Paper Group NANR 123
Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation. Bayesian Time Series Forecasting with Change Point and Anomaly Detection. Expert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings. Ant Colony System for Multi-Document Summarization. X2Face: A net …
Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation
| Title | Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation |
| Authors | Yongchan Kwon, Joong-Ho Won, Beom Joon Kim, Myunghee Cho Paik |
| Abstract | Most recent research of neural networks in the field of computer vision has focused on improving accuracy of point predictions by developing various network architectures or learning algorithms. Uncertainty quantification accompanied by point estimation can lead to a more informed decision, and the quality of prediction can be improved. In medical imaging applications, assessment of uncertainty could potentially reduce untoward outcomes due to suboptimal decisions. In this paper, we invoke a Bayesian neural network and propose a natural way to quantify uncertainty in classification problems by decomposing predictive uncertainty into two parts, aleatoric and epistemic uncertainty. The proposed method takes into account discrete nature of the outcome, yielding correct interpretation of each uncertainty. We demonstrate that the proposed uncertainty quantification method provides additional insight to the point prediction using images from the Ischemic Stroke Lesion Segmentation Challenge. |
| Tasks | Ischemic Stroke Lesion Segmentation, Lesion Segmentation |
| Published | 2018-04-10 |
| URL | https://openreview.net/forum?id=Sk_P2Q9sG |
| https://openreview.net/pdf?id=Sk_P2Q9sG | |
| PWC | https://paperswithcode.com/paper/uncertainty-quantification-using-bayesian |
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Bayesian Time Series Forecasting with Change Point and Anomaly Detection
| Title | Bayesian Time Series Forecasting with Change Point and Anomaly Detection |
| Authors | Anderson Y. Zhang, Miao Lu, Deguang Kong, Jimmy Yang |
| Abstract | Time series forecasting plays a crucial role in marketing, finance and many other quantitative fields. A large amount of methodologies has been developed on this topic, including ARIMA, Holt–Winters, etc. However, their performance is easily undermined by the existence of change points and anomaly points, two structures commonly observed in real data, but rarely considered in the aforementioned methods. In this paper, we propose a novel state space time series model, with the capability to capture the structure of change points and anomaly points, as well as trend and seasonality. To infer all the hidden variables, we develop a Bayesian framework, which is able to obtain distributions and forecasting intervals for time series forecasting, with provable theoretical properties. For implementation, an iterative algorithm with Markov chain Monte Carlo (MCMC), Kalman filter and Kalman smoothing is proposed. In both synthetic data and real data applications, our methodology yields a better performance in time series forecasting compared with existing methods, along with more accurate change point detection and anomaly detection. |
| Tasks | Anomaly Detection, Change Point Detection, Time Series, Time Series Forecasting |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=rJLTTe-0W |
| https://openreview.net/pdf?id=rJLTTe-0W | |
| PWC | https://paperswithcode.com/paper/bayesian-time-series-forecasting-with-change |
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Expert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings
| Title | Expert, Crowdsourced, and Machine Assessment of Suicide Risk via Online Postings |
| Authors | Han-Chin Shing, Suraj Nair, Ayah Zirikly, Meir Friedenberg, Hal Daum{'e} III, Philip Resnik |
| Abstract | We report on the creation of a dataset for studying assessment of suicide risk via online postings in Reddit. Evaluation of risk-level annotations by experts yields what is, to our knowledge, the first demonstration of reliability in risk assessment by clinicians based on social media postings. We also introduce and demonstrate the value of a new, detailed rubric for assessing suicide risk, compare crowdsourced with expert performance, and present baseline predictive modeling experiments using the new dataset, which will be made available to researchers through the American Association of Suicidology. |
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| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/W18-0603/ |
| https://www.aclweb.org/anthology/W18-0603 | |
| PWC | https://paperswithcode.com/paper/expert-crowdsourced-and-machine-assessment-of |
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Ant Colony System for Multi-Document Summarization
| Title | Ant Colony System for Multi-Document Summarization |
| Authors | Asma Al-Saleh, Mohamed El Bachir Menai |
| Abstract | This paper proposes an extractive multi-document summarization approach based on an ant colony system to optimize the information coverage of summary sentences. The implemented system was evaluated on both English and Arabic versions of the corpus of the Text Analysis Conference 2011 MultiLing Pilot by using ROUGE metrics. The evaluation results are promising in comparison to those of the participating systems. Indeed, our system achieved the best scores based on several ROUGE metrics. |
| Tasks | Document Summarization, Multi-Document Summarization, Text Summarization |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1062/ |
| https://www.aclweb.org/anthology/C18-1062 | |
| PWC | https://paperswithcode.com/paper/ant-colony-system-for-multi-document |
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X2Face: A network for controlling face generation using images, audio, and pose codes
| Title | X2Face: A network for controlling face generation using images, audio, and pose codes |
| Authors | Olivia Wiles, A. Sophia Koepke, Andrew Zisserman |
| Abstract | The objective of this paper is a neural network model that controls the pose and expression of a given face, using another face or modality (e.g. audio). This model can then be used for lightweight, sophisticated video and image editing. We make the following three contributions. First, we introduce a network, X2Face, that can control a source face (specified by one or more frames) using another face in a driving frame to produce a generated frame with the identity of the source frame but the pose and expression of the face in the driving frame. Second, we propose a method for training the network fully self-supervised using a large collection of video data. Third, we show that the generation process can be driven by other modalities, such as audio or pose codes, without any further training of the network. The generation results for driving a face with another face are compared to state-of-the-art self-supervised/supervised methods. We show that our approach is more robust than other methods, as it makes fewer assumptions about the input data. We also show examples of using our framework for video face editing. |
| Tasks | Face Generation, Talking Head Generation |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Olivia_Wiles_X2Face_A_network_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Olivia_Wiles_X2Face_A_network_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/x2face-a-network-for-controlling-face-1 |
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Neural Syntactic Generative Models with Exact Marginalization
| Title | Neural Syntactic Generative Models with Exact Marginalization |
| Authors | Jan Buys, Phil Blunsom |
| Abstract | We present neural syntactic generative models with exact marginalization that support both dependency parsing and language modeling. Exact marginalization is made tractable through dynamic programming over shift-reduce parsing and minimal RNN-based feature sets. Our algorithms complement previous approaches by supporting batched training and enabling online computation of next word probabilities. For supervised dependency parsing, our model achieves a state-of-the-art result among generative approaches. We also report empirical results on unsupervised syntactic models and their role in language modeling. We find that our model formulation of latent dependencies with exact marginalization do not lead to better intrinsic language modeling performance than vanilla RNNs, and that parsing accuracy is not correlated with language modeling perplexity in stack-based models. |
| Tasks | Dependency Parsing, Language Modelling, Transition-Based Dependency Parsing |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-1086/ |
| https://www.aclweb.org/anthology/N18-1086 | |
| PWC | https://paperswithcode.com/paper/neural-syntactic-generative-models-with-exact |
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Cache Transition Systems for Graph Parsing
| Title | Cache Transition Systems for Graph Parsing |
| Authors | Daniel Gildea, Giorgio Satta, Xiaochang Peng |
| Abstract | Motivated by the task of semantic parsing, we describe a transition system that generalizes standard transition-based dependency parsing techniques to generate a graph rather than a tree. Our system includes a cache with fixed size m, and we characterize the relationship between the parameter m and the class of graphs that can be produced through the graph-theoretic concept of tree decomposition. We find empirically that small cache sizes cover a high percentage of sentences in existing semantic corpora. |
| Tasks | Dependency Parsing, Semantic Parsing, Transition-Based Dependency Parsing |
| Published | 2018-03-01 |
| URL | https://www.aclweb.org/anthology/J18-1004/ |
| https://www.aclweb.org/anthology/J18-1004 | |
| PWC | https://paperswithcode.com/paper/cache-transition-systems-for-graph-parsing |
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Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy
| Title | Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy |
| Authors | Deepak Gupta, Rajkumar Pujari, Asif Ekbal, Pushpak Bhattacharyya, Anutosh Maitra, Tom Jain, Shubhashis Sengupta |
| Abstract | In this paper, we propose a hybrid technique for semantic question matching. It uses a proposed two-layered taxonomy for English questions by augmenting state-of-the-art deep learning models with question classes obtained from a deep learning based question classifier. Experiments performed on three open-domain datasets demonstrate the effectiveness of our proposed approach. We achieve state-of-the-art results on partial ordering question ranking (POQR) benchmark dataset. Our empirical analysis shows that coupling standard distributional features (provided by the question encoder) with knowledge from taxonomy is more effective than either deep learning or taxonomy-based knowledge alone. |
| Tasks | Question Answering, Semantic Textual Similarity |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1042/ |
| https://www.aclweb.org/anthology/C18-1042 | |
| PWC | https://paperswithcode.com/paper/can-taxonomy-help-improving-semantic-question |
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Online Hyper-Parameter Optimization
| Title | Online Hyper-Parameter Optimization |
| Authors | Damien Vincent, Sylvain Gelly, Nicolas Le Roux, Olivier Bousquet |
| Abstract | We propose an efficient online hyperparameter optimization method which uses a joint dynamical system to evaluate the gradient with respect to the hyperparameters. While similar methods are usually limited to hyperparameters with a smooth impact on the model, we show how to apply it to the probability of dropout in neural networks. Finally, we show its effectiveness on two distinct tasks. |
| Tasks | Hyperparameter Optimization |
| Published | 2018-01-01 |
| URL | https://openreview.net/forum?id=H1OQukZ0- |
| https://openreview.net/pdf?id=H1OQukZ0- | |
| PWC | https://paperswithcode.com/paper/online-hyper-parameter-optimization |
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Enhancing General Sentiment Lexicons for Domain-Specific Use
| Title | Enhancing General Sentiment Lexicons for Domain-Specific Use |
| Authors | Tim Kreutz, Walter Daelemans |
| Abstract | Lexicon based methods for sentiment analysis rely on high quality polarity lexicons. In recent years, automatic methods for inducing lexicons have increased the viability of lexicon based methods for polarity classification. SentProp is a framework for inducing domain-specific polarities from word embeddings. We elaborate on SentProp by evaluating its use for enhancing DuOMan, a general-purpose lexicon, for use in the political domain. By adding only top sentiment bearing words from the vocabulary and applying small polarity shifts in the general-purpose lexicon, we increase accuracy in an in-domain classification task. The enhanced lexicon performs worse than the original lexicon in an out-domain task, showing that the words we added and the polarity shifts we applied are domain-specific and do not translate well to an out-domain setting. |
| Tasks | Domain Adaptation, Sentiment Analysis, Word Embeddings |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1090/ |
| https://www.aclweb.org/anthology/C18-1090 | |
| PWC | https://paperswithcode.com/paper/enhancing-general-sentiment-lexicons-for |
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Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models
| Title | Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models |
| Authors | Simon Dobnik, Mehdi Ghanimifard, John Kelleher |
| Abstract | The challenge for computational models of spatial descriptions for situated dialogue systems is the integration of information from different modalities. The semantics of spatial descriptions are grounded in at least two sources of information: (i) a geometric representation of space and (ii) the functional interaction of related objects that. We train several neural language models on descriptions of scenes from a dataset of image captions and examine whether the functional or geometric bias of spatial descriptions reported in the literature is reflected in the estimated perplexity of these models. The results of these experiments have implications for the creation of models of spatial lexical semantics for human-robot dialogue systems. Furthermore, they also provide an insight into the kinds of the semantic knowledge captured by neural language models trained on spatial descriptions, which has implications for image captioning systems. |
| Tasks | Image Captioning |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/W18-1401/ |
| https://www.aclweb.org/anthology/W18-1401 | |
| PWC | https://paperswithcode.com/paper/exploring-the-functional-and-geometric-bias |
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Automated Scoring: Beyond Natural Language Processing
| Title | Automated Scoring: Beyond Natural Language Processing |
| Authors | Nitin Madnani, Aoife Cahill |
| Abstract | In this position paper, we argue that building operational automated scoring systems is a task that has disciplinary complexity above and beyond standard competitive shared tasks which usually involve applying the latest machine learning techniques to publicly available data in order to obtain the best accuracy. Automated scoring systems warrant significant cross-discipline collaboration of which natural language processing and machine learning are just two of many important components. Such systems have multiple stakeholders with different but valid perspectives that can often times be at odds with each other. Our position is that it is essential for us as NLP researchers to understand and incorporate these perspectives in our research and work towards a mutually satisfactory solution in order to build automated scoring systems that are accurate, fair, unbiased, and useful. |
| Tasks | |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1094/ |
| https://www.aclweb.org/anthology/C18-1094 | |
| PWC | https://paperswithcode.com/paper/automated-scoring-beyond-natural-language |
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Are BLEU and Meaning Representation in Opposition?
| Title | Are BLEU and Meaning Representation in Opposition? |
| Authors | Ond{\v{r}}ej C{'\i}fka, Ond{\v{r}}ej Bojar |
| Abstract | One of possible ways of obtaining continuous-space sentence representations is by training neural machine translation (NMT) systems. The recent attention mechanism however removes the single point in the neural network from which the source sentence representation can be extracted. We propose several variations of the attentive NMT architecture bringing this meeting point back. Empirical evaluation suggests that the better the translation quality, the worse the learned sentence representations serve in a wide range of classification and similarity tasks. |
| Tasks | Machine Translation, Semantic Textual Similarity |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-1126/ |
| https://www.aclweb.org/anthology/P18-1126 | |
| PWC | https://paperswithcode.com/paper/are-bleu-and-meaning-representation-in-1 |
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FaceID-GAN: Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis
| Title | FaceID-GAN: Learning a Symmetry Three-Player GAN for Identity-Preserving Face Synthesis |
| Authors | Yujun Shen, Ping Luo, Junjie Yan, Xiaogang Wang, Xiaoou Tang |
| Abstract | Face synthesis has achieved advanced development by using generative adversarial networks (GANs). Existing methods typically formulate GAN as a two-player game, where a discriminator distinguishes face images from the real and synthesized domains, while a generator reduces its discriminativeness by synthesizing a face of photo-realistic quality. Their competition converges when the discriminator is unable to differentiate these two domains. Unlike two-player GANs, this work generates identity-preserving faces by proposing FaceID-GAN, which treats a classifier of face identity as the third player, competing with the generator by distinguishing the identities of the real and synthesized faces (see Fig.1). A stationary point is reached when the generator produces faces that have high quality as well as preserve identity. Instead of simply modeling the identity classifier as an additional discriminator, FaceID-GAN is formulated by satisfying information symmetry, which ensures that the real and synthesized images are projected into the same feature space. In other words, the identity classifier is used to extract identity features from both input (real) and output (synthesized) face images of the generator, substantially alleviating training difficulty of GAN. Extensive experiments show that FaceID-GAN is able to generate faces of arbitrary viewpoint while preserve identity, outperforming recent advanced approaches. |
| Tasks | Face Generation |
| Published | 2018-06-01 |
| URL | http://openaccess.thecvf.com/content_cvpr_2018/html/Shen_FaceID-GAN_Learning_a_CVPR_2018_paper.html |
| http://openaccess.thecvf.com/content_cvpr_2018/papers/Shen_FaceID-GAN_Learning_a_CVPR_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/faceid-gan-learning-a-symmetry-three-player |
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LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics
| Title | LSDSCC: a Large Scale Domain-Specific Conversational Corpus for Response Generation with Diversity Oriented Evaluation Metrics |
| Authors | Zhen Xu, Nan Jiang, Bingquan Liu, Wenge Rong, Bowen Wu, Baoxun Wang, Zhuoran Wang, Xiaolong Wang |
| Abstract | It has been proven that automatic conversational agents can be built up using the Endto-End Neural Response Generation (NRG) framework, and such a data-driven methodology requires a large number of dialog pairs for model training and reasonable evaluation metrics for testing. This paper proposes a Large Scale Domain-Specific Conversational Corpus (LSDSCC) composed of high-quality queryresponse pairs extracted from the domainspecific online forum, with thorough preprocessing and cleansing procedures. Also, a testing set, including multiple diverse responses annotated for each query, is constructed, and on this basis, the metrics for measuring the diversity of generated results are further presented. We evaluate the performances of neural dialog models with the widely applied diversity boosting strategies on the proposed dataset. The experimental results have shown that our proposed corpus can be taken as a new benchmark dataset for the NRG task, and the presented metrics are promising to guide the optimization of NRG models by quantifying the diversity of the generated responses reasonably. |
| Tasks | Machine Translation |
| Published | 2018-06-01 |
| URL | https://www.aclweb.org/anthology/N18-1188/ |
| https://www.aclweb.org/anthology/N18-1188 | |
| PWC | https://paperswithcode.com/paper/lsdscc-a-large-scale-domain-specific |
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