Paper Group ANR 82
A new nonlocal forward model for diffuse optical tomography. Independence Promoted Graph Disentangled Networks. A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization. Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation. Transforming Complex Sentences into a …
A new nonlocal forward model for diffuse optical tomography
| Title | A new nonlocal forward model for diffuse optical tomography |
| Authors | Wenqi Lu, Jinming Duan, Joshua Deepak Veesa, Iain B. Styles |
| Abstract | The forward model in diffuse optical tomography (DOT) describes how light propagates through a turbid medium. It is often approximated by a diffusion equation (DE) that is numerically discretized by the classical finite element method (FEM). We propose a nonlocal diffusion equation (NDE) as a new forward model for DOT, the discretization of which is carried out with an efficient graph-based numerical method (GNM). To quantitatively evaluate the new forward model, we first conduct experiments on a homogeneous slab, where the numerical accuracy of both NDE and DE is compared against the existing analytical solution. We further evaluate NDE by comparing its image reconstruction performance (inverse problem) to that of DE. Our experiments show that NDE is quantitatively comparable to DE and is up to 64% faster due to the efficient graph-based representation that can be implemented identically for geometries in different dimensions. |
| Tasks | Image Reconstruction |
| Published | 2019-06-03 |
| URL | https://arxiv.org/abs/1906.00882v1 |
| https://arxiv.org/pdf/1906.00882v1.pdf | |
| PWC | https://paperswithcode.com/paper/190600882 |
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Independence Promoted Graph Disentangled Networks
| Title | Independence Promoted Graph Disentangled Networks |
| Authors | Yanbei Liu, Xiao Wang, Shu Wu, Zhitao Xiao |
| Abstract | We address the problem of disentangled representation learning with independent latent factors in graph convolutional networks (GCNs). The current methods usually learn node representation by describing its neighborhood as a perceptual whole in a holistic manner while ignoring the entanglement of the latent factors. However, a real-world graph is formed by the complex interaction of many latent factors (e.g., the same hobby, education or work in social network). While little effort has been made toward exploring the disentangled representation in GCNs. In this paper, we propose a novel Independence Promoted Graph Disentangled Networks (IPGDN) to learn disentangled node representation while enhancing the independence among node representations. In particular, we firstly present disentangled representation learning by neighborhood routing mechanism, and then employ the Hilbert-Schmidt Independence Criterion (HSIC) to enforce independence between the latent representations, which is effectively integrated into a graph convolutional framework as a regularizer at the output layer. Experimental studies on real-world graphs validate our model and demonstrate that our algorithms outperform the state-of-the-arts by a wide margin in different network applications, including semi-supervised graph classification, graph clustering and graph visualization. |
| Tasks | Graph Classification, Graph Clustering, Representation Learning |
| Published | 2019-11-26 |
| URL | https://arxiv.org/abs/1911.11430v1 |
| https://arxiv.org/pdf/1911.11430v1.pdf | |
| PWC | https://paperswithcode.com/paper/independence-promoted-graph-disentangled |
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A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization
| Title | A Distribution Dependent and Independent Complexity Analysis of Manifold Regularization |
| Authors | Alexander Mey, Tom Viering, Marco Loog |
| Abstract | Manifold regularization is a commonly used technique in semi-supervised learning. It enforces the classification rule to be smooth with respect to the data-manifold. Here, we derive sample complexity bounds based on pseudo-dimension for models that add a convex data dependent regularization term to a supervised learning process, as is in particular done in Manifold regularization. We then compare the bound for those semi-supervised methods to purely supervised methods, and discuss a setting in which the semi-supervised method can only have a constant improvement, ignoring logarithmic terms. By viewing Manifold regularization as a kernel method we then derive Rademacher bounds which allow for a distribution dependent analysis. Finally we illustrate that these bounds may be useful for choosing an appropriate manifold regularization parameter in situations with very sparsely labeled data. |
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| Published | 2019-06-14 |
| URL | https://arxiv.org/abs/1906.06100v2 |
| https://arxiv.org/pdf/1906.06100v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-distribution-dependent-and-independent |
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Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation
| Title | Guided Curriculum Model Adaptation and Uncertainty-Aware Evaluation for Semantic Nighttime Image Segmentation |
| Authors | Christos Sakaridis, Dengxin Dai, Luc Van Gool |
| Abstract | Most progress in semantic segmentation reports on daytime images taken under favorable illumination conditions. We instead address the problem of semantic segmentation of nighttime images and improve the state-of-the-art, by adapting daytime models to nighttime without using nighttime annotations. Moreover, we design a new evaluation framework to address the substantial uncertainty of semantics in nighttime images. Our central contributions are: 1) a curriculum framework to gradually adapt semantic segmentation models from day to night via labeled synthetic images and unlabeled real images, both for progressively darker times of day, which exploits cross-time-of-day correspondences for the real images to guide the inference of their labels; 2) a novel uncertainty-aware annotation and evaluation framework and metric for semantic segmentation, designed for adverse conditions and including image regions beyond human recognition capability in the evaluation in a principled fashion; 3) the Dark Zurich dataset, which comprises 2416 unlabeled nighttime and 2920 unlabeled twilight images with correspondences to their daytime counterparts plus a set of 151 nighttime images with fine pixel-level annotations created with our protocol, which serves as a first benchmark to perform our novel evaluation. Experiments show that our guided curriculum adaptation significantly outperforms state-of-the-art methods on real nighttime sets both for standard metrics and our uncertainty-aware metric. Furthermore, our uncertainty-aware evaluation reveals that selective invalidation of predictions can lead to better results on data with ambiguous content such as our nighttime benchmark and profit safety-oriented applications which involve invalid inputs. |
| Tasks | Semantic Segmentation, Style Transfer |
| Published | 2019-01-17 |
| URL | https://arxiv.org/abs/1901.05946v2 |
| https://arxiv.org/pdf/1901.05946v2.pdf | |
| PWC | https://paperswithcode.com/paper/semantic-nighttime-image-segmentation-with |
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Transforming Complex Sentences into a Semantic Hierarchy
| Title | Transforming Complex Sentences into a Semantic Hierarchy |
| Authors | Christina Niklaus, Matthias Cetto, Andre Freitas, Siegfried Handschuh |
| Abstract | We present an approach for recursively splitting and rephrasing complex English sentences into a novel semantic hierarchy of simplified sentences, with each of them presenting a more regular structure that may facilitate a wide variety of artificial intelligence tasks, such as machine translation (MT) or information extraction (IE). Using a set of hand-crafted transformation rules, input sentences are recursively transformed into a two-layered hierarchical representation in the form of core sentences and accompanying contexts that are linked via rhetorical relations. In this way, the semantic relationship of the decomposed constituents is preserved in the output, maintaining its interpretability for downstream applications. Both a thorough manual analysis and automatic evaluation across three datasets from two different domains demonstrate that the proposed syntactic simplification approach outperforms the state of the art in structural text simplification. Moreover, an extrinsic evaluation shows that when applying our framework as a preprocessing step the performance of state-of-the-art Open IE systems can be improved by up to 346% in precision and 52% in recall. To enable reproducible research, all code is provided online. |
| Tasks | Machine Translation, Text Simplification |
| Published | 2019-06-03 |
| URL | https://arxiv.org/abs/1906.01038v1 |
| https://arxiv.org/pdf/1906.01038v1.pdf | |
| PWC | https://paperswithcode.com/paper/transforming-complex-sentences-into-a |
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Incremental Cluster Validity Indices for Hard Partitions: Extensions and Comparative Study
| Title | Incremental Cluster Validity Indices for Hard Partitions: Extensions and Comparative Study |
| Authors | Leonardo Enzo Brito da Silva, Niklas M. Melton, Donald C. Wunsch II |
| Abstract | Validation is one of the most important aspects of clustering, but most approaches have been batch methods. Recently, interest has grown in providing incremental alternatives. This paper extends the incremental cluster validity index (iCVI) family to include incremental versions of Calinski-Harabasz (iCH), I index and Pakhira-Bandyopadhyay-Maulik (iI and iPBM), Silhouette (iSIL), Negentropy Increment (iNI), Representative Cross Information Potential (irCIP) and Representative Cross Entropy (irH), and Conn_Index (iConn_Index). Additionally, the effect of under- and over-partitioning on the behavior of these six iCVIs, the Partition Separation (PS) index, as well as two other recently developed iCVIs (incremental Xie-Beni (iXB) and incremental Davies-Bouldin (iDB)) was examined through a comparative study. Experimental results using fuzzy adaptive resonance theory (ART)-based clustering methods showed that while evidence of most under-partitioning cases could be inferred from the behaviors of all these iCVIs, over-partitioning was found to be a more challenging scenario indicated only by the iConn_Index. The expansion of incremental validity indices provides significant novel opportunities for assessing and interpreting the results of unsupervised learning. |
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| Published | 2019-02-18 |
| URL | http://arxiv.org/abs/1902.06711v1 |
| http://arxiv.org/pdf/1902.06711v1.pdf | |
| PWC | https://paperswithcode.com/paper/incremental-cluster-validity-indices-for-hard |
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Subjective Assessment of Text Complexity: A Dataset for German Language
| Title | Subjective Assessment of Text Complexity: A Dataset for German Language |
| Authors | Babak Naderi, Salar Mohtaj, Kaspar Ensikat, Sebastian Möller |
| Abstract | This paper presents TextComplexityDE, a dataset consisting of 1000 sentences in German language taken from 23 Wikipedia articles in 3 different article-genres to be used for developing text-complexity predictor models and automatic text simplification in German language. The dataset includes subjective assessment of different text-complexity aspects provided by German learners in level A and B. In addition, it contains manual simplification of 250 of those sentences provided by native speakers and subjective assessment of the simplified sentences by participants from the target group. The subjective ratings were collected using both laboratory studies and crowdsourcing approach. |
| Tasks | Text Simplification |
| Published | 2019-04-16 |
| URL | http://arxiv.org/abs/1904.07733v1 |
| http://arxiv.org/pdf/1904.07733v1.pdf | |
| PWC | https://paperswithcode.com/paper/subjective-assessment-of-text-complexity-a |
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Generating Animations from Screenplays
| Title | Generating Animations from Screenplays |
| Authors | Yeyao Zhang, Eleftheria Tsipidi, Sasha Schriber, Mubbasir Kapadia, Markus Gross, Ashutosh Modi |
| Abstract | Automatically generating animation from natural language text finds application in a number of areas e.g. movie script writing, instructional videos, and public safety. However, translating natural language text into animation is a challenging task. Existing text-to-animation systems can handle only very simple sentences, which limits their applications. In this paper, we develop a text-to-animation system which is capable of handling complex sentences. We achieve this by introducing a text simplification step into the process. Building on an existing animation generation system for screenwriting, we create a robust NLP pipeline to extract information from screenplays and map them to the system’s knowledge base. We develop a set of linguistic transformation rules that simplify complex sentences. Information extracted from the simplified sentences is used to generate a rough storyboard and video depicting the text. Our sentence simplification module outperforms existing systems in terms of BLEU and SARI metrics.We further evaluated our system via a user study: 68 % participants believe that our system generates reasonable animation from input screenplays. |
| Tasks | Text Simplification |
| Published | 2019-04-10 |
| URL | http://arxiv.org/abs/1904.05440v1 |
| http://arxiv.org/pdf/1904.05440v1.pdf | |
| PWC | https://paperswithcode.com/paper/generating-animations-from-screenplays |
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SemEval-2016 Task 3: Community Question Answering
| Title | SemEval-2016 Task 3: Community Question Answering |
| Authors | Preslav Nakov, Lluís Màrquez, Alessandro Moschitti, Walid Magdy, Hamdy Mubarak, Abed Alhakim Freihat, James Glass, Bilal Randeree |
| Abstract | This paper describes the SemEval–2016 Task 3 on Community Question Answering, which we offered in English and Arabic. For English, we had three subtasks: Question–Comment Similarity (subtask A), Question–Question Similarity (B), and Question–External Comment Similarity (C). For Arabic, we had another subtask: Rerank the correct answers for a new question (D). Eighteen teams participated in the task, submitting a total of 95 runs (38 primary and 57 contrastive) for the four subtasks. A variety of approaches and features were used by the participating systems to address the different subtasks, which are summarized in this paper. The best systems achieved an official score (MAP) of 79.19, 76.70, 55.41, and 45.83 in subtasks A, B, C, and D, respectively. These scores are significantly better than those for the baselines that we provided. For subtask A, the best system improved over the 2015 winner by 3 points absolute in terms of Accuracy. |
| Tasks | Community Question Answering, Question Answering, Question Similarity |
| Published | 2019-12-03 |
| URL | https://arxiv.org/abs/1912.01972v1 |
| https://arxiv.org/pdf/1912.01972v1.pdf | |
| PWC | https://paperswithcode.com/paper/semeval-2016-task-3-community-question-1 |
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Deep Contextualized Pairwise Semantic Similarity for Arabic Language Questions
| Title | Deep Contextualized Pairwise Semantic Similarity for Arabic Language Questions |
| Authors | Hesham Al-Bataineh, Wael Farhan, Ahmad Mustafa, Haitham Seelawi, Hussein T. Al-Natsheh |
| Abstract | Question semantic similarity is a challenging and active research problem that is very useful in many NLP applications, such as detecting duplicate questions in community question answering platforms such as Quora. Arabic is considered to be an under-resourced language, has many dialects, and rich in morphology. Combined together, these challenges make identifying semantically similar questions in Arabic even more difficult. In this paper, we introduce a novel approach to tackle this problem, and test it on two benchmarks; one for Modern Standard Arabic (MSA), and another for the 24 major Arabic dialects. We are able to show that our new system outperforms state-of-the-art approaches by achieving 93% F1-score on the MSA benchmark and 82% on the dialectical one. This is achieved by utilizing contextualized word representations (ELMo embeddings) trained on a text corpus containing MSA and dialectic sentences. This in combination with a pairwise fine-grained similarity layer, helps our question-to-question similarity model to generalize predictions on different dialects while being trained only on question-to-question MSA data. |
| Tasks | Community Question Answering, Question Answering, Question Similarity, Semantic Similarity, Semantic Textual Similarity |
| Published | 2019-09-19 |
| URL | https://arxiv.org/abs/1909.09490v1 |
| https://arxiv.org/pdf/1909.09490v1.pdf | |
| PWC | https://paperswithcode.com/paper/deep-contextualized-pairwise-semantic |
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Making Third Person Techniques Recognize First-Person Actions in Egocentric Videos
| Title | Making Third Person Techniques Recognize First-Person Actions in Egocentric Videos |
| Authors | Sagar Verma, Pravin Nagar, Divam Gupta, Chetan Arora |
| Abstract | We focus on first-person action recognition from egocentric videos. Unlike third person domain, researchers have divided first-person actions into two categories: involving hand-object interactions and the ones without, and developed separate techniques for the two action categories. Further, it has been argued that traditional cues used for third person action recognition do not suffice, and egocentric specific features, such as head motion and handled objects have been used for such actions. Unlike the state-of-the-art approaches, we show that a regular two stream Convolutional Neural Network (CNN) with Long Short-Term Memory (LSTM) architecture, having separate streams for objects and motion, can generalize to all categories of first-person actions. The proposed approach unifies the feature learned by all action categories, making the proposed architecture much more practical. In an important observation, we note that the size of the objects visible in the egocentric videos is much smaller. We show that the performance of the proposed model improves after cropping and resizing frames to make the size of objects comparable to the size of ImageNet’s objects. Our experiments on the standard datasets: GTEA, EGTEA Gaze+, HUJI, ADL, UTE, and Kitchen, proves that our model significantly outperforms various state-of-the-art techniques. |
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| Published | 2019-10-17 |
| URL | https://arxiv.org/abs/1910.07766v1 |
| https://arxiv.org/pdf/1910.07766v1.pdf | |
| PWC | https://paperswithcode.com/paper/making-third-person-techniques-recognize |
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ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets
| Title | ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets |
| Authors | Ramy Baly, Alaa Khaddaj, Hazem Hajj, Wassim El-Hajj, Khaled Bashir Shaban |
| Abstract | Sentiment analysis is a highly subjective and challenging task. Its complexity further increases when applied to the Arabic language, mainly because of the large variety of dialects that are unstandardized and widely used in the Web, especially in social media. While many datasets have been released to train sentiment classifiers in Arabic, most of these datasets contain shallow annotation, only marking the sentiment of the text unit, as a word, a sentence or a document. In this paper, we present the Arabic Sentiment Twitter Dataset for the Levantine dialect (ArSenTD-LEV). Based on findings from analyzing tweets from the Levant region, we created a dataset of 4,000 tweets with the following annotations: the overall sentiment of the tweet, the target to which the sentiment was expressed, how the sentiment was expressed, and the topic of the tweet. Results confirm the importance of these annotations at improving the performance of a baseline sentiment classifier. They also confirm the gap of training in a certain domain, and testing in another domain. |
| Tasks | Sentiment Analysis |
| Published | 2019-05-25 |
| URL | https://arxiv.org/abs/1906.01830v1 |
| https://arxiv.org/pdf/1906.01830v1.pdf | |
| PWC | https://paperswithcode.com/paper/190601830 |
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NSURL-2019 Shared Task 8: Semantic Question Similarity in Arabic
| Title | NSURL-2019 Shared Task 8: Semantic Question Similarity in Arabic |
| Authors | Haitham Seelawi, Ahmad Mustafa, Hesham Al-Bataineh, Wael Farhan, Hussein T. Al-Natsheh |
| Abstract | Question semantic similarity (Q2Q) is a challenging task that is very useful in many NLP applications, such as detecting duplicate questions and question answering systems. In this paper, we present the results and findings of the shared task (Semantic Question Similarity in Arabic). The task was organized as part of the first workshop on NLP Solutions for Under Resourced Languages (NSURL 2019) The goal of the task is to predict whether two questions are semantically similar or not, even if they are phrased differently. A total of 9 teams participated in the task. The datasets created for this task are made publicly available to support further research on Arabic Q2Q. |
| Tasks | Question Answering, Question Similarity, Semantic Similarity, Semantic Textual Similarity |
| Published | 2019-09-12 |
| URL | https://arxiv.org/abs/1909.09691v1 |
| https://arxiv.org/pdf/1909.09691v1.pdf | |
| PWC | https://paperswithcode.com/paper/nsurl-2019-shared-task-8-semantic-question |
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On Memory Mechanism in Multi-Agent Reinforcement Learning
| Title | On Memory Mechanism in Multi-Agent Reinforcement Learning |
| Authors | Yilun Zhou, Derrik E. Asher, Nicholas R. Waytowich, Julie A. Shah |
| Abstract | Multi-agent reinforcement learning (MARL) extends (single-agent) reinforcement learning (RL) by introducing additional agents and (potentially) partial observability of the environment. Consequently, algorithms for solving MARL problems incorporate various extensions beyond traditional RL methods, such as a learned communication protocol between cooperative agents that enables exchange of private information or adaptive modeling of opponents in competitive settings. One popular algorithmic construct is a memory mechanism such that an agent’s decisions can depend not only upon the current state but also upon the history of observed states and actions. In this paper, we study how a memory mechanism can be useful in environments with different properties, such as observability, internality and presence of a communication channel. Using both prior work and new experiments, we show that a memory mechanism is helpful when learning agents need to model other agents and/or when communication is constrained in some way; however we must to be cautious of agents achieving effective memoryfulness through other means. |
| Tasks | Multi-agent Reinforcement Learning |
| Published | 2019-09-11 |
| URL | https://arxiv.org/abs/1909.05232v1 |
| https://arxiv.org/pdf/1909.05232v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-memory-mechanism-in-multi-agent |
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Investigating The Piece-Wise Linearity And Benchmark Related To Koczy-Hirota Fuzzy Linear Interpolation
| Title | Investigating The Piece-Wise Linearity And Benchmark Related To Koczy-Hirota Fuzzy Linear Interpolation |
| Authors | Maen Alzubi, Szilvester Kovács |
| Abstract | Fuzzy Rule Interpolation (FRI) reasoning methods have been introduced to address sparse fuzzy rule bases and reduce complexity. The first FRI method was the Koczy and Hirota (KH) proposed “Linear Interpolation”. Besides, several conditions and criteria have been suggested for unifying the common requirements FRI methods have to satisfy. One of the most conditions is restricted the fuzzy set of the conclusion must preserve a Piece-Wise Linearity (PWL) if all antecedents and consequents of the fuzzy rules are preserving on PWL sets at {\alpha}-cut levels. The KH FRI is one of FRI methods which cannot satisfy this condition. Therefore, the goal of this paper is to investigate equations and notations related to PWL property, which is aimed to highlight the problematic properties of the KH FRI method to prove its efficiency with PWL condition. In addition, this paper is focusing on constructing benchmark examples to be a baseline for testing other FRI methods against situations that are not satisfied with the linearity condition for KH FRI. |
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| Published | 2019-07-01 |
| URL | https://arxiv.org/abs/1907.01047v2 |
| https://arxiv.org/pdf/1907.01047v2.pdf | |
| PWC | https://paperswithcode.com/paper/investigating-the-piece-wise-linearity-and |
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