Paper Group NANR 71
Parallel Dependency Treebank Annotated with Interlinked Verbal Synonym Classes and Roles. Oblivious Sampling Algorithms for Private Data Analysis. Dense Node Representation for Geolocation. Toward a cognitive dependency grammar of Hungarian. Convex Shape Prior for Multi-Object Segmentation Using a Single Level Set Function. Neural GRANNy at SemEval …
Parallel Dependency Treebank Annotated with Interlinked Verbal Synonym Classes and Roles
| Title | Parallel Dependency Treebank Annotated with Interlinked Verbal Synonym Classes and Roles |
| 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}} |
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| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-7805/ |
| https://www.aclweb.org/anthology/W19-7805 | |
| PWC | https://paperswithcode.com/paper/parallel-dependency-treebank-annotated-with |
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Oblivious Sampling Algorithms for Private Data Analysis
| Title | Oblivious Sampling Algorithms for Private Data Analysis |
| Authors | Sajin Sasy, Olga Ohrimenko |
| Abstract | We study secure and privacy-preserving data analysis based on queries executed on samples from a dataset. Trusted execution environments (TEEs) can be used to protect the content of the data during query computation, while supporting differential-private (DP) queries in TEEs provides record privacy when query output is revealed. Support for sample-based queries is attractive due to \emph{privacy amplification} since not all dataset is used to answer a query but only a small subset. However, extracting data samples with TEEs while proving strong DP guarantees is not trivial as secrecy of sample indices has to be preserved. To this end, we design efficient secure variants of common sampling algorithms. Experimentally we show that accuracy of models trained with shuffling and sampling is the same for differentially private models for MNIST and CIFAR-10, while sampling provides stronger privacy guarantees than shuffling. |
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| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/8877-oblivious-sampling-algorithms-for-private-data-analysis |
| http://papers.nips.cc/paper/8877-oblivious-sampling-algorithms-for-private-data-analysis.pdf | |
| PWC | https://paperswithcode.com/paper/oblivious-sampling-algorithms-for-private |
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Dense Node Representation for Geolocation
| Title | Dense Node Representation for Geolocation |
| Authors | Tommaso Fornaciari, Dirk Hovy |
| Abstract | Prior research has shown that geolocation can be substantially improved by including user network information. While effective, it suffers from the curse of dimensionality, since networks are usually represented as sparse adjacency matrices of connections, which grow exponentially with the number of users. In order to incorporate this information, we therefore need to limit the network size, in turn limiting performance and risking sample bias. In this paper, we address these limitations by instead using dense network representations. We explore two methods to learn continuous node representations from either 1) the network structure with node2vec (Grover and Leskovec, 2016), or 2) textual user mentions via doc2vec (Le and Mikolov, 2014). We combine both methods with input from social media posts in an attention-based convolutional neural network and evaluate the contribution of each component on geolocation performance. Our method enables us to incorporate arbitrarily large networks in a fixed-length vector, without limiting the network size. Our models achieve competitive results with similar state-of-the-art methods, but with much fewer model parameters, while being applicable to networks of virtually any size. |
| Tasks | |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-5529/ |
| https://www.aclweb.org/anthology/D19-5529 | |
| PWC | https://paperswithcode.com/paper/dense-node-representation-for-geolocation |
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Toward a cognitive dependency grammar of Hungarian
| Title | Toward a cognitive dependency grammar of Hungarian |
| Authors | Andr{'a}s Imr{'e}nyi |
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| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-7710/ |
| https://www.aclweb.org/anthology/W19-7710 | |
| PWC | https://paperswithcode.com/paper/toward-a-cognitive-dependency-grammar-of |
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Convex Shape Prior for Multi-Object Segmentation Using a Single Level Set Function
| Title | Convex Shape Prior for Multi-Object Segmentation Using a Single Level Set Function |
| Authors | Shousheng Luo, Xue-Cheng Tai, Limei Huo, Yang Wang, Roland Glowinski |
| Abstract | Many objects in real world have convex shapes. It is a difficult task to have representations for convex shapes with good and fast numerical solutions. This paper proposes a method to incorporate convex shape prior for multi-object segmentation using level set method. The relationship between the convexity of the segmented objects and the signed distance function corresponding to their union is analyzed theoretically. This result is combined with Gaussian mixture method for the multiple objects segmentation with convexity shape prior. Alternating direction method of multiplier (ADMM) is adopted to solve the proposed model. Special boundary conditions are also imposed to obtain efficient algorithms for 4th order partial differential equations in one step of ADMM algorithm. In addition, our method only needs one level set function regardless of the number of objects. So the increase in the number of objects does not result in the increase of model and algorithm complexity. Various numerical experiments are illustrated to show the performance and advantages of the proposed method. |
| Tasks | Semantic Segmentation |
| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Luo_Convex_Shape_Prior_for_Multi-Object_Segmentation_Using_a_Single_Level_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Luo_Convex_Shape_Prior_for_Multi-Object_Segmentation_Using_a_Single_Level_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/convex-shape-prior-for-multi-object |
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Neural GRANNy at SemEval-2019 Task 2: A combined approach for better modeling of semantic relationships in semantic frame induction
| Title | Neural GRANNy at SemEval-2019 Task 2: A combined approach for better modeling of semantic relationships in semantic frame induction |
| Authors | Nikolay Arefyev, Boris Sheludko, Adis Davletov, Dmitry Kharchev, Alex Nevidomsky, Alex Panchenko, er |
| Abstract | We describe our solutions for semantic frame and role induction subtasks of SemEval 2019 Task 2. Our approaches got the highest scores, and the solution for the frame induction problem officially took the first place. The main contributions of this paper are related to the semantic frame induction problem. We propose a combined approach that employs two different types of vector representations: dense representations from hidden layers of a masked language model, and sparse representations based on substitutes for the target word in the context. The first one better groups synonyms, the second one is better at disambiguating homonyms. Extending the context to include nearby sentences improves the results in both cases. New Hearst-like patterns for verbs are introduced that prove to be effective for frame induction. Finally, we propose an approach to selecting the number of clusters in agglomerative clustering. |
| Tasks | Language Modelling |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-2004/ |
| https://www.aclweb.org/anthology/S19-2004 | |
| PWC | https://paperswithcode.com/paper/neural-granny-at-semeval-2019-task-2-a |
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On Learning Word Embeddings From Linguistically Augmented Text Corpora
| Title | On Learning Word Embeddings From Linguistically Augmented Text Corpora |
| Authors | Amila Silva, Chathurika Amarathunga |
| Abstract | Word embedding is a technique in Natural Language Processing (NLP) to map words into vector space representations. Since it has boosted the performance of many NLP downstream tasks, the task of learning word embeddings has been addressing significantly. Nevertheless, most of the underlying word embedding methods such as word2vec and GloVe fail to produce high-quality embeddings if the text corpus is small and sparse. This paper proposes a method to generate effective word embeddings from limited data. Through experiments, we show that our proposed model outperforms existing works for the classical word similarity task and for a domain-specific application. |
| Tasks | Learning Word Embeddings, Word Embeddings |
| Published | 2019-05-01 |
| URL | https://www.aclweb.org/anthology/W19-0508/ |
| https://www.aclweb.org/anthology/W19-0508 | |
| PWC | https://paperswithcode.com/paper/on-learning-word-embeddings-from |
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Investigating the Effect of Lexical Segmentation in Transformer-based Models on Medical Datasets
| Title | Investigating the Effect of Lexical Segmentation in Transformer-based Models on Medical Datasets |
| Authors | Vincent Nguyen, Sarvnaz Karimi, Zhenchang Xing |
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| Published | 2019-04-01 |
| URL | https://www.aclweb.org/anthology/U19-1022/ |
| https://www.aclweb.org/anthology/U19-1022 | |
| PWC | https://paperswithcode.com/paper/investigating-the-effect-of-lexical |
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Structure Learning with Side Information: Sample Complexity
| Title | Structure Learning with Side Information: Sample Complexity |
| Authors | Saurabh Sihag, Ali Tajer |
| Abstract | Graphical models encode the stochastic dependencies among random variables (RVs). The vertices represent the RVs, and the edges signify the conditional dependencies among the RVs. Structure learning is the process of inferring the edges by observing realizations of the RVs, and it has applications in a wide range of technological, social, and biological networks. Learning the structure of graphs when the vertices are treated in isolation from inferential information known about them is well-investigated. In a wide range of domains, however, often there exist additional inferred knowledge about the structure, which can serve as valuable side information. For instance, the gene networks that represent different subtypes of the same cancer share similar edges across all subtypes and also have exclusive edges corresponding to each subtype, rendering partially similar graphical models for gene expression in different cancer subtypes. Hence, an inferential decision regarding a gene network can serve as side information for inferring other related gene networks. When such side information is leveraged judiciously, it can translate to significant improvement in structure learning. Leveraging such side information can be abstracted as inferring structures of distinct graphical models that are {\sl partially} similar. This paper focuses on Ising graphical models, and considers the problem of simultaneously learning the structures of two {\sl partially} similar graphs, where any inference about the structure of one graph offers side information for the other graph. The bounded edge subclass of Ising models is considered, and necessary conditions (information-theoretic ), as well as sufficient conditions (algorithmic) for the sample complexity for achieving a bounded probability of error, are established. Furthermore, specific regimes are identified in which the necessary and sufficient conditions coincide, rendering the optimal sample complexity. |
| Tasks | |
| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/9582-structure-learning-with-side-information-sample-complexity |
| http://papers.nips.cc/paper/9582-structure-learning-with-side-information-sample-complexity.pdf | |
| PWC | https://paperswithcode.com/paper/structure-learning-with-side-information |
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What Does It Mean to Learn in Deep Networks? And, How Does One Detect Adversarial Attacks?
| Title | What Does It Mean to Learn in Deep Networks? And, How Does One Detect Adversarial Attacks? |
| Authors | Ciprian A. Corneanu, Meysam Madadi, Sergio Escalera, Aleix M. Martinez |
| Abstract | The flexibility and high-accuracy of Deep Neural Networks (DNNs) has transformed computer vision. But, the fact that we do not know when a specific DNN will work and when it will fail has resulted in a lack of trust. A clear example is self-driving cars; people are uncomfortable sitting in a car driven by algorithms that may fail under some unknown, unpredictable conditions. Interpretability and explainability approaches attempt to address this by uncovering what a DNN models, i.e., what each node (cell) in the network represents and what images are most likely to activate it. This can be used to generate, for example, adversarial attacks. But these approaches do not generally allow us to determine where a DNN will succeed or fail and why . i.e., does this learned representation generalize to unseen samples? Here, we derive a novel approach to define what it means to learn in deep networks, and how to use this knowledge to detect adversarial attacks. We show how this defines the ability of a network to generalize to unseen testing samples and, most importantly, why this is the case. |
| Tasks | Self-Driving Cars |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Corneanu_What_Does_It_Mean_to_Learn_in_Deep_Networks_And_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Corneanu_What_Does_It_Mean_to_Learn_in_Deep_Networks_And_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/what-does-it-mean-to-learn-in-deep-networks |
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Utilizing Word Embeddings based Features for Phylogenetic Tree Generation of Sanskrit Texts
| Title | Utilizing Word Embeddings based Features for Phylogenetic Tree Generation of Sanskrit Texts |
| Authors | Diptesh Kanojia, Abhijeet Dubey, Malhar Kulkarni, Pushpak Bhattacharyya, Gholemreza Haffari |
| Abstract | |
| Tasks | Word Embeddings |
| Published | 2019-10-01 |
| URL | https://www.aclweb.org/anthology/W19-7511/ |
| https://www.aclweb.org/anthology/W19-7511 | |
| PWC | https://paperswithcode.com/paper/utilizing-word-embeddings-based-features-for |
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An Attentive Fine-Grained Entity Typing Model with Latent Type Representation
| Title | An Attentive Fine-Grained Entity Typing Model with Latent Type Representation |
| Authors | Ying Lin, Heng Ji |
| Abstract | We propose a fine-grained entity typing model with a novel attention mechanism and a hybrid type classifier. We advance existing methods in two aspects: feature extraction and type prediction. To capture richer contextual information, we adopt contextualized word representations instead of fixed word embeddings used in previous work. In addition, we propose a two-step mention-aware attention mechanism to enable the model to focus on important words in mentions and contexts. We also present a hybrid classification method beyond binary relevance to exploit type inter-dependency with latent type representation. Instead of independently predicting each type, we predict a low-dimensional vector that encodes latent type features and reconstruct the type vector from this latent representation. Experiment results on multiple data sets show that our model significantly advances the state-of-the-art on fine-grained entity typing, obtaining up to 6.1{%} and 5.5{%} absolute gains in macro averaged F-score and micro averaged F-score respectively. |
| Tasks | Entity Typing, Word Embeddings |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1641/ |
| https://www.aclweb.org/anthology/D19-1641 | |
| PWC | https://paperswithcode.com/paper/an-attentive-fine-grained-entity-typing-model |
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Margin Call: an Accessible Web-based Text Viewer with Generated Paragraph Summaries in the Margin
| Title | Margin Call: an Accessible Web-based Text Viewer with Generated Paragraph Summaries in the Margin |
| Authors | Nabah Rizvi, Sebastian Gehrmann, Franck Dernoncourt |
| Abstract | We present Margin Call, a web-based text viewer that automatically generates short summaries for each paragraph of the text and displays the summaries in the margin of the text next to the corresponding paragraph. On the back-end, the summarizer first identifies the most important sentence for each paragraph in the text file uploaded by the user. The selected sentence is then automatically compressed to produce the short summary. The resulting summary is a few words long. The displayed summaries can help the user understand and retrieve information faster from the text, while increasing the retention of information. |
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| Published | 2019-10-01 |
| URL | https://www.aclweb.org/anthology/W19-8632/ |
| https://www.aclweb.org/anthology/W19-8632 | |
| PWC | https://paperswithcode.com/paper/margin-call-an-accessible-web-based-text |
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What Gets Echoed? Understanding the ``Pointers’’ in Explanations of Persuasive Arguments
| Title | What Gets Echoed? Understanding the ``Pointers’’ in Explanations of Persuasive Arguments | |
| Authors | David Atkinson, Kumar Bhargav Srinivasan, Chenhao Tan |
| Abstract | Explanations are central to everyday life, and are a topic of growing interest in the AI community. To investigate the process of providing natural language explanations, we leverage the dynamics of the /r/ChangeMyView subreddit to build a dataset with 36K naturally occurring explanations of why an argument is persuasive. We propose a novel word-level prediction task to investigate how explanations selectively reuse, or echo, information from what is being explained (henceforth, explanandum). We develop features to capture the properties of a word in the explanandum, and show that our proposed features not only have relatively strong predictive power on the echoing of a word in an explanation, but also enhance neural methods of generating explanations. In particular, while the non-contextual properties of a word itself are more valuable for stopwords, the interaction between the constituent parts of an explanandum is crucial in predicting the echoing of content words. We also find intriguing patterns of a word being echoed. For example, although nouns are generally less likely to be echoed, subjects and objects can, depending on their source, be more likely to be echoed in the explanations. |
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| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1289/ |
| https://www.aclweb.org/anthology/D19-1289 | |
| PWC | https://paperswithcode.com/paper/what-gets-echoed-understanding-the-pointers |
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Learning to Update Knowledge Graphs by Reading News
| Title | Learning to Update Knowledge Graphs by Reading News |
| Authors | Jizhi Tang, Yansong Feng, Dongyan Zhao |
| Abstract | News streams contain rich up-to-date information which can be used to update knowledge graphs (KGs). Most current text-based KG updating methods rely on elaborately designed information extraction (IE) systems and carefully crafted rules, which are often domain-specific and hard to maintain. Besides, such methods often hardly pay enough attention to the implicit information that lies underneath texts. In this paper, we propose a novel neural network method, GUpdater, to tackle these problems. GUpdater is built upon graph neural networks (GNNs) with a text-based attention mechanism to guide the updating message passing through the KG structures. Experiments on a real-world KG updating dataset show that our model can effectively broadcast the news information to the KG structures and perform necessary link-adding or link-deleting operations to ensure the KG up-to-date according to news snippets. |
| Tasks | Knowledge Graphs |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1265/ |
| https://www.aclweb.org/anthology/D19-1265 | |
| PWC | https://paperswithcode.com/paper/learning-to-update-knowledge-graphs-by |
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