Paper Group NANR 29
SB-CH: A Swiss German Corpus with Sentiment Annotations. MetaReg: Towards Domain Generalization using Meta-Regularization. Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. A Large Automatically-Acquired All-Words List of Multiword Expressions Scored for Compositionality. Sign Languages and the …
SB-CH: A Swiss German Corpus with Sentiment Annotations
| Title | SB-CH: A Swiss German Corpus with Sentiment Annotations |
| Authors | Ralf Grubenmann, Don Tuggener, Pius von D{"a}niken, Jan Deriu, Mark Cieliebak |
| Abstract | |
| Tasks | Sentiment Analysis |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1372/ |
| https://www.aclweb.org/anthology/L18-1372 | |
| PWC | https://paperswithcode.com/paper/sb-ch-a-swiss-german-corpus-with-sentiment |
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MetaReg: Towards Domain Generalization using Meta-Regularization
| Title | MetaReg: Towards Domain Generalization using Meta-Regularization |
| Authors | Yogesh Balaji, Swami Sankaranarayanan, Rama Chellappa |
| Abstract | Training models that generalize to new domains at test time is a problem of fundamental importance in machine learning. In this work, we encode this notion of domain generalization using a novel regularization function. We pose the problem of finding such a regularization function in a Learning to Learn (or) meta-learning framework. The objective of domain generalization is explicitly modeled by learning a regularizer that makes the model trained on one domain to perform well on another domain. Experimental validations on computer vision and natural language datasets indicate that our method can learn regularizers that achieve good cross-domain generalization. |
| Tasks | Domain Generalization, Meta-Learning |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7378-metareg-towards-domain-generalization-using-meta-regularization |
| http://papers.nips.cc/paper/7378-metareg-towards-domain-generalization-using-meta-regularization.pdf | |
| PWC | https://paperswithcode.com/paper/metareg-towards-domain-generalization-using |
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Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP
| Title | Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP |
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| Abstract | |
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| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-5400/ |
| https://www.aclweb.org/anthology/W18-5400 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-2018-emnlp-workshop |
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A Large Automatically-Acquired All-Words List of Multiword Expressions Scored for Compositionality
| Title | A Large Automatically-Acquired All-Words List of Multiword Expressions Scored for Compositionality |
| Authors | Will Roberts, Markus Egg |
| Abstract | |
| Tasks | Information Retrieval, Machine Translation, Question Answering, Text Generation, Word Sense Disambiguation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1046/ |
| https://www.aclweb.org/anthology/L18-1046 | |
| PWC | https://paperswithcode.com/paper/a-large-automatically-acquired-all-words-list |
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Sign Languages and the Online World Online Dictionaries & Lexicostatistics
| Title | Sign Languages and the Online World Online Dictionaries & Lexicostatistics |
| Authors | Shi Yu, Carlo Geraci, Natasha Abner |
| Abstract | |
| Tasks | |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1668/ |
| https://www.aclweb.org/anthology/L18-1668 | |
| PWC | https://paperswithcode.com/paper/sign-languages-and-the-online-world-online |
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A Multilingual Wikified Data Set of Educational Material
| Title | A Multilingual Wikified Data Set of Educational Material |
| Authors | Iris Hendrickx, Eirini Takoulidou, Thanasis Naskos, Katia Lida Kermanidis, Vilelmini Sosoni, Hugo de Vos, Maria Stasimioti, Menno van Zaanen, Panayota Georgakopoulou, Valia Kordoni, Maja Popovic, Markus Egg, Antal van den Bosch |
| Abstract | |
| Tasks | Cross-Lingual Semantic Textual Similarity, Machine Translation, Semantic Textual Similarity, Word Sense Disambiguation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1073/ |
| https://www.aclweb.org/anthology/L18-1073 | |
| PWC | https://paperswithcode.com/paper/a-multilingual-wikified-data-set-of |
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Geometry Based Data Generation
| Title | Geometry Based Data Generation |
| Authors | Ofir Lindenbaum, Jay Stanley, Guy Wolf, Smita Krishnaswamy |
| Abstract | We propose a new type of generative model for high-dimensional data that learns a manifold geometry of the data, rather than density, and can generate points evenly along this manifold. This is in contrast to existing generative models that represent data density, and are strongly affected by noise and other artifacts of data collection. We demonstrate how this approach corrects sampling biases and artifacts, thus improves several downstream data analysis tasks, such as clustering and classification. Finally, we demonstrate that this approach is especially useful in biology where, despite the advent of single-cell technologies, rare subpopulations and gene-interaction relationships are affected by biased sampling. We show that SUGAR can generate hypothetical populations, and it is able to reveal intrinsic patterns and mutual-information relationships between genes on a single-cell RNA sequencing dataset of hematopoiesis. |
| Tasks | |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/7414-geometry-based-data-generation |
| http://papers.nips.cc/paper/7414-geometry-based-data-generation.pdf | |
| PWC | https://paperswithcode.com/paper/geometry-based-data-generation-1 |
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BabyCloud, a Technological Platform for Parents and Researchers
| Title | BabyCloud, a Technological Platform for Parents and Researchers |
| Authors | Xu{^a}n-Nga Cao, Cyrille Dakhlia, Patricia Del Carmen, Mohamed-Amine Jaouani, Malik Ould-Arbi, Emmanuel Dupoux |
| Abstract | |
| Tasks | Language Acquisition |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1361/ |
| https://www.aclweb.org/anthology/L18-1361 | |
| PWC | https://paperswithcode.com/paper/babycloud-a-technological-platform-for |
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Neural Maximum Subgraph Parsing for Cross-Domain Semantic Dependency Analysis
| Title | Neural Maximum Subgraph Parsing for Cross-Domain Semantic Dependency Analysis |
| Authors | Yufei Chen, Sheng Huang, Fang Wang, Junjie Cao, Weiwei Sun, Xiaojun Wan |
| Abstract | We present experiments for cross-domain semantic dependency analysis with a neural Maximum Subgraph parser. Our parser targets 1-endpoint-crossing, pagenumber-2 graphs which are a good fit to semantic dependency graphs, and utilizes an efficient dynamic programming algorithm for decoding. For disambiguation, the parser associates words with BiLSTM vectors and utilizes these vectors to assign scores to candidate dependencies. We conduct experiments on the data sets from SemEval 2015 as well as Chinese CCGBank. Our parser achieves very competitive results for both English and Chinese. To improve the parsing performance on cross-domain texts, we propose a data-oriented method to explore the linguistic generality encoded in English Resource Grammar, which is a precisionoriented, hand-crafted HPSG grammar, in an implicit way. Experiments demonstrate the effectiveness of our data-oriented method across a wide range of conditions. |
| Tasks | Dependency Parsing, Semantic Parsing |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/K18-1054/ |
| https://www.aclweb.org/anthology/K18-1054 | |
| PWC | https://paperswithcode.com/paper/neural-maximum-subgraph-parsing-for-cross |
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A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning
| Title | A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning |
| Authors | Hatem Mousselly-Sergieh, Teresa Botschen, Iryna Gurevych, Stefan Roth |
| Abstract | Current methods for knowledge graph (KG) representation learning focus solely on the structure of the KG and do not exploit any kind of external information, such as visual and linguistic information corresponding to the KG entities. In this paper, we propose a multimodal translation-based approach that defines the energy of a KG triple as the sum of sub-energy functions that leverage both multimodal (visual and linguistic) and structural KG representations. Next, a ranking-based loss is minimized using a simple neural network architecture. Moreover, we introduce a new large-scale dataset for multimodal KG representation learning. We compared the performance of our approach to other baselines on two standard tasks, namely knowledge graph completion and triple classification, using our as well as the WN9-IMG dataset. The results demonstrate that our approach outperforms all baselines on both tasks and datasets. |
| Tasks | Graph Representation Learning, Information Retrieval, Knowledge Graph Completion, Knowledge Graphs, Question Answering, Representation Learning |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/S18-2027/ |
| https://www.aclweb.org/anthology/S18-2027 | |
| PWC | https://paperswithcode.com/paper/a-multimodal-translation-based-approach-for |
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Edit me: A Corpus and a Framework for Understanding Natural Language Image Editing
| Title | Edit me: A Corpus and a Framework for Understanding Natural Language Image Editing |
| Authors | Ramesh Manuvinakurike, Jacqueline Brixey, Trung Bui, Walter Chang, Doo Soon Kim, Ron Artstein, Kallirroi Georgila |
| Abstract | |
| Tasks | Image Captioning, Question Answering, Visual Question Answering |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1683/ |
| https://www.aclweb.org/anthology/L18-1683 | |
| PWC | https://paperswithcode.com/paper/edit-me-a-corpus-and-a-framework-for |
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Deep Reinforcement Learning of Region Proposal Networks for Object Detection
| Title | Deep Reinforcement Learning of Region Proposal Networks for Object Detection |
| Authors | Aleksis Pirinen, Cristian Sminchisescu |
| Abstract | We propose drl-RPN, a deep reinforcement learning-based visual recognition model consisting of a sequential region proposal network (RPN) and an object detector. In contrast to typical RPNs, where candidate object regions (RoIs) are selected greedily via class-agnostic NMS, drl-RPN optimizes an objective closer to the final detection task. This is achieved by replacing the greedy RoI selection process with a sequential attention mechanism which is trained via deep reinforcement learning (RL). Our model is capable of accumulating class-specific evidence over time, potentially affecting subsequent proposals and classification scores, and we show that such context integration significantly boosts detection accuracy. Moreover, drl-RPN automatically decides when to stop the search process and has the benefit of being able to jointly learn the parameters of the policy and the detector, both represented as deep networks. Our model can further learn to search over a wide range of exploration-accuracy trade-offs making it possible to specify or adapt the exploration extent at test time. The resulting search trajectories are image- and category-dependent, yet rely only on a single policy over all object categories. Results on the MS COCO and PASCAL VOC challenges show that our approach outperforms established, typical state-of-the-art object detection pipelines. |
| Tasks | Object Detection |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Pirinen_Deep_Reinforcement_Learning_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Pirinen_Deep_Reinforcement_Learning_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/deep-reinforcement-learning-of-region |
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Evaluating EcoLexiCAT: a Terminology-Enhanced CAT Tool
| Title | Evaluating EcoLexiCAT: a Terminology-Enhanced CAT Tool |
| Authors | Pilar Le{'o}n-Ara{'u}z, Arianne Reimerink |
| Abstract | |
| Tasks | Machine Translation |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1377/ |
| https://www.aclweb.org/anthology/L18-1377 | |
| PWC | https://paperswithcode.com/paper/evaluating-ecolexicat-a-terminology-enhanced |
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Solving the Perspective-2-Point Problem for Flying-Camera Photo Composition
| Title | Solving the Perspective-2-Point Problem for Flying-Camera Photo Composition |
| Authors | Ziquan Lan, David Hsu, Gim Hee Lee |
| Abstract | Drone-mounted flying cameras will revolutionize photo-taking. The user, instead of holding a camera in hand and manually searching for a viewpoint, will interact directly with image contents in the viewfinder through simple gestures, and the flying camera will achieve the desired viewpoint through the autonomous flying capability of the drone. This work studies the underlying viewpoint search problem for composing a photo with two objects of interest, a common situation in photo-taking. We model it as a Perspective-2-Point (P2P) problem, which is under-constrained to determine the six degrees-of-freedom camera pose uniquely. By incorporating the user’s composition requirements and minimizing the camera’s flying distance, we form a constrained nonlinear optimization problem and solve it in closed form. Experiments on synthetic data sets and on a real flying camera system indicate promising results. |
| Tasks | |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Lan_Solving_the_Perspective-2-Point_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Lan_Solving_the_Perspective-2-Point_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/solving-the-perspective-2-point-problem-for |
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A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events
| Title | A German Corpus for Fine-Grained Named Entity Recognition and Relation Extraction of Traffic and Industry Events |
| Authors | Martin Schiersch, Veselina Mironova, Maximilian Schmitt, Philippe Thomas, Aleks Gabryszak, ra, Leonhard Hennig |
| Abstract | |
| Tasks | Decision Making, Named Entity Recognition, Relation Extraction |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1703/ |
| https://www.aclweb.org/anthology/L18-1703 | |
| PWC | https://paperswithcode.com/paper/a-german-corpus-for-fine-grained-named-entity |
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