Paper Group NANR 76
DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs. Inferring Latent Velocities from Weather Radar Data using Gaussian Processes. Occluded Pedestrian Detection Through Guided Attention in CNNs. Learning to Generate Move-by-Move Commentary for Chess Games from Large-Scale Social Forum Data. Integrating Tree Str …
DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs
| Title | DDRNet: Depth Map Denoising and Refinement for Consumer Depth Cameras Using Cascaded CNNs |
| Authors | Shi Yan, Chenglei Wu, Lizhen Wang, Feng Xu, Liang An, Kaiwen Guo, Yebin Liu |
| Abstract | Consumer depth sensors are more and more popular and come to our daily lives marked by its recent integration in the latest Iphone X. However, they still suffer from heavy noises which limit their applications. Although plenty of progresses have been made to reduce the noises and boost geometric details, due to the inherent illness and the real-time requirement, the problem is still far from been solved. We propose a cascaded Depth Denoising and Refinement Network (DDRNet) to tackle this problem by leveraging the multi-frame fused geometry and the accompanying high quality color image through a joint training strategy. The rendering equation is exploited in our network in an unsupervised manner. In detail, we impose an unsupervised loss based on the light transport to extract the high-frequency geometry. Experimental results indicate that our network achieves real-time single depth enhancement on various categories of scenes. Thanks to the well decoupling of the low and high frequency information in the cascaded network, we achieve superior performance over the state-of-the-art techniques. |
| Tasks | Denoising |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Shi_Yan_DDRNet_Depth_Map_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Shi_Yan_DDRNet_Depth_Map_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/ddrnet-depth-map-denoising-and-refinement-for |
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Inferring Latent Velocities from Weather Radar Data using Gaussian Processes
| Title | Inferring Latent Velocities from Weather Radar Data using Gaussian Processes |
| Authors | Rico Angell, Daniel R. Sheldon |
| Abstract | Archived data from the US network of weather radars hold detailed information about bird migration over the last 25 years, including very high-resolution partial measurements of velocity. Historically, most of this spatial resolution is discarded and velocities are summarized at a very small number of locations due to modeling and algorithmic limitations. This paper presents a Gaussian process (GP) model to reconstruct high-resolution full velocity fields across the entire US. The GP faithfully models all aspects of the problem in a single joint framework, including spatially random velocities, partial velocity measurements, station-specific geometries, measurement noise, and an ambiguity known as aliasing. We develop fast inference algorithms based on the FFT; to do so, we employ a creative use of Laplace’s method to sidestep the fact that the kernel of the joint process is non-stationary. |
| Tasks | Gaussian Processes |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/8113-inferring-latent-velocities-from-weather-radar-data-using-gaussian-processes |
| http://papers.nips.cc/paper/8113-inferring-latent-velocities-from-weather-radar-data-using-gaussian-processes.pdf | |
| PWC | https://paperswithcode.com/paper/inferring-latent-velocities-from-weather |
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Occluded Pedestrian Detection Through Guided Attention in CNNs
| Title | Occluded Pedestrian Detection Through Guided Attention in CNNs |
| Authors | Shanshan Zhang, Jian Yang, Bernt Schiele |
| Abstract | Pedestrian detection has progressed significantly in the last years. However, occluded people are notoriously hard to detect, as their appearance varies substantially depending on a wide range of partial occlusions. In this paper, we aim to propose a simple and compact method based on the FasterRCNN architecture for occluded pedestrian detection. We start with interpreting CNN channel features of a pedestrian detector, and we find that different channels activate responses for different body parts respectively. These findings strongly motivate us to employ an attention mechanism across channels to represent various occlusion patterns in one single model, as each occlusion pattern can be formulated as some specific combination of body parts. Therefore, an attention network with self or external guidances is proposed as an add-on to the baseline FasterRCNN detector. When evaluating on the heavy occlusion subset, we achieve a significant improvement of 8pp to the baseline FasterRCNN detector on CityPersons and on Caltech we outperform the state-of-the-art method by 4pp. |
| Tasks | Pedestrian Detection |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Zhang_Occluded_Pedestrian_Detection_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Zhang_Occluded_Pedestrian_Detection_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/occluded-pedestrian-detection-through-guided |
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Learning to Generate Move-by-Move Commentary for Chess Games from Large-Scale Social Forum Data
| Title | Learning to Generate Move-by-Move Commentary for Chess Games from Large-Scale Social Forum Data |
| Authors | Harsh Jhamtani, Varun Gangal, Eduard Hovy, Graham Neubig, Taylor Berg-Kirkpatrick |
| Abstract | This paper examines the problem of generating natural language descriptions of chess games. We introduce a new large-scale chess commentary dataset and propose methods to generate commentary for individual moves in a chess game. The introduced dataset consists of more than 298K chess move-commentary pairs across 11K chess games. We highlight how this task poses unique research challenges in natural language generation: the data contain a large variety of styles of commentary and frequently depend on pragmatic context. We benchmark various baselines and propose an end-to-end trainable neural model which takes into account multiple pragmatic aspects of the game state that may be commented upon to describe a given chess move. Through a human study on predictions for a subset of the data which deals with direct move descriptions, we observe that outputs from our models are rated similar to ground truth commentary texts in terms of correctness and fluency. |
| Tasks | Game of Chess, Text Generation |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-1154/ |
| https://www.aclweb.org/anthology/P18-1154 | |
| PWC | https://paperswithcode.com/paper/learning-to-generate-move-by-move-commentary |
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Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts
| Title | Integrating Tree Structures and Graph Structures with Neural Networks to Classify Discussion Discourse Acts |
| Authors | Yasuhide Miura, Ryuji Kano, Motoki Taniguchi, Tomoki Taniguchi, Shotaro Misawa, Tomoko Ohkuma |
| Abstract | We proposed a model that integrates discussion structures with neural networks to classify discourse acts. Several attempts have been made in earlier works to analyze texts that are used in various discussions. The importance of discussion structures has been explored in those works but their methods required a sophisticated design to combine structural features with a classifier. Our model introduces tree learning approaches and a graph learning approach to directly capture discussion structures without structural features. In an evaluation to classify discussion discourse acts in Reddit, the model achieved improvements of 1.5{%} in accuracy and 2.2 in FB1 score compared to the previous best model. We further analyzed the model using an attention mechanism to inspect interactions among different learning approaches. |
| Tasks | Structured Prediction |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1322/ |
| https://www.aclweb.org/anthology/C18-1322 | |
| PWC | https://paperswithcode.com/paper/integrating-tree-structures-and-graph |
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Extracting Syntactic Trees from Transformer Encoder Self-Attentions
| Title | Extracting Syntactic Trees from Transformer Encoder Self-Attentions |
| Authors | David Mare{\v{c}}ek, Rudolf Rosa |
| Abstract | This is a work in progress about extracting the sentence tree structures from the encoder{'}s self-attention weights, when translating into another language using the Transformer neural network architecture. We visualize the structures and discuss their characteristics with respect to the existing syntactic theories and annotations. |
| Tasks | Machine Translation |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-5444/ |
| https://www.aclweb.org/anthology/W18-5444 | |
| PWC | https://paperswithcode.com/paper/extracting-syntactic-trees-from-transformer |
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Portable, layer-wise task performance monitoring for NLP models
| Title | Portable, layer-wise task performance monitoring for NLP models |
| Authors | Tom Lippincott |
| Abstract | There is a long-standing interest in understanding the internal behavior of neural networks. Deep neural architectures for natural language processing (NLP) are often accompanied by explanations for their effectiveness, from general observations (e.g. RNNs can represent unbounded dependencies in a sequence) to specific arguments about linguistic phenomena (early layers encode lexical information, deeper layers syntactic). The recent ascendancy of DNNs is fueling efforts in the NLP community to explore these claims. Previous work has tended to focus on easily-accessible representations like word or sentence embeddings, with deeper structure requiring more ad hoc methods to extract and examine. In this work, we introduce Vivisect, a toolkit that aims at a general solution for broad and fine-grained monitoring in the major DNN frameworks, with minimal change to research patterns. |
| Tasks | Machine Translation, Sentence Embeddings |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-5445/ |
| https://www.aclweb.org/anthology/W18-5445 | |
| PWC | https://paperswithcode.com/paper/portable-layer-wise-task-performance |
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Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network
| Title | Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network |
| Authors | Jianfei Yu, Lu{'\i}s Marujo, Jing Jiang, Pradeep Karuturi, William Brendel |
| Abstract | In this paper, we target at improving the performance of multi-label emotion classification with the help of sentiment classification. Specifically, we propose a new transfer learning architecture to divide the sentence representation into two different feature spaces, which are expected to respectively capture the general sentiment words and the other important emotion-specific words via a dual attention mechanism. Experimental results on two benchmark datasets demonstrate the effectiveness of our proposed method. |
| Tasks | Emotion Classification, Multi-Label Classification, Sentiment Analysis, Stock Market Prediction, Transfer Learning |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/D18-1137/ |
| https://www.aclweb.org/anthology/D18-1137 | |
| PWC | https://paperswithcode.com/paper/improving-multi-label-emotion-classification |
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Representation of Word Meaning in the Intermediate Projection Layer of a Neural Language Model
| Title | Representation of Word Meaning in the Intermediate Projection Layer of a Neural Language Model |
| Authors | Steven Derby, Paul Miller, Brian Murphy, Barry Devereux |
| Abstract | Performance in language modelling has been significantly improved by training recurrent neural networks on large corpora. This progress has come at the cost of interpretability and an understanding of how these architectures function, making principled development of better language models more difficult. We look inside a state-of-the-art neural language model to analyse how this model represents high-level lexico-semantic information. In particular, we investigate how the model represents words by extracting activation patterns where they occur in the text, and compare these representations directly to human semantic knowledge. |
| Tasks | Language Modelling, Time Series |
| Published | 2018-11-01 |
| URL | https://www.aclweb.org/anthology/W18-5449/ |
| https://www.aclweb.org/anthology/W18-5449 | |
| PWC | https://paperswithcode.com/paper/representation-of-word-meaning-in-the |
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Model-Free Context-Aware Word Composition
| Title | Model-Free Context-Aware Word Composition |
| Authors | Bo An, Xianpei Han, Le Sun |
| Abstract | Word composition is a promising technique for representation learning of large linguistic units (e.g., phrases, sentences and documents). However, most of the current composition models do not take the ambiguity of words and the context outside of a linguistic unit into consideration for learning representations, and consequently suffer from the inaccurate representation of semantics. To address this issue, we propose a model-free context-aware word composition model, which employs the latent semantic information as global context for learning representations. The proposed model attempts to resolve the word sense disambiguation and word composition in a unified framework. Extensive evaluation shows consistent improvements over various strong word representation/composition models at different granularities (including word, phrase and sentence), demonstrating the effectiveness of our proposed method. |
| Tasks | Dimensionality Reduction, Learning Word Embeddings, Machine Translation, Representation Learning, Sentiment Analysis, Word Embeddings, Word Sense Disambiguation |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1240/ |
| https://www.aclweb.org/anthology/C18-1240 | |
| PWC | https://paperswithcode.com/paper/model-free-context-aware-word-composition |
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The Role of Emotions in Native Language Identification
| Title | The Role of Emotions in Native Language Identification |
| Authors | Ilia Markov, Vivi Nastase, Carlo Strapparava, Grigori Sidorov |
| Abstract | We explore the hypothesis that emotion is one of the dimensions of language that surfaces from the native language into a second language. To check the role of emotions in native language identification (NLI), we model emotion information through polarity and emotion load features, and use document representations using these features to classify the native language of the author. The results indicate that emotion is relevant for NLI, even for high proficiency levels and across topics. |
| Tasks | Deception Detection, Language Identification, Native Language Identification, Sentiment Analysis |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/W18-6218/ |
| https://www.aclweb.org/anthology/W18-6218 | |
| PWC | https://paperswithcode.com/paper/the-role-of-emotions-in-native-language |
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Leveraging Writing Systems Change for Deep Learning Based Chinese Emotion Analysis
| Title | Leveraging Writing Systems Change for Deep Learning Based Chinese Emotion Analysis |
| Authors | Rong Xiang, Yunfei Long, Qin Lu, Dan Xiong, I-Hsuan Chen |
| Abstract | Social media text written in Chinese communities contains mixed scripts including major text written in Chinese, an ideograph-based writing system, and some minor text using Latin letters, an alphabet-based writing system. This phenomenon is called writing systems changes (WSCs). Past studies have shown that WSCs can be used to express emotions, particularly where the social and political environment is more conservative. However, because WSCs can break the syntax of the major text, it poses more challenges in Natural Language Processing (NLP) tasks like emotion classification. In this work, we present a novel deep learning based method to include WSCs as an effective feature for emotion analysis. The method first identifies all WSCs points. Then representation of the major text is learned through an LSTM model whereas the minor text is learned by a separate CNN model. Emotions in the minor text are further highlighted through an attention mechanism before emotion classification. Performance evaluation shows that incorporating WSCs features using deep learning models can improve performance measured by F1-scores compared to the state-of-the-art model. |
| Tasks | Emotion Classification, Emotion Recognition |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/W18-6214/ |
| https://www.aclweb.org/anthology/W18-6214 | |
| PWC | https://paperswithcode.com/paper/leveraging-writing-systems-change-for-deep |
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NLP at IEST 2018: BiLSTM-Attention and LSTM-Attention via Soft Voting in Emotion Classification
| Title | NLP at IEST 2018: BiLSTM-Attention and LSTM-Attention via Soft Voting in Emotion Classification |
| Authors | Qimin Zhou, Hao Wu |
| Abstract | This paper describes our method that competed at WASSA2018 \textit{Implicit Emotion Shared Task}. The goal of this task is to classify the emotions of excluded words in tweets into six different classes: sad, joy, disgust, surprise, anger and fear. For this, we examine a BiLSTM architecture with attention mechanism (BiLSTM-Attention) and a LSTM architecture with attention mechanism (LSTM-Attention), and try different dropout rates based on these two models. We then exploit an ensemble of these methods to give the final prediction which improves the model performance significantly compared with the baseline model. The proposed method achieves 7th position out of 30 teams and outperforms the baseline method by 12.5{%} in terms of \textit{macro F1}. |
| Tasks | Emotion Classification, Sentiment Analysis |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/W18-6226/ |
| https://www.aclweb.org/anthology/W18-6226 | |
| PWC | https://paperswithcode.com/paper/nlp-at-iest-2018-bilstm-attention-and-lstm |
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SINAI at IEST 2018: Neural Encoding of Emotional External Knowledge for Emotion Classification
| Title | SINAI at IEST 2018: Neural Encoding of Emotional External Knowledge for Emotion Classification |
| Authors | Flor Miriam Plaza-del-Arco, Eugenio Mart{'\i}nez-C{'a}mara, Maite Martin, L. Alfonso Ure{~n}a- L{'o}pez |
| Abstract | In this paper, we describe our participation in WASSA 2018 Implicit Emotion Shared Task (IEST 2018). We claim that the use of emotional external knowledge may enhance the performance and the capacity of generalization of an emotion classification system based on neural networks. Accordingly, we submitted four deep learning systems grounded in a sequence encoding layer. They mainly differ in the feature vector space and the recurrent neural network used in the sequence encoding layer. The official results show that the systems that used emotional external knowledge have a higher capacity of generalization, hence our claim holds. |
| Tasks | Emotion Classification, Emotion Recognition, Sentiment Analysis |
| Published | 2018-10-01 |
| URL | https://www.aclweb.org/anthology/W18-6227/ |
| https://www.aclweb.org/anthology/W18-6227 | |
| PWC | https://paperswithcode.com/paper/sinai-at-iest-2018-neural-encoding-of |
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Tel(s)-Telle(s)-Signs: Highly Accurate Automatic Crosslingual Hypernym Discovery
| Title | Tel(s)-Telle(s)-Signs: Highly Accurate Automatic Crosslingual Hypernym Discovery |
| Authors | Ada Wan |
| Abstract | |
| Tasks | Hypernym Discovery, Prepositional Phrase Attachment, Relation Extraction, Transfer Learning, Word Alignment |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1096/ |
| https://www.aclweb.org/anthology/L18-1096 | |
| PWC | https://paperswithcode.com/paper/tels-telles-signs-highly-accurate-automatic |
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