. The L2TOR ITS is developed for the purpose of investigating the efficacy of robot-assisted second language tutoring in early childhood. We explain the process of generating contextually-relevant utterances, such as task-specific feedback messages, and discuss challenges regarding multimodality and multilingualism for situated natural language generation from a robot tutoring perspective.
Tasks	Text Generation
Published	2018-11-01
URL	https://www.aclweb.org/anthology/W18-6901/
PDF	https://www.aclweb.org/anthology/W18-6901
PWC	https://paperswithcode.com/paper/context-sensitive-natural-language-generation
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Title	Shaping a social robot’s humor with Natural Language Generation and socially-aware reinforcement learning
Authors	Hannes Ritschel, Elisabeth Andr{'e}
Abstract	Humor is an important aspect in human interaction to regulate conversations, increase interpersonal attraction and trust. For social robots, humor is one aspect to make interactions more natural, enjoyable, and to increase credibility and acceptance. In combination with appropriate non-verbal behavior, natural language generation offers the ability to create content on-the-fly. This work outlines the building-blocks for providing an individual, multimodal interaction experience by shaping the robot{'}s humor with the help of Natural Language Generation and Reinforcement Learning based on human social signals.
Tasks	Text Generation
Published	2018-11-01
URL	https://www.aclweb.org/anthology/W18-6903/
PDF	https://www.aclweb.org/anthology/W18-6903
PWC	https://paperswithcode.com/paper/shaping-a-social-robots-humor-with-natural
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Ego-CNN: An Ego Network-based Representation of Graphs Detecting Critical Structures


Title	Ego-CNN: An Ego Network-based Representation of Graphs Detecting Critical Structures
Authors	Ruo-Chun Tzeng, Shan-Hung Wu
Abstract	While existing graph embedding models can generate useful embedding vectors that perform well on graph-related tasks, what valuable information can be jointly learned by a graph embedding model is less discussed. In this paper, we consider the possibility of detecting critical structures by a graph embedding model. We propose Ego-CNN to embed graph, which works in a local-to-global manner to take advantages of CNNs that gradually expanding the detectable local regions on the graph as the network depth increases. Critical structures can be detected if Ego-CNN is combined with a supervised task model. We show that Ego-CNN is (1) competitive to state-of-the-art graph embeddings models, (2) can nicely work with CNNs visualization techniques to show the detected structures, and (3) is efficient and can incorporate with scale-free priors, which commonly occurs in social network datasets, to further improve the training efficiency.
Tasks	Graph Embedding
Published	2018-01-01
URL	https://openreview.net/forum?id=SkBHr1WRW
PDF	https://openreview.net/pdf?id=SkBHr1WRW
PWC	https://paperswithcode.com/paper/ego-cnn-an-ego-network-based-representation
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An Automatic Error Tagger for German


Title	An Automatic Error Tagger for German
Authors	Inga Kempfert, Christine K{"o}hn
Abstract
Tasks	Grammatical Error Correction
Published	2018-11-01
URL	https://www.aclweb.org/anthology/W18-7104/
PDF	https://www.aclweb.org/anthology/W18-7104
PWC	https://paperswithcode.com/paper/an-automatic-error-tagger-for-german
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A Semantic Role-based Approach to Open-Domain Automatic Question Generation


Title	A Semantic Role-based Approach to Open-Domain Automatic Question Generation
Authors	Michael Flor, Brian Riordan
Abstract	We present a novel rule-based system for automatic generation of factual questions from sentences, using semantic role labeling (SRL) as the main form of text analysis. The system is capable of generating both wh-questions and yes/no questions from the same semantic analysis. We present an extensive evaluation of the system and compare it to a recent neural network architecture for question generation. The SRL-based system outperforms the neural system in both average quality and variety of generated questions.
Tasks	Dependency Parsing, Question Generation, Reading Comprehension, Semantic Role Labeling
Published	2018-06-01
URL	https://www.aclweb.org/anthology/W18-0530/
PDF	https://www.aclweb.org/anthology/W18-0530
PWC	https://paperswithcode.com/paper/a-semantic-role-based-approach-to-open-domain
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Single Image Water Hazard Detection using FCN with Reflection Attention Units


Title	Single Image Water Hazard Detection using FCN with Reflection Attention Units
Authors	Xiaofeng Han, Chuong Nguyen, Shaodi You, Jianfeng Lu
Abstract	Water bodies, such as puddles and flooded areas, on and off road pose significant risks to autonomous cars. Detecting water from moving camera is a challenging task as water surface is highly refractive, and its appearance varies with viewing angle, surrounding scene, weather conditions. In this paper, we present a water puddle detection method based on a Fully Convolutional Network (FCN) with our newly proposed Reflection Attention Units (RAUs). An RAU is a deep network unit designed to embody the physics of reflection on water surface from sky and nearby scene. To verify the performance of our proposed method, we collect 11455 color stereo images with polarizers, and 985 of left images are annotated and divided into 2 datasets: On Road (ONR) dataset and Off Road (OFR) dataset. We show that FCN-8s with RAUs improves significantly precision and recall metrics as compared to FCN-8s, DeepLab V2 and Gaussian Mixture Model (GMM). We also show that focal loss function can improve the performance of FCN-8s network due to the extreme imbalance of water versus ground classification problem.
Tasks
Published	2018-09-01
URL	http://openaccess.thecvf.com/content_ECCV_2018/html/Xiaofeng_Han_Single_Image_Water_ECCV_2018_paper.html
PDF	http://openaccess.thecvf.com/content_ECCV_2018/papers/Xiaofeng_Han_Single_Image_Water_ECCV_2018_paper.pdf
PWC	https://paperswithcode.com/paper/single-image-water-hazard-detection-using-fcn
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Faster Reinforcement Learning with Expert State Sequences


Title	Faster Reinforcement Learning with Expert State Sequences
Authors	Xiaoxiao Guo, Shiyu Chang, Mo Yu, Miao Liu, Gerald Tesauro
Abstract	Imitation learning relies on expert demonstrations. Existing approaches often re- quire that the complete demonstration data, including sequences of actions and states are available. In this paper, we consider a realistic and more difficult sce- nario where a reinforcement learning agent only has access to the state sequences of an expert, while the expert actions are not available. Inferring the unseen ex- pert actions in a stochastic environment is challenging and usually infeasible when combined with a large state space. We propose a novel policy learning method which only utilizes the expert state sequences without inferring the unseen ac- tions. Specifically, our agent first learns to extract useful sub-goal information from the state sequences of the expert and then utilizes the extracted sub-goal information to factorize the action value estimate over state-action pairs and sub- goals. The extracted sub-goals are also used to synthesize guidance rewards in the policy learning. We evaluate our agent on five Doom tasks. Our empirical results show that the proposed method significantly outperforms the conventional DQN method.
Tasks	Imitation Learning
Published	2018-01-01
URL	https://openreview.net/forum?id=BJ7d0fW0b
PDF	https://openreview.net/pdf?id=BJ7d0fW0b
PWC	https://paperswithcode.com/paper/faster-reinforcement-learning-with-expert
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Within and Between-Person Differences in Language Used Across Anxiety Support and Neutral Reddit Communities


Title	Within and Between-Person Differences in Language Used Across Anxiety Support and Neutral Reddit Communities
Authors	Molly Ireland, Micah Iserman
Abstract
Tasks
Published	2018-06-01
URL	https://www.aclweb.org/anthology/papers/W18-0620/w18-0620
PDF	https://www.aclweb.org/anthology/W18-0620
PWC	https://paperswithcode.com/paper/within-and-between-person-differences-in
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Explaining Deep Learning Models – A Bayesian Non-parametric Approach


Title	Explaining Deep Learning Models – A Bayesian Non-parametric Approach
Authors	Wenbo Guo, Sui Huang, Yunzhe Tao, Xinyu Xing, Lin Lin
Abstract	Understanding and interpreting how machine learning (ML) models make decisions have been a big challenge. While recent research has proposed various technical approaches to provide some clues as to how an ML model makes individual predictions, they cannot provide users with an ability to inspect a model as a complete entity. In this work, we propose a novel technical approach that augments a Bayesian non-parametric regression mixture model with multiple elastic nets. Using the enhanced mixture model, we can extract generalizable insights for a target model through a global approximation. To demonstrate the utility of our approach, we evaluate it on different ML models in the context of image recognition. The empirical results indicate that our proposed approach not only outperforms the state-of-the-art techniques in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of the target ML models.
Tasks
Published	2018-12-01
URL	http://papers.nips.cc/paper/7703-explaining-deep-learning-models-a-bayesian-non-parametric-approach
PDF	http://papers.nips.cc/paper/7703-explaining-deep-learning-models-a-bayesian-non-parametric-approach.pdf
PWC	https://paperswithcode.com/paper/explaining-deep-learning-models-a-bayesian-1
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ADMM and Accelerated ADMM as Continuous Dynamical Systems


Title	ADMM and Accelerated ADMM as Continuous Dynamical Systems
Authors	Guilherme Franca, Daniel Robinson, Rene Vidal
Abstract	Recently, there has been an increasing interest in using tools from dynamical systems to analyze the behavior of simple optimization algorithms such as gradient descent and accelerated variants. This paper strengthens such connections by deriving the differential equations that model the continuous limit of the sequence of iterates generated by the alternating direction method of multipliers, as well as an accelerated variant. We employ the direct method of Lyapunov to analyze the stability of critical points of the dynamical systems and to obtain associated convergence rates.
Tasks
Published	2018-07-01
URL	https://icml.cc/Conferences/2018/Schedule?showEvent=2396
PDF	http://proceedings.mlr.press/v80/franca18a/franca18a.pdf
PWC	https://paperswithcode.com/paper/admm-and-accelerated-admm-as-continuous
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Rolling Shutter and Radial Distortion Are Features for High Frame Rate Multi-Camera Tracking


Title	Rolling Shutter and Radial Distortion Are Features for High Frame Rate Multi-Camera Tracking
Authors	Akash Bapat, True Price, Jan-Michael Frahm
Abstract	Traditionally, camera-based tracking approaches have treated rolling shutter and radial distortion as imaging artifacts that have to be overcome and corrected for in order to apply standard camera models and scene reconstruction methods. In this paper, we introduce a novel multi-camera tracking approach that for the first time jointly leverages the information introduced by rolling shutter and radial distortion as a feature to achieve superior performance with respect to high-frequency camera pose estimation. In particular, our system is capable of attaining high tracking rates that were previously unachievable. Our approach explicitly leverages rolling shutter capture and radial distortion to process individual rows, rather than entire image frames, for accurate camera motion estimation. We estimate a per-row 6 DoF pose of a rolling shutter camera by tracking multiple points on a radially distorted row whose rays span a curved surface in 3D space. Although tracking systems for rolling shutter cameras exist, we are the first to leverage radial distortion to measure a per-row pose – enabling us to use less than half the number of cameras required by the previous state of the art. We validate our system on both synthetic and real imagery.
Tasks	Motion Estimation, Pose Estimation
Published	2018-06-01
URL	http://openaccess.thecvf.com/content_cvpr_2018/html/Bapat_Rolling_Shutter_and_CVPR_2018_paper.html
PDF	http://openaccess.thecvf.com/content_cvpr_2018/papers/Bapat_Rolling_Shutter_and_CVPR_2018_paper.pdf
PWC	https://paperswithcode.com/paper/rolling-shutter-and-radial-distortion-are
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Tracking Multiple Objects Outside the Line of Sight Using Speckle Imaging


Title	Tracking Multiple Objects Outside the Line of Sight Using Speckle Imaging
Authors	Brandon M. Smith, Matthew O’Toole, Mohit Gupta
Abstract	This paper presents techniques for tracking non-line-of-sight (NLOS) objects using speckle imaging. We develop a novel speckle formation and motion model where both the sensor and the source view objects only indirectly via a diffuse wall. We show that this NLOS imaging scenario is analogous to direct LOS imaging with the wall acting as a virtual, bare (lens-less) sensor. This enables tracking of a single, rigidly moving NLOS object using existing speckle-based motion estimation techniques. However, when imaging multiple NLOS objects, the speckle components due to different objects are superimposed on the virtual bare sensor image, and cannot be analyzed separately for recovering the motion of individual objects. We develop a novel clustering algorithm based on the statistical and geometrical properties of speckle images, which enables identifying the motion trajectories of multiple, independently moving NLOS objects. We demonstrate, for the first time, tracking individual trajectories of multiple objects around a corner with extreme precision (< 10 microns) using only off-the-shelf imaging components.
Tasks	Motion Estimation
Published	2018-06-01
URL	http://openaccess.thecvf.com/content_cvpr_2018/html/Smith_Tracking_Multiple_Objects_CVPR_2018_paper.html
PDF	http://openaccess.thecvf.com/content_cvpr_2018/papers/Smith_Tracking_Multiple_Objects_CVPR_2018_paper.pdf
PWC	https://paperswithcode.com/paper/tracking-multiple-objects-outside-the-line-of
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