Paper Group NANR 250
Combining Concepts and Their Translations from Structured Dictionaries of Uralic Minority Languages. Tools for Building an Interlinked Synonym Lexicon Network. Differentially Private Identity and Equivalence Testing of Discrete Distributions. Annotating Zero Anaphora for Question Answering. Annotation and Quantitative Analysis of Speaker Informatio …
Combining Concepts and Their Translations from Structured Dictionaries of Uralic Minority Languages
| Title | Combining Concepts and Their Translations from Structured Dictionaries of Uralic Minority Languages |
| Authors | Mika H{"a}m{"a}l{"a}inen, Liisa Lotta Tarvainen, Jack Rueter |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1138/ |
| https://www.aclweb.org/anthology/L18-1138 | |
| PWC | https://paperswithcode.com/paper/combining-concepts-and-their-translations |
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Tools for Building an Interlinked Synonym Lexicon Network
| Title | Tools for Building an Interlinked Synonym Lexicon Network |
| Authors | Zde{\v{n}}ka Ure{\v{s}}ov{'a}, Eva Fu{\v{c}}{'\i}kov{'a}, Eva Haji{\v{c}}ov{'a}, Jan Haji{\v{c}} |
| Abstract | |
| Tasks | |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1136/ |
| https://www.aclweb.org/anthology/L18-1136 | |
| PWC | https://paperswithcode.com/paper/tools-for-building-an-interlinked-synonym |
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Differentially Private Identity and Equivalence Testing of Discrete Distributions
| Title | Differentially Private Identity and Equivalence Testing of Discrete Distributions |
| Authors | Maryam Aliakbarpour, Ilias Diakonikolas, Ronitt Rubinfeld |
| Abstract | We study the fundamental problems of identity and equivalence testing over a discrete population from random samples. Our goal is to develop efficient testers while guaranteeing differential privacy to the individuals of the population. We provide sample-efficient differentially private testers for these problems. Our theoretical results significantly improve over the best known algorithms for identity testing, and are the first results for private equivalence testing. The conceptual message of our work is that there exist private hypothesis testers that are nearly as sample-efficient as their non-private counterparts. We perform an experimental evaluation of our algorithms on synthetic data. Our experiments illustrate that our private testers achieve small type I and type II errors with sample size sublinear in the domain size of the underlying distributions. |
| Tasks | |
| Published | 2018-07-01 |
| URL | https://icml.cc/Conferences/2018/Schedule?showEvent=2415 |
| http://proceedings.mlr.press/v80/aliakbarpour18a/aliakbarpour18a.pdf | |
| PWC | https://paperswithcode.com/paper/differentially-private-identity-and |
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Annotating Zero Anaphora for Question Answering
| Title | Annotating Zero Anaphora for Question Answering |
| Authors | Yoshihiko Asao, Ryu Iida, Kentaro Torisawa |
| Abstract | |
| Tasks | Question Answering |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1556/ |
| https://www.aclweb.org/anthology/L18-1556 | |
| PWC | https://paperswithcode.com/paper/annotating-zero-anaphora-for-question |
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Annotation and Quantitative Analysis of Speaker Information in Novel Conversation Sentences in Japanese
| Title | Annotation and Quantitative Analysis of Speaker Information in Novel Conversation Sentences in Japanese |
| Authors | Makoto Yamazaki, Yumi Miyazaki, Wakako Kashino |
| Abstract | |
| Tasks | |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1174/ |
| https://www.aclweb.org/anthology/L18-1174 | |
| PWC | https://paperswithcode.com/paper/annotation-and-quantitative-analysis-of |
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Deep Video Quality Assessor: From Spatio-temporal Visual Sensitivity to A Convolutional Neural Aggregation Network
| Title | Deep Video Quality Assessor: From Spatio-temporal Visual Sensitivity to A Convolutional Neural Aggregation Network |
| Authors | Woojae Kim, Jongyoo Kim, Sewoong Ahn, Jinwoo Kim, Sanghoon Lee |
| Abstract | Incorporating spatio-temporal human visual perception into video quality assessment (VQA) remains a formidable issue. Previous statistical or computational models of spatio-temporal perception have limitations to be applied to the general VQA algorithms. In this paper, we propose a novel full-reference (FR) VQA framework named Deep Video Quality Assessor (DeepVQA) to quantify the spatio-temporal visual perception via a convolutional neural network (CNN) and a convolutional neural aggregation network (CNAN). Our framework enables to figure out the spatio-temporal sensitivity behavior through learning in accordance with the subjective score. In addition, to manipulate the temporal variation of distortions, we propose a novel temporal pooling method using an attention model. In the experiment, we show DeepVQA remarkably achieves the state-of-the-art prediction accuracy of more than 0.9 correlation, which is ~5% higher than those of conventional methods on the LIVE and CSIQ video databases. |
| Tasks | Video Quality Assessment, Visual Question Answering |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Woojae_Kim_Deep_Video_Quality_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Woojae_Kim_Deep_Video_Quality_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/deep-video-quality-assessor-from-spatio |
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Improving Machine Translation of English Relative Clauses with Automatic Text Simplification
| Title | Improving Machine Translation of English Relative Clauses with Automatic Text Simplification |
| Authors | Sanja {\v{S}}tajner, Maja Popovi{'c} |
| Abstract | |
| Tasks | Machine Translation, Text Simplification |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-7006/ |
| https://www.aclweb.org/anthology/W18-7006 | |
| PWC | https://paperswithcode.com/paper/improving-machine-translation-of-english |
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Linking, Searching, and Visualizing Entities in Wikipedia
| Title | Linking, Searching, and Visualizing Entities in Wikipedia |
| Authors | Marcus Klang, Pierre Nugues |
| Abstract | |
| Tasks | Entity Extraction, Named Entity Recognition, Open Information Extraction, Question Answering, Text Categorization |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1540/ |
| https://www.aclweb.org/anthology/L18-1540 | |
| PWC | https://paperswithcode.com/paper/linking-searching-and-visualizing-entities-in |
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Gaining and Losing Influence in Online Conversation
| Title | Gaining and Losing Influence in Online Conversation |
| Authors | Arun Sharma, Tomek Strzalkowski |
| Abstract | |
| Tasks | Dialogue Understanding, Sentiment Analysis |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1110/ |
| https://www.aclweb.org/anthology/L18-1110 | |
| PWC | https://paperswithcode.com/paper/gaining-and-losing-influence-in-online |
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AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning
| Title | AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning |
| Authors | Ahmed Alaa, Mihaela Schaar |
| Abstract | Clinical prognostic models derived from largescale healthcare data can inform critical diagnostic and therapeutic decisions. To enable off-theshelf usage of machine learning (ML) in prognostic research, we developed AUTOPROGNOSIS: a system for automating the design of predictive modeling pipelines tailored for clinical prognosis. AUTOPROGNOSIS optimizes ensembles of pipeline configurations efficiently using a novel batched Bayesian optimization (BO) algorithm that learns a low-dimensional decomposition of the pipelines’ high-dimensional hyperparameter space in concurrence with the BO procedure. This is achieved by modeling the pipelines’ performances as a black-box function with a Gaussian process prior, and modeling the “similarities” between the pipelines’ baseline algorithms via a sparse additive kernel with a Dirichlet prior. Meta-learning is used to warmstart BO with external data from “similar” patient cohorts by calibrating the priors using an algorithm that mimics the empirical Bayes method. The system automatically explains its predictions by presenting the clinicians with logical association rules that link patients’ features to predicted risk strata. We demonstrate the utility of AUTOPROGNOSIS using 10 major patient cohorts representing various aspects of cardiovascular patient care. |
| Tasks | Meta-Learning |
| Published | 2018-07-01 |
| URL | https://icml.cc/Conferences/2018/Schedule?showEvent=2050 |
| http://proceedings.mlr.press/v80/alaa18b/alaa18b.pdf | |
| PWC | https://paperswithcode.com/paper/autoprognosis-automated-clinical-prognostic-1 |
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WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony
| Title | WLV at SemEval-2018 Task 3: Dissecting Tweets in Search of Irony |
| Authors | Omid Rohanian, Shiva Taslimipoor, Richard Evans, Ruslan Mitkov |
| Abstract | This paper describes the systems submitted to SemEval 2018 Task 3 {``}Irony detection in English tweets{''} for both subtasks A and B. The first system leveraging a combination of sentiment, distributional semantic, and text surface features is ranked third among 44 teams according to the official leaderboard of the subtask A. The second system with slightly different representation of the features ranked ninth in subtask B. We present a method that entails decomposing tweets into separate parts. Searching for contrast within the constituents of a tweet is an integral part of our system. We embrace an extensive definition of contrast which leads to a vast coverage in detecting ironic content. | |
| Tasks | Sentiment Analysis |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-1090/ |
| https://www.aclweb.org/anthology/S18-1090 | |
| PWC | https://paperswithcode.com/paper/wlv-at-semeval-2018-task-3-dissecting-tweets |
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EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS
| Title | EXPLORING NEURAL ARCHITECTURE SEARCH FOR LANGUAGE TASKS |
| Authors | Minh-Thang Luong, David Dohan, Adams Wei Yu, Quoc V. Le, Barret Zoph, Vijay Vasudevan |
| Abstract | Neural architecture search (NAS), the task of finding neural architectures automatically, has recently emerged as a promising approach for unveiling better models over human-designed ones. However, most success stories are for vision tasks and have been quite limited for text, except for a small language modeling setup. In this paper, we explore NAS for text sequences at scale, by first focusing on the task of language translation and later extending to reading comprehension. From a standard sequence-to-sequence models for translation, we conduct extensive searches over the recurrent cells and attention similarity functions across two translation tasks, IWSLT English-Vietnamese and WMT German-English. We report challenges in performing cell searches as well as demonstrate initial success on attention searches with translation improvements over strong baselines. In addition, we show that results on attention searches are transferable to reading comprehension on the SQuAD dataset. |
| Tasks | Language Modelling, Neural Architecture Search, Reading Comprehension |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=r1Zi2Mb0- |
| https://openreview.net/pdf?id=r1Zi2Mb0- | |
| PWC | https://paperswithcode.com/paper/exploring-neural-architecture-search-for |
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Clebsch–Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network
| Title | Clebsch–Gordan Nets: a Fully Fourier Space Spherical Convolutional Neural Network |
| Authors | Risi Kondor, Zhen Lin, Shubhendu Trivedi |
| Abstract | Recent work by Cohen et al. has achieved state-of-the-art results for learning spherical images in a rotation invariant way by using ideas from group representation theory and noncommutative harmonic analysis. In this paper we propose a generalization of this work that generally exhibits improved performace, but from an implementation point of view is actually simpler. An unusual feature of the proposed architecture is that it uses the Clebsch–Gordan transform as its only source of nonlinearity, thus avoiding repeated forward and backward Fourier transforms. The underlying ideas of the paper generalize to constructing neural networks that are invariant to the action of other compact groups. |
| Tasks | |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/8215-clebschgordan-nets-a-fully-fourier-space-spherical-convolutional-neural-network |
| http://papers.nips.cc/paper/8215-clebschgordan-nets-a-fully-fourier-space-spherical-convolutional-neural-network.pdf | |
| PWC | https://paperswithcode.com/paper/clebschgordan-nets-a-fully-fourier-space |
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Learning Deep Descriptors With Scale-Aware Triplet Networks
| Title | Learning Deep Descriptors With Scale-Aware Triplet Networks |
| Authors | Michel Keller, Zetao Chen, Fabiola Maffra, Patrik Schmuck, Margarita Chli |
| Abstract | Research on learning suitable feature descriptors for Computer Vision has recently shifted to deep learning where the biggest challenge lies with the formulation of appropriate loss functions, especially since the descriptors to be learned are not known at training time. While approaches such as Siamese and triplet losses have been applied with success, it is still not well understood what makes a good loss function. In this spirit, this work demonstrates that many commonly used losses suffer from a range of problems. Based on this analysis, we introduce mixed-context losses and scale-aware sampling, two methods that when combined enable networks to learn consistently scaled descriptors for the first time. |
| Tasks | |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Keller_Learning_Deep_Descriptors_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Keller_Learning_Deep_Descriptors_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/learning-deep-descriptors-with-scale-aware |
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Proceedings of the Workshop on Machine Reading for Question Answering
| Title | Proceedings of the Workshop on Machine Reading for Question Answering |
| Authors | |
| Abstract | |
| Tasks | Question Answering, Reading Comprehension |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-2600/ |
| https://www.aclweb.org/anthology/W18-2600 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-workshop-on-machine-1 |
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