Paper Group NANR 132
Word Embeddings-Based Uncertainty Detection in Financial Disclosures. EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogues. Sentence Classification for Investment Rules Detection. Significantly Fast and Robust Fuzzy C-MeansClustering Algorithm Based on MorphologicalReconstruction and Membership Filtering. A Helping Han …
Word Embeddings-Based Uncertainty Detection in Financial Disclosures
| Title | Word Embeddings-Based Uncertainty Detection in Financial Disclosures |
| Authors | Christoph Kilian Theil, Sanja {\v{S}}tajner, Heiner Stuckenschmidt |
| Abstract | In this paper, we use NLP techniques to detect linguistic uncertainty in financial disclosures. Leveraging general-domain and domain-specific word embedding models, we automatically expand an existing dictionary of uncertainty triggers. We furthermore examine how an expert filtering affects the quality of such an expansion. We show that the dictionary expansions significantly improve regressions on stock return volatility. Lastly, we prove that the expansions significantly boost the automatic detection of uncertain sentences. |
| Tasks | Sentence Classification, Word Embeddings |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-3104/ |
| https://www.aclweb.org/anthology/W18-3104 | |
| PWC | https://paperswithcode.com/paper/word-embeddings-based-uncertainty-detection |
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EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogues
| Title | EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogues |
| Authors | Linkai Luo, Haiqin Yang, Francis Y. L. Chin |
| Abstract | In this paper, we propose a self-attentive bidirectional long short-term memory (SA-BiLSTM) network to predict multiple emotions for the EmotionX challenge. The BiLSTM exhibits the power of modeling the word dependencies, and extracting the most relevant features for emotion classification. Building on top of BiLSTM, the self-attentive network can model the contextual dependencies between utterances which are helpful for classifying the ambiguous emotions. We achieve 59.6 and 55.0 unweighted accuracy scores in the Friends and the EmotionPush test sets, respectively. |
| Tasks | Emotion Classification, Sentence Classification, Sentence Embedding |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-3506/ |
| https://www.aclweb.org/anthology/W18-3506 | |
| PWC | https://paperswithcode.com/paper/emotionx-dlc-self-attentive-bilstm-for |
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Sentence Classification for Investment Rules Detection
| Title | Sentence Classification for Investment Rules Detection |
| Authors | Youness Mansar, Sira Ferradans |
| Abstract | In the last years, compliance requirements for the banking sector have greatly augmented, making the current compliance processes difficult to maintain. Any process that allows to accelerate the identification and implementation of compliance requirements can help address this issues. The contributions of the paper are twofold: we propose a new NLP task that is the investment rule detection, and a group of methods identify them. We show that the proposed methods are highly performing and fast, thus can be deployed in production. |
| Tasks | Sentence Classification |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-3106/ |
| https://www.aclweb.org/anthology/W18-3106 | |
| PWC | https://paperswithcode.com/paper/sentence-classification-for-investment-rules |
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Significantly Fast and Robust Fuzzy C-MeansClustering Algorithm Based on MorphologicalReconstruction and Membership Filtering
| Title | Significantly Fast and Robust Fuzzy C-MeansClustering Algorithm Based on MorphologicalReconstruction and Membership Filtering |
| Authors | Tao Lei, Xiaohong Jia, Yanning Zhang, Senior Member, IEEE, Lifeng He, Senior Member, IEEE, Hongy-ing Meng, Senior Member, IEEE, and Asoke K. Nandi, Fellow, IEEE |
| Abstract | As fuzzy c-means clustering (FCM) algorithm issensitive to noise, local spatial information is often introducedto an objective function to improve the robustness of the FCMalgorithm for image segmentation. However, the introduction oflocal spatial information often leads to a high computationalcomplexity, arising out of an iterative calculation of the distancebetween pixels within local spatial neighbors and clusteringcenters. To address this issue, an improved FCM algorithmbased on morphological reconstruction and membership filtering(FRFCM) that is significantly faster and more robust than FCM,is proposed in this paper. Firstly, the local spatial informationof images is incorporated into FRFCM by introducing mor-phological reconstruction operation to guarantee noise-immunityand image detail-preservation. Secondly, the modification ofmembership partition, based on the distance between pixelswithin local spatial neighbors and clustering centers, is replacedby local membership filtering that depends only on the spatialneighbors of membership partition. Compared to state-of-the-art algorithms, the proposed FRFCM algorithm is simpler andsignificantly faster, since it is unnecessary to compute the distancebetween pixels within local spatial neighbors and clusteringcenters. In addition, it is efficient for noisy image segmentationbecause membership filtering are able to improve membershippartition matrix efficiently. Experiments performed on syntheticand real-world images demonstrate that the proposed algorithmnot only achieves better results, but also requires less time thanstate-of-the-art algorithms for image segmentation. |
| Tasks | Semantic Segmentation |
| Published | 2018-01-23 |
| URL | https://ieeexplore.ieee.org/document/8265186 |
| https://ieeexplore.ieee.org/document/8265186 | |
| PWC | https://paperswithcode.com/paper/significantly-fast-and-robust-fuzzy-c |
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A Helping Hand: Transfer Learning for Deep Sentiment Analysis
| Title | A Helping Hand: Transfer Learning for Deep Sentiment Analysis |
| Authors | Xin Dong, Gerard de Melo |
| Abstract | Deep convolutional neural networks excel at sentiment polarity classification, but tend to require substantial amounts of training data, which moreover differs quite significantly between domains. In this work, we present an approach to feed generic cues into the training process of such networks, leading to better generalization abilities given limited training data. We propose to induce sentiment embeddings via supervision on extrinsic data, which are then fed into the model via a dedicated memory-based component. We observe significant gains in effectiveness on a range of different datasets in seven different languages. |
| Tasks | Sentiment Analysis, Transfer Learning, Word Embeddings |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/P18-1235/ |
| https://www.aclweb.org/anthology/P18-1235 | |
| PWC | https://paperswithcode.com/paper/a-helping-hand-transfer-learning-for-deep |
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A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery
| Title | A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery |
| Authors | Xiao Zhang, Lingxiao Wang, Yaodong Yu, Quanquan Gu |
| Abstract | We propose a primal-dual based framework for analyzing the global optimality of nonconvex low-rank matrix recovery. Our analysis are based on the restricted strongly convex and smooth conditions, which can be verified for a broad family of loss functions. In addition, our analytic framework can directly handle the widely-used incoherence constraints through the lens of duality. We illustrate the applicability of the proposed framework to matrix completion and one-bit matrix completion, and prove that all these problems have no spurious local minima. Our results not only improve the sample complexity required for characterizing the global optimality of matrix completion, but also resolve an open problem in Ge et al. (2017) regarding one-bit matrix completion. Numerical experiments show that primal-dual based algorithm can successfully recover the global optimum for various low-rank problems. |
| Tasks | Matrix Completion |
| Published | 2018-07-01 |
| URL | https://icml.cc/Conferences/2018/Schedule?showEvent=2096 |
| http://proceedings.mlr.press/v80/zhang18m/zhang18m.pdf | |
| PWC | https://paperswithcode.com/paper/a-primal-dual-analysis-of-global-optimality |
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Building a Finnish SOM-based ontology concept tagger and harvester
| Title | Building a Finnish SOM-based ontology concept tagger and harvester |
| Authors | Seppo Nyrkk{"o} |
| Abstract | |
| Tasks | Information Retrieval, Morphological Analysis, Word Sense Disambiguation |
| Published | 2018-01-01 |
| URL | https://www.aclweb.org/anthology/W18-0202/ |
| https://www.aclweb.org/anthology/W18-0202 | |
| PWC | https://paperswithcode.com/paper/building-a-finnish-som-based-ontology-concept |
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Joint Modeling of Structure Identification and Nuclearity Recognition in Macro Chinese Discourse Treebank
| Title | Joint Modeling of Structure Identification and Nuclearity Recognition in Macro Chinese Discourse Treebank |
| Authors | Xiaomin Chu, Feng Jiang, Yi Zhou, Guodong Zhou, Qiaoming Zhu |
| Abstract | Discourse parsing is a challenging task and plays a critical role in discourse analysis. This paper focus on the macro level discourse structure analysis, which has been less studied in the previous researches. We explore a macro discourse structure presentation schema to present the macro level discourse structure, and propose a corresponding corpus, named Macro Chinese Discourse Treebank. On these bases, we concentrate on two tasks of macro discourse structure analysis, including structure identification and nuclearity recognition. In order to reduce the error transmission between the associated tasks, we adopt a joint model of the two tasks, and an Integer Linear Programming approach is proposed to achieve global optimization with various kinds of constraints. |
| Tasks | Machine Translation, Question Answering, Text Summarization |
| Published | 2018-08-01 |
| URL | https://www.aclweb.org/anthology/C18-1045/ |
| https://www.aclweb.org/anthology/C18-1045 | |
| PWC | https://paperswithcode.com/paper/joint-modeling-of-structure-identification |
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Sound-aligned corpus of Udmurt dialectal texts
| Title | Sound-aligned corpus of Udmurt dialectal texts |
| Authors | Timofey Arkhangelskiy, Ekaterina Georgieva |
| Abstract | |
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| Published | 2018-01-01 |
| URL | https://www.aclweb.org/anthology/W18-0203/ |
| https://www.aclweb.org/anthology/W18-0203 | |
| PWC | https://paperswithcode.com/paper/sound-aligned-corpus-of-udmurt-dialectal |
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Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition
| Title | Data-Driven Pronunciation Modeling of Swiss German Dialectal Speech for Automatic Speech Recognition |
| Authors | Michael Stadtschnitzer, Christoph Schmidt |
| Abstract | |
| Tasks | Information Retrieval, Language Modelling, Robust Speech Recognition, Speech Recognition |
| Published | 2018-05-01 |
| URL | https://www.aclweb.org/anthology/L18-1498/ |
| https://www.aclweb.org/anthology/L18-1498 | |
| PWC | https://paperswithcode.com/paper/data-driven-pronunciation-modeling-of-swiss |
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Proceedings of the Society for Computation in Linguistics (SCiL) 2018
| Title | Proceedings of the Society for Computation in Linguistics (SCiL) 2018 |
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| Published | 2018-01-01 |
| URL | https://www.aclweb.org/anthology/W18-0300/ |
| https://www.aclweb.org/anthology/W18-0300 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-society-for-computation-in |
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Learning To Learn Around A Common Mean
| Title | Learning To Learn Around A Common Mean |
| Authors | Giulia Denevi, Carlo Ciliberto, Dimitris Stamos, Massimiliano Pontil |
| Abstract | The problem of learning-to-learn (LTL) or meta-learning is gaining increasing attention due to recent empirical evidence of its effectiveness in applications. The goal addressed in LTL is to select an algorithm that works well on tasks sampled from a meta-distribution. In this work, we consider the family of algorithms given by a variant of Ridge Regression, in which the regularizer is the square distance to an unknown mean vector. We show that, in this setting, the LTL problem can be reformulated as a Least Squares (LS) problem and we exploit a novel meta- algorithm to efficiently solve it. At each iteration the meta-algorithm processes only one dataset. Specifically, it firstly estimates the stochastic LS objective function, by splitting this dataset into two subsets used to train and test the inner algorithm, respectively. Secondly, it performs a stochastic gradient step with the estimated value. Under specific assumptions, we present a bound for the generalization error of our meta-algorithm, which suggests the right splitting parameter to choose. When the hyper-parameters of the problem are fixed, this bound is consistent as the number of tasks grows, even if the sample size is kept constant. Preliminary experiments confirm our theoretical findings, highlighting the advantage of our approach, with respect to independent task learning. |
| Tasks | Meta-Learning |
| Published | 2018-12-01 |
| URL | http://papers.nips.cc/paper/8220-learning-to-learn-around-a-common-mean |
| http://papers.nips.cc/paper/8220-learning-to-learn-around-a-common-mean.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-learn-around-a-common-mean |
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Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching
| Title | Learning to Fuse Proposals from Multiple Scanline Optimizations in Semi-Global Matching |
| Authors | Johannes L. Schonberger, Sudipta N. Sinha, Marc Pollefeys |
| Abstract | Semi-Global Matching (SGM) uses an aggregation scheme to combine costs from multiple 1D scanline optimizations that tends to hurt its accuracy in difficult scenarios. We propose replacing this aggregation scheme with a new learning-based method that fuses disparity proposals estimated using scanline optimization. Our proposed SGM-Forest algorithm solves this problem using per-pixel classification. SGM-Forest currently ranks 1st on the ETH3D stereo benchmark and is ranked competitively on the Middlebury 2014 and KITTI 2015 benchmarks. It consistently outperforms SGM in challenging settings and under difficult training protocols that demonstrate robust generalization, while adding only a small computational overhead to SGM. |
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| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Johannes_Schoenberger_Learning_to_Fuse_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Johannes_Schoenberger_Learning_to_Fuse_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-fuse-proposals-from-multiple |
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Cogent: A Generic Dialogue System Shell Based on a Collaborative Problem Solving Model
| Title | Cogent: A Generic Dialogue System Shell Based on a Collaborative Problem Solving Model |
| Authors | Lucian Galescu, Choh Man Teng, James Allen, Ian Perera |
| Abstract | The bulk of current research in dialogue systems is focused on fairly simple task models, primarily state-based. Progress on developing dialogue systems for more complex tasks has been limited by the lack generic toolkits to build from. In this paper we report on our development from the ground up of a new dialogue model based on collaborative problem solving. We implemented the model in a dialogue system shell (Cogent) that al-lows developers to plug in problem-solving agents to create dialogue systems in new domains. The Cogent shell has now been used by several independent teams of researchers to develop dialogue systems in different domains, with varied lexicons and interaction style, each with their own problem-solving back-end. We believe this to be the first practical demonstration of the feasibility of a CPS-based dialogue system shell. |
| Tasks | Dialogue Management, Dialogue State Tracking |
| Published | 2018-07-01 |
| URL | https://www.aclweb.org/anthology/W18-5048/ |
| https://www.aclweb.org/anthology/W18-5048 | |
| PWC | https://paperswithcode.com/paper/cogent-a-generic-dialogue-system-shell-based |
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HybridFusion: Real-Time Performance Capture Using a Single Depth Sensor and Sparse IMUs
| Title | HybridFusion: Real-Time Performance Capture Using a Single Depth Sensor and Sparse IMUs |
| Authors | Zerong Zheng, Tao Yu, Hao Li, Kaiwen Guo, Qionghai Dai, Lu Fang, Yebin Liu |
| Abstract | We propose a light-weight and highly robust real-time human performance capture method based on a single depth camera and sparse inertial measurement units (IMUs). The proposed method combines non-rigid surface tracking and volumetric surface fusion to simultaneously reconstruct challenging motions, detailed geometries and the inner human body shapes of a clothed subject. The proposed hybrid motion tracking algorithm and efficient per-frame sensor calibration technique enable non-rigid surface reconstruction for fast motions and challenging poses with severe occlusions. Significant fusion artifacts are reduced using a new confidence measurement for our adaptive TSDF-based fusion. The above contributions benefit each other in our real-time reconstruction system, which enable practical human performance capture that is real-time, robust, low-cost and easy to deploy. Our experiments show how extremely challenging performances and loop closure problems can be handled successfully. |
| Tasks | Calibration |
| Published | 2018-09-01 |
| URL | http://openaccess.thecvf.com/content_ECCV_2018/html/Zerong_Zheng_HybridFusion_Real-Time_Performance_ECCV_2018_paper.html |
| http://openaccess.thecvf.com/content_ECCV_2018/papers/Zerong_Zheng_HybridFusion_Real-Time_Performance_ECCV_2018_paper.pdf | |
| PWC | https://paperswithcode.com/paper/hybridfusion-real-time-performance-capture |
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