Paper Group NANR 179
Adapting Term Recognition to an Under-Resourced Language: the Case of Irish. Computerized Note-taking in Consecutive Interpreting: A Pen-voice Integrated Approach towards Omissions, Additions and Reconstructions in Notes. Construction and Annotation of the Jordan Comprehensive Contemporary Arabic Corpus (JCCA). Proceedings of the Second MEMENTO wor …
Adapting Term Recognition to an Under-Resourced Language: the Case of Irish
| Title | Adapting Term Recognition to an Under-Resourced Language: the Case of Irish |
| Authors | John P. McCrae, Adrian Doyle |
| Abstract | |
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| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-6907/ |
| https://www.aclweb.org/anthology/W19-6907 | |
| PWC | https://paperswithcode.com/paper/adapting-term-recognition-to-an-under |
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Computerized Note-taking in Consecutive Interpreting: A Pen-voice Integrated Approach towards Omissions, Additions and Reconstructions in Notes
| Title | Computerized Note-taking in Consecutive Interpreting: A Pen-voice Integrated Approach towards Omissions, Additions and Reconstructions in Notes |
| Authors | Huolingxiao Kuang |
| Abstract | |
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| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-7009/ |
| https://www.aclweb.org/anthology/W19-7009 | |
| PWC | https://paperswithcode.com/paper/computerized-note-taking-in-consecutive |
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Construction and Annotation of the Jordan Comprehensive Contemporary Arabic Corpus (JCCA)
| Title | Construction and Annotation of the Jordan Comprehensive Contemporary Arabic Corpus (JCCA) |
| Authors | Majdi Sawalha, Faisal Alshargi, Abdallah AlShdaifat, Sane Yagi, Mohammad A. Qudah |
| Abstract | To compile a modern dictionary that catalogues the words in currency, and to study linguistic patterns in the contemporary language, it is necessary to have a corpus of authentic texts that reflect current usage of the language. Although there are numerous Arabic corpora, none claims to be representative of the language in terms of the combination of geographical region, genre, subject matter, mode, and medium. This paper describes a 100-million-word corpus that takes the British National Corpus (BNC) as a model. The aim of the corpus is to be balanced, annotated, comprehensive, and representative of contemporary Arabic as written and spoken in Arab countries today. It will be different from most others in not being heavily-dominated by the news or in mixing the classical with the modern. In this paper is an outline of the methodology adopted for the design, construction, and annotation of this corpus. DIWAN (Alshargi and Rambow, 2015) was used to annotate a one-million-word snapshot of the corpus. DIWAN is a dialectal word annotation tool, but we upgraded it by adding a new tag-set that is based on traditional Arabic grammar and by adding the roots and morphological patterns of nouns and verbs. Moreover, the corpus we constructed covers the major spoken varieties of Arabic. |
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| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-4616/ |
| https://www.aclweb.org/anthology/W19-4616 | |
| PWC | https://paperswithcode.com/paper/construction-and-annotation-of-the-jordan |
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Proceedings of the Second MEMENTO workshop on Modelling Parameters of Cognitive Effort in Translation Production
| Title | Proceedings of the Second MEMENTO workshop on Modelling Parameters of Cognitive Effort in Translation Production |
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| Abstract | |
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| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-7000/ |
| https://www.aclweb.org/anthology/W19-7000 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-second-memento-workshop-on |
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Adverse Drug Effect and Personalized Health Mentions, CLaC at SMM4H 2019, Tasks 1 and 4
| Title | Adverse Drug Effect and Personalized Health Mentions, CLaC at SMM4H 2019, Tasks 1 and 4 |
| Authors | Parsa Bagherzadeh, Nadia Sheikh, Sabine Bergler |
| Abstract | CLaC labs participated in Task 1 and 4 of SMM4H 2019. We pursed two main objectives in our submission. First we tried to use some textual features in a deep net framework, and second, the potential use of more than one word embedding was tested. The results seem positively affected by the proposed architectures. |
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| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-3222/ |
| https://www.aclweb.org/anthology/W19-3222 | |
| PWC | https://paperswithcode.com/paper/adverse-drug-effect-and-personalized-health |
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Regularized Gradient Boosting
| Title | Regularized Gradient Boosting |
| Authors | Corinna Cortes, Mehryar Mohri, Dmitry Storcheus |
| Abstract | Gradient Boosting (\GB) is a popular and very successful ensemble method for binary trees. While various types of regularization of the base predictors are used with this algorithm, the theory that connects such regularizations with generalization guarantees is poorly understood. We fill this gap by deriving data-dependent learning guarantees for \GB\ used with \emph{regularization}, expressed in terms of the Rademacher complexities of the constrained families of base predictors. We introduce a new algorithm, called \rgb, that directly benefits from these generalization bounds and that, at every boosting round, applies the \emph{Structural Risk Minimization} principle to search for a base predictor with the best empirical fit versus complexity trade-off. Inspired by \emph{Randomized Coordinate Descent} we provide a scalable implementation of our algorithm, able to search over large families of base predictors. Finally, we provide experimental results, demonstrating that our algorithm achieves significantly better out-of-sample performance on multiple datasets than the standard \GB\ algorithm used with its regularization. |
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| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/8784-regularized-gradient-boosting |
| http://papers.nips.cc/paper/8784-regularized-gradient-boosting.pdf | |
| PWC | https://paperswithcode.com/paper/regularized-gradient-boosting |
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Orthogonal Decomposition Network for Pixel-Wise Binary Classification
| Title | Orthogonal Decomposition Network for Pixel-Wise Binary Classification |
| Authors | Chang Liu, Fang Wan, Wei Ke, Zhuowei Xiao, Yuan Yao, Xiaosong Zhang, Qixiang Ye |
| Abstract | The weight sharing scheme and spatial pooling operations in Convolutional Neural Networks (CNNs) introduce semantic correlation to neighboring pixels on feature maps and therefore deteriorate their pixel-wise classification performance. In this paper, we implement an Orthogonal Decomposition Unit (ODU) that transforms a convolutional feature map into orthogonal bases targeting at de-correlating neighboring pixels on convolutional features. In theory, complete orthogonal decomposition produces orthogonal bases which can perfectly reconstruct any binary mask (ground-truth). In practice, we further design incomplete orthogonal decomposition focusing on de-correlating local patches which balances the reconstruction performance and computational cost. Fully Convolutional Networks (FCNs) implemented with ODUs, referred to as Orthogonal Decomposition Networks (ODNs), learn de-correlated and complementary convolutional features and fuse such features in a pixel-wise selective manner. Over pixel-wise binary classification tasks for two-dimensional image processing, specifically skeleton detection, edge detection, and saliency detection, and one-dimensional keypoint detection, specifically S-wave arrival time detection for earthquake localization, ODNs consistently improves the state-of-the-arts with significant margins. |
| Tasks | Edge Detection, Keypoint Detection, Saliency Detection |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Liu_Orthogonal_Decomposition_Network_for_Pixel-Wise_Binary_Classification_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Liu_Orthogonal_Decomposition_Network_for_Pixel-Wise_Binary_Classification_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/orthogonal-decomposition-network-for-pixel |
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A Cross-Season Correspondence Dataset for Robust Semantic Segmentation
| Title | A Cross-Season Correspondence Dataset for Robust Semantic Segmentation |
| Authors | Mans Larsson, Erik Stenborg, Lars Hammarstrand, Marc Pollefeys, Torsten Sattler, Fredrik Kahl |
| Abstract | In this paper, we present a method to utilize 2D-2D point matches between images taken during different image conditions to train a convolutional neural network for semantic segmentation. Enforcing label consistency across the matches makes the final segmentation algorithm robust to seasonal changes. We describe how these 2D-2D matches can be generated with little human interaction by geometrically matching points from 3D models built from images. Two cross-season correspondence datasets are created providing 2D-2D matches across seasonal changes as well as from day to night. The datasets are made publicly available to facilitate further research. We show that adding the correspondences as extra supervision during training improves the segmentation performance of the convolutional neural network, making it more robust to seasonal changes and weather conditions. |
| Tasks | Semantic Segmentation |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Larsson_A_Cross-Season_Correspondence_Dataset_for_Robust_Semantic_Segmentation_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Larsson_A_Cross-Season_Correspondence_Dataset_for_Robust_Semantic_Segmentation_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/a-cross-season-correspondence-dataset-for-1 |
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Automatic identification of writers’ intentions: Comparing different methods for predicting relationship goals in online dating profile texts
| Title | Automatic identification of writers’ intentions: Comparing different methods for predicting relationship goals in online dating profile texts |
| Authors | Chris van der Lee, van der Z, Tess en, Emiel Krahmer, Maria Mos, Alex Schouten, er |
| Abstract | Psychologically motivated, lexicon-based text analysis methods such as LIWC (Pennebaker et al., 2015) have been criticized by computational linguists for their lack of adaptability, but they have not often been systematically compared with either human evaluations or machine learning approaches. The goal of the current study was to assess the effectiveness and predictive ability of LIWC on a relationship goal classification task. In this paper, we compared the outcomes of (1) LIWC, (2) machine learning, and (3) a human baseline. A newly collected corpus of online dating profile texts (a genre not explored before in the ACL anthology) was used, accompanied by the profile writers{'} self-selected relationship goal (long-term versus date). These three approaches were tested by comparing their performance on identifying both the intended relationship goal and content-related text labels. Results show that LIWC and machine learning models correlate with human evaluations in terms of content-related labels. LIWC{'}s content-related labels corresponded more strongly to humans than those of the classifier. Moreover, all approaches were similarly accurate in predicting the relationship goal. |
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| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-5512/ |
| https://www.aclweb.org/anthology/D19-5512 | |
| PWC | https://paperswithcode.com/paper/automatic-identification-of-writers |
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Query-focused Sentence Compression in Linear Time
| Title | Query-focused Sentence Compression in Linear Time |
| Authors | H, Abram ler, Brendan O{'}Connor |
| Abstract | Search applications often display shortened sentences which must contain certain query terms and must fit within the space constraints of a user interface. This work introduces a new transition-based sentence compression technique developed for such settings. Our query-focused method constructs length and lexically constrained compressions in linear time, by growing a subgraph in the dependency parse of a sentence. This theoretically efficient approach achieves an 11x empirical speedup over baseline ILP methods, while better reconstructing gold constrained shortenings. Such speedups help query-focused applications, because users are measurably hindered by interface lags. Additionally, our technique does not require an ILP solver or a GPU. |
| Tasks | Sentence Compression |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1612/ |
| https://www.aclweb.org/anthology/D19-1612 | |
| PWC | https://paperswithcode.com/paper/query-focused-sentence-compression-in-linear-1 |
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Learning Robust Facial Landmark Detection via Hierarchical Structured Ensemble
| Title | Learning Robust Facial Landmark Detection via Hierarchical Structured Ensemble |
| Authors | Xu Zou, Sheng Zhong, Luxin Yan, Xiangyun Zhao, Jiahuan Zhou, Ying Wu |
| Abstract | Heatmap regression-based models have significantly advanced the progress of facial landmark detection. However, the lack of structural constraints always generates inaccurate heatmaps resulting in poor landmark detection performance. While hierarchical structure modeling methods have been proposed to tackle this issue, they all heavily rely on manually designed tree structures. The designed hierarchical structure is likely to be completely corrupted due to the missing or inaccurate prediction of landmarks. To the best of our knowledge, in the context of deep learning, no work before has investigated how to automatically model proper structures for facial landmarks, by discovering their inherent relations. In this paper, we propose a novel Hierarchical Structured Landmark Ensemble (HSLE) model for learning robust facial landmark detection, by using it as the structural constraints. Different from existing approaches of manually designing structures, our proposed HSLE model is constructed automatically via discovering the most robust patterns so HSLE has the ability to robustly depict both local and holistic landmark structures simultaneously. Our proposed HSLE can be readily plugged into any existing facial landmark detection baselines for further performance improvement. Extensive experimental results demonstrate our approach significantly outperforms the baseline by a large margin to achieve a state-of-the-art performance. |
| Tasks | Facial Landmark Detection |
| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Zou_Learning_Robust_Facial_Landmark_Detection_via_Hierarchical_Structured_Ensemble_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Zou_Learning_Robust_Facial_Landmark_Detection_via_Hierarchical_Structured_Ensemble_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/learning-robust-facial-landmark-detection-via |
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Robust Facial Landmark Detection via Occlusion-Adaptive Deep Networks
| Title | Robust Facial Landmark Detection via Occlusion-Adaptive Deep Networks |
| Authors | Meilu Zhu, Daming Shi, Mingjie Zheng, Muhammad Sadiq |
| Abstract | In this paper, we present a simple and effective framework called Occlusion-adaptive Deep Networks (ODN) with the purpose of solving the occlusion problem for facial landmark detection. In this model, the occlusion probability of each position in high-level features are inferred by a distillation module that can be learnt automatically in the process of estimating the relationship between facial appearance and facial shape. The occlusion probability serves as the adaptive weight on high-level features to reduce the impact of occlusion and obtain clean feature representation. Nevertheless, the clean feature representation cannot represent the holistic face due to the missing semantic features. To obtain exhaustive and complete feature representation, it is vital that we leverage a low-rank learning module to recover lost features. Considering that facial geometric characteristics are conducive to the low-rank module to recover lost features, we propose a geometry-aware module to excavate geometric relationships between different facial components. Depending on the synergistic effect of three modules, the proposed network achieves better performance in comparison to state-of-the-art methods on challenging benchmark datasets. |
| Tasks | Facial Landmark Detection |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhu_Robust_Facial_Landmark_Detection_via_Occlusion-Adaptive_Deep_Networks_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhu_Robust_Facial_Landmark_Detection_via_Occlusion-Adaptive_Deep_Networks_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/robust-facial-landmark-detection-via |
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Towards Photorealistic Reconstruction of Highly Multiplexed Lensless Images
| Title | Towards Photorealistic Reconstruction of Highly Multiplexed Lensless Images |
| Authors | Salman S. Khan, Adarsh V. R., Vivek Boominathan, Jasper Tan, Ashok Veeraraghavan, Kaushik Mitra |
| Abstract | Recent advancements in fields like Internet of Things (IoT), augmented reality, etc. have led to an unprecedented demand for miniature cameras with low cost that can be integrated anywhere and can be used for distributed monitoring. Mask-based lensless imaging systems make such inexpensive and compact models realizable. However, reduction in the size and cost of these imagers comes at the expense of their image quality due to the high degree of multiplexing inherent in their design. In this paper, we present a method to obtain image reconstructions from mask-based lensless measurements that are more photorealistic than those currently available in the literature. We particularly focus on FlatCam, a lensless imager consisting of a coded mask placed over a bare CMOS sensor. Existing techniques for reconstructing FlatCam measurements suffer from several drawbacks including lower resolution and dynamic range than lens-based cameras. Our approach overcomes these drawbacks using a fully trainable non-iterative deep learning based model. Our approach is based on two stages: an inversion stage that maps the measurement into the space of intermediate reconstruction and a perceptual enhancement stage that improves this intermediate reconstruction based on perceptual and signal distortion metrics. Our proposed method is fast and produces photo-realistic reconstruction as demonstrated on many real and challenging scenes. |
| Tasks | |
| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Khan_Towards_Photorealistic_Reconstruction_of_Highly_Multiplexed_Lensless_Images_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Khan_Towards_Photorealistic_Reconstruction_of_Highly_Multiplexed_Lensless_Images_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/towards-photorealistic-reconstruction-of |
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Podlab at SemEval-2019 Task 3: The Importance of Being Shallow
| Title | Podlab at SemEval-2019 Task 3: The Importance of Being Shallow |
| Authors | Andrew Nguyen, Tobin South, Nigel Bean, Jonathan Tuke, Lewis Mitchell |
| Abstract | This paper describes our linear SVM system for emotion classification from conversational dialogue, entered in SemEval2019 Task 3. We used off-the-shelf tools coupled with feature engineering and parameter tuning to create a simple, interpretable, yet high-performing, classification model. Our system achieves a micro F1 score of 0.7357, which is 92{%} of the top score for the competition, demonstrating that {``}shallow{''} classification approaches can perform well when coupled with detailed fea- ture selection and statistical analysis. | |
| Tasks | Emotion Classification, Feature Engineering |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-2050/ |
| https://www.aclweb.org/anthology/S19-2050 | |
| PWC | https://paperswithcode.com/paper/podlab-at-semeval-2019-task-3-the-importance |
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A Robust Local Spectral Descriptor for Matching Non-Rigid Shapes With Incompatible Shape Structures
| Title | A Robust Local Spectral Descriptor for Matching Non-Rigid Shapes With Incompatible Shape Structures |
| Authors | Yiqun Wang, Jianwei Guo, Dong-Ming Yan, Kai Wang, Xiaopeng Zhang |
| Abstract | Constructing a robust and discriminative local descriptor for 3D shape is a key component of many computer vision applications. Although existing learning-based approaches can achieve good performance in some specific benchmarks, they usually fail to learn enough information from shapes with different shape types and structures (e.g., spatial resolution, connectivity, transformations, etc.) Focusing on this issue, in this paper, we present a more discriminative local descriptor for deformable 3D shapes with incompatible structures. Based on the spectral embedding using the Laplace-Beltrami framework on the surface, we first construct a novel local spectral feature which shows great resilience to change in mesh resolution, triangulation, transformation. Then the multi-scale local spectral features around each vertex are encoded into a `geometry image’, called vertex spectral image, in a very compact way. Such vertex spectral images can be efficiently trained to learn local descriptors using a triplet neural network. Finally, for training and evaluation, we present a new benchmark dataset by extending the widely used FAUST dataset. We utilize a remeshing approach to generate modified shapes with different structures. We evaluate the proposed approach thoroughly and make an extensive comparison to demonstrate that our approach outperforms recent state-of-the-art methods on this benchmark. | |
| Tasks | |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Wang_A_Robust_Local_Spectral_Descriptor_for_Matching_Non-Rigid_Shapes_With_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Wang_A_Robust_Local_Spectral_Descriptor_for_Matching_Non-Rigid_Shapes_With_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/a-robust-local-spectral-descriptor-for |
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