Paper Group NANR 165
Complaint Analysis and Classification for Economic and Food Safety. Efficient Parameter-Free Clustering Using First Neighbor Relations. Enhancing BERT for Lexical Normalization. Natural Language Generation: Recently Learned Lessons, Directions for Semantic Representation-based Approaches, and the Case of Brazilian Portuguese Language. SpiderBoost a …
Complaint Analysis and Classification for Economic and Food Safety
| Title | Complaint Analysis and Classification for Economic and Food Safety |
| Authors | Jo{~a}o Filgueiras, Lu{'\i}s Barbosa, Gil Rocha, Henrique Lopes Cardoso, Lu{'\i}s Paulo Reis, Jo{~a}o Pedro Machado, Ana Maria Oliveira |
| Abstract | Governmental institutions are employing artificial intelligence techniques to deal with their specific problems and exploit their huge amounts of both structured and unstructured information. In particular, natural language processing and machine learning techniques are being used to process citizen feedback. In this paper, we report on the use of such techniques for analyzing and classifying complaints, in the context of the Portuguese Economic and Food Safety Authority. Grounded in its operational process, we address three different classification problems: target economic activity, implied infraction severity level, and institutional competence. We show promising results obtained using feature-based approaches and traditional classifiers, with accuracy scores above 70{%}, and analyze the shortcomings of our current results and avenues for further improvement, taking into account the intended use of our classifiers in helping human officers to cope with thousands of yearly complaints. |
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| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-5107/ |
| https://www.aclweb.org/anthology/D19-5107 | |
| PWC | https://paperswithcode.com/paper/complaint-analysis-and-classification-for |
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Efficient Parameter-Free Clustering Using First Neighbor Relations
| Title | Efficient Parameter-Free Clustering Using First Neighbor Relations |
| Authors | Saquib Sarfraz, Vivek Sharma, Rainer Stiefelhagen |
| Abstract | We present a new clustering method in the form of a single clustering equation that is able to directly discover groupings in the data. The main proposition is that the first neighbor of each sample is all one needs to discover large chains and finding the groups in the data. In contrast to most existing clustering algorithms our method does not require any hyper-parameters, distance thresholds and/or the need to specify the number of clusters. The proposed algorithm belongs to the family of hierarchical agglomerative methods. The technique has a very low computational overhead, is easily scalable and applicable to large practical problems. Evaluation on well known datasets from different domains ranging between 1077 and 8.1 million samples shows substantial performance gains when compared to the existing clustering techniques. |
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| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Sarfraz_Efficient_Parameter-Free_Clustering_Using_First_Neighbor_Relations_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Sarfraz_Efficient_Parameter-Free_Clustering_Using_First_Neighbor_Relations_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-parameter-free-clustering-using-1 |
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Enhancing BERT for Lexical Normalization
| Title | Enhancing BERT for Lexical Normalization |
| Authors | Benjamin Muller, Benoit Sagot, Djam{'e} Seddah |
| Abstract | Language model-based pre-trained representations have become ubiquitous in natural language processing. They have been shown to significantly improve the performance of neural models on a great variety of tasks. However, it remains unclear how useful those general models can be in handling non-canonical text. In this article, focusing on User Generated Content (UGC), we study the ability of BERT to perform lexical normalisation. Our contribution is simple: by framing lexical normalisation as a token prediction task, by enhancing its architecture and by carefully fine-tuning it, we show that BERT can be a competitive lexical normalisation model without the need of any UGC resources aside from 3,000 training sentences. To the best of our knowledge, it is the first work done in adapting and analysing the ability of this model to handle noisy UGC data. |
| Tasks | Language Modelling, Lexical Normalization |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-5539/ |
| https://www.aclweb.org/anthology/D19-5539 | |
| PWC | https://paperswithcode.com/paper/enhancing-bert-for-lexical-normalization |
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Natural Language Generation: Recently Learned Lessons, Directions for Semantic Representation-based Approaches, and the Case of Brazilian Portuguese Language
| Title | Natural Language Generation: Recently Learned Lessons, Directions for Semantic Representation-based Approaches, and the Case of Brazilian Portuguese Language |
| Authors | Marco Antonio Sobrevilla Cabezudo, Thiago Pardo |
| Abstract | This paper presents a more recent literature review on Natural Language Generation. In particular, we highlight the efforts for Brazilian Portuguese in order to show the available resources and the existent approaches for this language. We also focus on the approaches for generation from semantic representations (emphasizing the Abstract Meaning Representation formalism) as well as their advantages and limitations, including possible future directions. |
| Tasks | Text Generation |
| Published | 2019-07-01 |
| URL | https://www.aclweb.org/anthology/P19-2011/ |
| https://www.aclweb.org/anthology/P19-2011 | |
| PWC | https://paperswithcode.com/paper/natural-language-generation-recently-learned |
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SpiderBoost and Momentum: Faster Variance Reduction Algorithms
| Title | SpiderBoost and Momentum: Faster Variance Reduction Algorithms |
| Authors | Zhe Wang, Kaiyi Ji, Yi Zhou, Yingbin Liang, Vahid Tarokh |
| Abstract | SARAH and SPIDER are two recently developed stochastic variance-reduced algorithms, and SPIDER has been shown to achieve a near-optimal first-order oracle complexity in smooth nonconvex optimization. However, SPIDER uses an accuracy-dependent stepsize that slows down the convergence in practice, and cannot handle objective functions that involve nonsmooth regularizers. In this paper, we propose SpiderBoost as an improved scheme, which allows to use a much larger constant-level stepsize while maintaining the same near-optimal oracle complexity, and can be extended with proximal mapping to handle composite optimization (which is nonsmooth and nonconvex) with provable convergence guarantee. In particular, we show that proximal SpiderBoost achieves an oracle complexity of O(min{n^{1/2}\epsilon^{-2},\epsilon^{-3}}) in composite nonconvex optimization, improving the state-of-the-art result by a factor of O(min{n^{1/6},\epsilon^{-1/3}}). We further develop a novel momentum scheme to accelerate SpiderBoost for composite optimization, which achieves the near-optimal oracle complexity in theory and substantial improvement in experiments. |
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| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/8511-spiderboost-and-momentum-faster-variance-reduction-algorithms |
| http://papers.nips.cc/paper/8511-spiderboost-and-momentum-faster-variance-reduction-algorithms.pdf | |
| PWC | https://paperswithcode.com/paper/spiderboost-and-momentum-faster-variance |
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Dyr Bul Shchyl. Proxying Sound Symbolism With Word Embeddings
| Title | Dyr Bul Shchyl. Proxying Sound Symbolism With Word Embeddings |
| Authors | Ivan Yamshchikov, Viascheslav Shibaev, Alexey Tikhonov |
| Abstract | This paper explores modern word embeddings in the context of sound symbolism. Using basic properties of the representations space one can construct semantic axes. A method is proposed to measure if the presence of individual sounds in a given word shifts its semantics of that word along a specific axis. It is shown that, in accordance with several experimental and statistical results, word embeddings capture symbolism for certain sounds. |
| Tasks | Word Embeddings |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/W19-2012/ |
| https://www.aclweb.org/anthology/W19-2012 | |
| PWC | https://paperswithcode.com/paper/dyr-bul-shchyl-proxying-sound-symbolism-with |
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Deep Multi-Model Fusion for Single-Image Dehazing
| Title | Deep Multi-Model Fusion for Single-Image Dehazing |
| Authors | Zijun Deng, Lei Zhu, Xiaowei Hu, Chi-Wing Fu, Xuemiao Xu, Qing Zhang, Jing Qin, Pheng-Ann Heng |
| Abstract | This paper presents a deep multi-model fusion network to attentively integrate multiple models to separate layers and boost the performance in single-image dehazing. To do so, we first formulate the attentional feature integration module to maximize the integration of the convolutional neural network (CNN) features at different CNN layers and generate the attentional multi-level integrated features (AMLIF). Then, from the AMLIF, we further predict a haze-free result for an atmospheric scattering model, as well as for four haze-layer separation models, and then fuse the results together to produce the final haze-free image. To evaluate the effectiveness of our method, we compare our network with several state-of-the-art methods on two widely-used dehazing benchmark datasets, as well as on two sets of real-world hazy images. Experimental results demonstrate clear quantitative and qualitative improvements of our method over the state-of-the-arts. |
| Tasks | Image Dehazing, Single Image Dehazing |
| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Deng_Deep_Multi-Model_Fusion_for_Single-Image_Dehazing_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Deng_Deep_Multi-Model_Fusion_for_Single-Image_Dehazing_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/deep-multi-model-fusion-for-single-image |
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Surrogate Objectives for Batch Policy Optimization in One-step Decision Making
| Title | Surrogate Objectives for Batch Policy Optimization in One-step Decision Making |
| Authors | Minmin Chen, Ramki Gummadi, Chris Harris, Dale Schuurmans |
| Abstract | We investigate batch policy optimization for cost-sensitive classification and contextual bandits—two related tasks that obviate exploration but require generalizing from observed rewards to action selections in unseen contexts. When rewards are fully observed, we show that the expected reward objective exhibits suboptimal plateaus and exponentially many local optima in the worst case. To overcome the poor landscape, we develop a convex surrogate that is calibrated with respect to entropy regularized expected reward. We then consider the partially observed case, where rewards are recorded for only a subset of actions. Here we generalize the surrogate to partially observed data, and uncover novel objectives for batch contextual bandit training. We find that surrogate objectives remain provably sound in this setting and empirically demonstrate state-of-the-art performance. |
| Tasks | Decision Making, Multi-Armed Bandits |
| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/9086-surrogate-objectives-for-batch-policy-optimization-in-one-step-decision-making |
| http://papers.nips.cc/paper/9086-surrogate-objectives-for-batch-policy-optimization-in-one-step-decision-making.pdf | |
| PWC | https://paperswithcode.com/paper/surrogate-objectives-for-batch-policy |
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DSTC7 Task 1: Noetic End-to-End Response Selection
| Title | DSTC7 Task 1: Noetic End-to-End Response Selection |
| Authors | Chulaka Gunasekara, Jonathan K. Kummerfeld, Lazaros Polymenakos, Walter Lasecki |
| Abstract | Goal-oriented dialogue in complex domains is an extremely challenging problem and there are relatively few datasets. This task provided two new resources that presented different challenges: one was focused but small, while the other was large but diverse. We also considered several new variations on the next utterance selection problem: (1) increasing the number of candidates, (2) including paraphrases, and (3) not including a correct option in the candidate set. Twenty teams participated, developing a range of neural network models, including some that successfully incorporated external data to boost performance. Both datasets have been publicly released, enabling future work to build on these results, working towards robust goal-oriented dialogue systems. |
| Tasks | Conversational Response Selection, Goal-Oriented Dialogue Systems |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-4107/ |
| https://www.aclweb.org/anthology/W19-4107 | |
| PWC | https://paperswithcode.com/paper/dstc7-task-1-noetic-end-to-end-response |
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A Simple and Robust Approach to Detecting Subject-Verb Agreement Errors
| Title | A Simple and Robust Approach to Detecting Subject-Verb Agreement Errors |
| Authors | Simon Flachs, Oph{'e}lie Lacroix, Marek Rei, Helen Yannakoudakis, Anders S{\o}gaard |
| Abstract | While rule-based detection of subject-verb agreement (SVA) errors is sensitive to syntactic parsing errors and irregularities and exceptions to the main rules, neural sequential labelers have a tendency to overfit their training data. We observe that rule-based error generation is less sensitive to syntactic parsing errors and irregularities than error detection and explore a simple, yet efficient approach to getting the best of both worlds: We train neural sequential labelers on the combination of large volumes of silver standard data, obtained through rule-based error generation, and gold standard data. We show that our simple protocol leads to more robust detection of SVA errors on both in-domain and out-of-domain data, as well as in the context of other errors and long-distance dependencies; and across four standard benchmarks, the induced model on average achieves a new state of the art. |
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| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/N19-1251/ |
| https://www.aclweb.org/anthology/N19-1251 | |
| PWC | https://paperswithcode.com/paper/a-simple-and-robust-approach-to-detecting |
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Learning Representations for Time Series Clustering
| Title | Learning Representations for Time Series Clustering |
| Authors | Qianli Ma, Jiawei Zheng, Sen Li, Gary W. Cottrell |
| Abstract | Time series clustering is an essential unsupervised technique in cases when category information is not available. It has been widely applied to genome data, anomaly detection, and in general, in any domain where pattern detection is important. Although feature-based time series clustering methods are robust to noise and outliers, and can reduce the dimensionality of the data, they typically rely on domain knowledge to manually construct high-quality features. Sequence to sequence (seq2seq) models can learn representations from sequence data in an unsupervised manner by designing appropriate learning objectives, such as reconstruction and context prediction. When applying seq2seq to time series clustering, obtaining a representation that effectively represents the temporal dynamics of the sequence, multi-scale features, and good clustering properties remains a challenge. How to best improve the ability of the encoder is still an open question. Here we propose a novel unsupervised temporal representation learning model, named Deep Temporal Clustering Representation (DTCR), which integrates the temporal reconstruction and K-means objective into the seq2seq model. This approach leads to improved cluster structures and thus obtains cluster-specific temporal representations. Also, to enhance the ability of encoder, we propose a fake-sample generation strategy and auxiliary classification task. Experiments conducted on extensive time series datasets show that DTCR is state-of-the-art compared to existing methods. The visualization analysis not only shows the effectiveness of cluster-specific representation but also shows the learning process is robust, even if K-means makes mistakes. |
| Tasks | Anomaly Detection, Representation Learning, Time Series, Time Series Clustering |
| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/8634-learning-representations-for-time-series-clustering |
| http://papers.nips.cc/paper/8634-learning-representations-for-time-series-clustering.pdf | |
| PWC | https://paperswithcode.com/paper/learning-representations-for-time-series |
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Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System
| Title | Ordinal and Attribute Aware Response Generation in a Multimodal Dialogue System |
| Authors | Hardik Chauhan, Mauajama Firdaus, Asif Ekbal, Pushpak Bhattacharyya |
| Abstract | Multimodal dialogue systems have opened new frontiers in the traditional goal-oriented dialogue systems. The state-of-the-art dialogue systems are primarily based on unimodal sources, predominantly the text, and hence cannot capture the information present in the other sources such as videos, audios, images etc. With the availability of large scale multimodal dialogue dataset (MMD) (Saha et al., 2018) on the fashion domain, the visual appearance of the products is essential for understanding the intention of the user. Without capturing the information from both the text and image, the system will be incapable of generating correct and desirable responses. In this paper, we propose a novel position and attribute aware attention mechanism to learn enhanced image representation conditioned on the user utterance. Our evaluation shows that the proposed model can generate appropriate responses while preserving the position and attribute information. Experimental results also prove that our proposed approach attains superior performance compared to the baseline models, and outperforms the state-of-the-art approaches on text similarity based evaluation metrics. |
| Tasks | Goal-Oriented Dialogue Systems |
| Published | 2019-07-01 |
| URL | https://www.aclweb.org/anthology/P19-1540/ |
| https://www.aclweb.org/anthology/P19-1540 | |
| PWC | https://paperswithcode.com/paper/ordinal-and-attribute-aware-response |
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Cascaded Generative and Discriminative Learning for Microcalcification Detection in Breast Mammograms
| Title | Cascaded Generative and Discriminative Learning for Microcalcification Detection in Breast Mammograms |
| Authors | Fandong Zhang, Ling Luo, Xinwei Sun, Zhen Zhou, Xiuli Li, Yizhou Yu, Yizhou Wang |
| Abstract | Accurate microcalcification (mC) detection is of great importance due to its high proportion in early breast cancers. Most of the previous mC detection methods belong to discriminative models, where classifiers are exploited to distinguish mCs from other backgrounds. However, it is still challenging for these methods to tell the mCs from amounts of normal tissues because they are too tiny (at most 14 pixels). Generative methods can precisely model the normal tissues and regard the abnormal ones as outliers, while they fail to further distinguish the mCs from other anomalies, i.e. vessel calcifications. In this paper, we propose a hybrid approach by taking advantages of both generative and discriminative models. Firstly, a generative model named Anomaly Separation Network (ASN) is used to generate candidate mCs. ASN contains two major components. A deep convolutional encoder-decoder network is built to learn the image reconstruction mapping and a t-test loss function is designed to separate the distributions of the reconstruction residuals of mCs from normal tissues. Secondly, a discriminative model is cascaded to tell the mCs from the false positives. Finally, to verify the effectiveness of our method, we conduct experiments on both public and in-house datasets, which demonstrates that our approach outperforms previous state-of-the-art methods. |
| Tasks | Image Reconstruction |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Zhang_Cascaded_Generative_and_Discriminative_Learning_for_Microcalcification_Detection_in_Breast_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Zhang_Cascaded_Generative_and_Discriminative_Learning_for_Microcalcification_Detection_in_Breast_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/cascaded-generative-and-discriminative |
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Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese
| Title | Topic Tensor Network for Implicit Discourse Relation Recognition in Chinese |
| Authors | Sheng Xu, Peifeng Li, Fang Kong, Qiaoming Zhu, Guodong Zhou |
| Abstract | In the literature, most of the previous studies on English implicit discourse relation recognition only use sentence-level representations, which cannot provide enough semantic information in Chinese due to its unique paratactic characteristics. In this paper, we propose a topic tensor network to recognize Chinese implicit discourse relations with both sentence-level and topic-level representations. In particular, besides encoding arguments (discourse units) using a gated convolutional network to obtain sentence-level representations, we train a simplified topic model to infer the latent topic-level representations. Moreover, we feed the two pairs of representations to two factored tensor networks, respectively, to capture both the sentence-level interactions and topic-level relevance using multi-slice tensors. Experimentation on CDTB, a Chinese discourse corpus, shows that our proposed model significantly outperforms several state-of-the-art baselines in both micro and macro F1-scores. |
| Tasks | Tensor Networks |
| Published | 2019-07-01 |
| URL | https://www.aclweb.org/anthology/P19-1058/ |
| https://www.aclweb.org/anthology/P19-1058 | |
| PWC | https://paperswithcode.com/paper/topic-tensor-network-for-implicit-discourse |
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The Utility of Discourse Parsing Features for Predicting Argumentation Structure
| Title | The Utility of Discourse Parsing Features for Predicting Argumentation Structure |
| Authors | Freya Hewett, Roshan Prakash Rane, Nina Harlacher, Manfred Stede |
| Abstract | Research on argumentation mining from text has frequently discussed relationships to discourse parsing, but few empirical results are available so far. One corpus that has been annotated in parallel for argumentation structure and for discourse structure (RST, SDRT) are the {`}argumentative microtexts{'} (Peldszus and Stede, 2016a). While results on perusing the gold RST annotations for predicting argumentation have been published (Peldszus and Stede, 2016b), the step to automatic discourse parsing has not yet been taken. In this paper, we run various discourse parsers (RST, PDTB) on the corpus, compare their results to the gold annotations (for RST) and then assess the contribution of automatically-derived discourse features for argumentation parsing. After reproducing the state-of-the-art Evidence Graph model from Afantenos et al. (2018) for the microtexts, we find that PDTB features can indeed improve its performance. | |
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| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-4512/ |
| https://www.aclweb.org/anthology/W19-4512 | |
| PWC | https://paperswithcode.com/paper/the-utility-of-discourse-parsing-features-for |
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