Paper Group NANR 131
Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of Basque. Automatic rubric-based content grading for clinical notes. Transferable AutoML by Model Sharing Over Grouped Datasets. Discriminative Feature Transformation for Occluded Pedestrian Detection. Proceedings of the 5th Workshop on Noisy User-generated Text (W-NU …
Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of Basque
| Title | Deep Cross-Lingual Coreference Resolution for Less-Resourced Languages: The Case of Basque |
| Authors | Gorka Urbizu, Ander Soraluze, Olatz Arregi |
| Abstract | In this paper, we present a cross-lingual neural coreference resolution system for a less-resourced language such as Basque. To begin with, we build the first neural coreference resolution system for Basque, training it with the relatively small EPEC-KORREF corpus (45,000 words). Next, a cross-lingual coreference resolution system is designed. With this approach, the system learns from a bigger English corpus, using cross-lingual embeddings, to perform the coreference resolution for Basque. The cross-lingual system obtains slightly better results (40.93 F1 CoNLL) than the monolingual system (39.12 F1 CoNLL), without using any Basque language corpus to train it. |
| Tasks | Coreference Resolution |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/W19-2806/ |
| https://www.aclweb.org/anthology/W19-2806 | |
| PWC | https://paperswithcode.com/paper/deep-cross-lingual-coreference-resolution-for |
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Automatic rubric-based content grading for clinical notes
| Title | Automatic rubric-based content grading for clinical notes |
| Authors | Wen-wai Yim, Ashley Mills, Harold Chun, Teresa Hashiguchi, Justin Yew, Bryan Lu |
| Abstract | Clinical notes provide important documentation critical to medical care, as well as billing and legal needs. Too little information degrades quality of care; too much information impedes care. Training for clinical note documentation is highly variable, depending on institutions and programs. In this work, we introduce the problem of automatic evaluation of note creation through rubric-based content grading, which has the potential for accelerating and regularizing clinical note documentation training. To this end, we describe our corpus creation methods as well as provide simple feature-based and neural network baseline systems. We further provide tagset and scaling experiments to inform readers of plausible expected performances. Our baselines show promising results with content point accuracy and kappa values at 0.86 and 0.71 on the test set. |
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| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-6216/ |
| https://www.aclweb.org/anthology/D19-6216 | |
| PWC | https://paperswithcode.com/paper/automatic-rubric-based-content-grading-for |
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Transferable AutoML by Model Sharing Over Grouped Datasets
| Title | Transferable AutoML by Model Sharing Over Grouped Datasets |
| Authors | Chao Xue, Junchi Yan, Rong Yan, Stephen M. Chu, Yonggang Hu, Yonghua Lin |
| Abstract | Automated Machine Learning (AutoML) is an active area on the design of deep neural networks for specific tasks and datasets. Given the complexity of discovering new network designs, methods for speeding up the search procedure are becoming important. This paper presents a so-called transferable AutoML approach that Automated Machine Learning (AutoML) is an active area on the design of deep neural networks for specific tasks and datasets. Given the complexity of discovering new network designs, methods for speeding up the search procedure are becoming important. This paper presents a so-called transferable AutoML approach that leverages previously trained models to speed up the search process for new tasks and datasets. Our approach involves a novel meta-feature extraction technique based on the performance of benchmark models, and a dynamic dataset clustering algorithm based on Markov process and statistical hypothesis test. As such multiple models can share a common structure while with different learned parameters. The transferable AutoML can either be applied to search from scratch, search from predesigned models, or transfer from basic cells according to the difficulties of the given datasets. The experimental results on image classification show notable speedup in overall search time for multiple datasets with negligible loss in accuracy. |
| Tasks | AutoML, Image Classification |
| Published | 2019-06-01 |
| URL | http://openaccess.thecvf.com/content_CVPR_2019/html/Xue_Transferable_AutoML_by_Model_Sharing_Over_Grouped_Datasets_CVPR_2019_paper.html |
| http://openaccess.thecvf.com/content_CVPR_2019/papers/Xue_Transferable_AutoML_by_Model_Sharing_Over_Grouped_Datasets_CVPR_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/transferable-automl-by-model-sharing-over |
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Discriminative Feature Transformation for Occluded Pedestrian Detection
| Title | Discriminative Feature Transformation for Occluded Pedestrian Detection |
| Authors | Chunluan Zhou, Ming Yang, Junsong Yuan |
| Abstract | Despite promising performance achieved by deep con- volutional neural networks for non-occluded pedestrian de- tection, it remains a great challenge to detect partially oc- cluded pedestrians. Compared with non-occluded pedes- trian examples, it is generally more difficult to distinguish occluded pedestrian examples from background in featue space due to the missing of occluded parts. In this paper, we propose a discriminative feature transformation which en- forces feature separability of pedestrian and non-pedestrian examples to handle occlusions for pedestrian detection. Specifically, in feature space it makes pedestrian exam- ples approach the centroid of easily classified non-occluded pedestrian examples and pushes non-pedestrian examples close to the centroid of easily classified non-pedestrian ex- amples. Such a feature transformation partially compen- sates the missing contribution of occluded parts in feature space, therefore improving the performance for occluded pedestrian detection. We implement our approach in the Fast R-CNN framework by adding one transformation net- work branch. We validate the proposed approach on two widely used pedestrian detection datasets: Caltech and CityPersons. Experimental results show that our approach achieves promising performance for both non-occluded and occluded pedestrian detection. |
| Tasks | Pedestrian Detection |
| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhou_Discriminative_Feature_Transformation_for_Occluded_Pedestrian_Detection_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhou_Discriminative_Feature_Transformation_for_Occluded_Pedestrian_Detection_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/discriminative-feature-transformation-for |
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Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019)
| Title | Proceedings of the 5th Workshop on Noisy User-generated Text (W-NUT 2019) |
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| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-5500/ |
| https://www.aclweb.org/anthology/D19-5500 | |
| PWC | https://paperswithcode.com/paper/proceedings-of-the-5th-workshop-on-noisy-user |
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An Information-Theoretic Metric of Transferability for Task Transfer Learning
| Title | An Information-Theoretic Metric of Transferability for Task Transfer Learning |
| Authors | Yajie Bao, Yang Li, Shao-Lun Huang, Lin Zhang, Amir R. Zamir, Leonidas J. Guibas |
| Abstract | An important question in task transfer learning is to determine task transferability, i.e. given a common input domain, estimating to what extent representations learned from a source task can help in learning a target task. Typically, transferability is either measured experimentally or inferred through task relatedness, which is often defined without a clear operational meaning. In this paper, we present a novel metric, H-score, an easily-computable evaluation function that estimates the performance of transferred representations from one task to another in classification problems. Inspired by a principled information theoretic approach, H-score has a direct connection to the asymptotic error probability of the decision function based on the transferred feature. This formulation of transferability can further be used to select a suitable set of source tasks in task transfer learning problems or to devise efficient transfer learning policies. Experiments using both synthetic and real image data show that not only our formulation of transferability is meaningful in practice, but also it can generalize to inference problems beyond classification, such as recognition tasks for 3D indoor-scene understanding. |
| Tasks | Scene Understanding, Transfer Learning |
| Published | 2019-05-01 |
| URL | https://openreview.net/forum?id=BkxAUjRqY7 |
| https://openreview.net/pdf?id=BkxAUjRqY7 | |
| PWC | https://paperswithcode.com/paper/an-information-theoretic-metric-of |
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FW-GAN: Flow-Navigated Warping GAN for Video Virtual Try-On
| Title | FW-GAN: Flow-Navigated Warping GAN for Video Virtual Try-On |
| Authors | Haoye Dong, Xiaodan Liang, Xiaohui Shen, Bowen Wu, Bing-Cheng Chen, Jian Yin |
| Abstract | Beyond current image-based virtual try-on systems that have attracted increasing attention, we move a step forward to developing a video virtual try-on system that precisely transfers clothes onto the person and generates visually realistic videos conditioned on arbitrary poses. Besides the challenges in image-based virtual try-on (e.g., clothes fidelity, image synthesis), video virtual try-on further requires spatiotemporal consistency. Directly adopting existing image-based approaches often fails to generate coherent video with natural and realistic textures. In this work, we propose Flow-navigated Warping Generative Adversarial Network (FW-GAN), a novel framework that learns to synthesize the video of virtual try-on based on a person image, the desired clothes image, and a series of target poses. FW-GAN aims to synthesize the coherent and natural video while manipulating the pose and clothes. It consists of: (i) a flow-guided fusion module that warps the past frames to assist synthesis, which is also adopted in the discriminator to help enhance the coherence and quality of the synthesized video; (ii) a warping net that is designed to warp clothes image for the refinement of clothes textures; (iii) a parsing constraint loss that alleviates the problem caused by the misalignment of segmentation maps from images with different poses and various clothes. Experiments on our newly collected dataset show that FW-GAN can synthesize high-quality video of virtual try-on and significantly outperforms other methods both qualitatively and quantitatively. |
| Tasks | Image Generation |
| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Dong_FW-GAN_Flow-Navigated_Warping_GAN_for_Video_Virtual_Try-On_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Dong_FW-GAN_Flow-Navigated_Warping_GAN_for_Video_Virtual_Try-On_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/fw-gan-flow-navigated-warping-gan-for-video |
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Validation of Facts Against Textual Sources
| Title | Validation of Facts Against Textual Sources |
| Authors | Vamsi Krishna Pendyala, Simran Sinha, Satya Prakash, Shriya Reddy, Anupam Jamatia |
| Abstract | In today{'}s digital world of information, a fact verification system to disprove assertions made in speech, print media or online content is the need of the hour. We propose a system which would verify a claim against a source and classify the claim to be true, false, out-of-context or an inappropriate claim with respect to the textual source provided to the system. A true label is used if the claim is true, false if it is false, if the claim has no relation with the source then it is classified as out-of-context and if the claim cannot be verified at all then it is classified as inappropriate. This would help us to verify a claim or a fact as well as know about the source or our knowledge base against which we are trying to verify our facts. We used a two-step approach to achieve our goal. At first, we retrieved evidence related to the claims from the textual source using the Term Frequency-Inverse Document Frequency(TF-IDF) vectors. Later we classified the claim-evidence pairs as true, false, inappropriate and out of context using a modified version of textual entailment module. Textual entailment module calculates the probability of each sentence supporting the claim, contradicting the claim or not providing any relevant information using Bi-LSTM network to assess the veracity of the claim. The accuracy of the best performing system is 64.49{%} |
| Tasks | Natural Language Inference |
| Published | 2019-09-01 |
| URL | https://www.aclweb.org/anthology/R19-1104/ |
| https://www.aclweb.org/anthology/R19-1104 | |
| PWC | https://paperswithcode.com/paper/validation-of-facts-against-textual-sources |
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Top-Down Neural Model For Formulae
| Title | Top-Down Neural Model For Formulae |
| Authors | Karel Chvalovsky |
| Abstract | We present a simple neural model that given a formula and a property tries to answer the question whether the formula has the given property, for example whether a propositional formula is always true. A structure of formula is captured by a feedforward neural network build recursively for the given formula in a top-down manner. The results of this network are then processed by two recurrent neural networks. One of the interesting aspects of our model is how propositional atoms are treated. For example, the model is insensitive to their names, it only matters whether they are the same or distinct. |
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| Published | 2019-05-01 |
| URL | https://openreview.net/forum?id=Byg5QhR5FQ |
| https://openreview.net/pdf?id=Byg5QhR5FQ | |
| PWC | https://paperswithcode.com/paper/top-down-neural-model-for-formulae |
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What does the language of foods say about us?
| Title | What does the language of foods say about us? |
| Authors | Hoang Van, Ahmad Musa, Hang Chen, Stephen Kobourov, Mihai Surdeanu |
| Abstract | In this work we investigate the signal contained in the language of food on social media. We experiment with a dataset of 24 million food-related tweets, and make several observations. First,thelanguageoffoodhaspredictive power. We are able to predict if states in the United States (US) are above the medianratesfortype2diabetesmellitus(T2DM), income, poverty, and education {–} outperforming previous work by 4{–}18{%}. Second, we investigate the effect of socioeconomic factors (income, poverty, and education) on predicting state-level T2DM rates. Socioeconomic factors do improve T2DM prediction, with the greatestimprovementcomingfrompovertyinformation(6{%}),but,importantly,thelanguage of food adds distinct information that is not captured by socioeconomics. Third, we analyze how the language of food has changed over a five-year period (2013 {–} 2017), which is indicative of the shift in eating habits in the US during that period. We find several food trends, and that the language of food is used differently by different groups such as differentgenders. Last,weprovideanonlinevisualization tool for real-time queries and semantic analysis. |
| Tasks | |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-6212/ |
| https://www.aclweb.org/anthology/D19-6212 | |
| PWC | https://paperswithcode.com/paper/what-does-the-language-of-foods-say-about-us |
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Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation
| Title | Conditional Coupled Generative Adversarial Networks for Zero-Shot Domain Adaptation |
| Authors | Jinghua Wang, Jianmin Jiang |
| Abstract | Machine learning models trained in one domain perform poorly in the other domains due to the existence of domain shift. Domain adaptation techniques solve this problem by training transferable models from the label-rich source domain to the label-scarce target domain. Unfortunately, a majority of the existing domain adaptation techniques rely on the availability of the target-domain data, and thus limit their applications to a small community across few computer vision problems. In this paper, we tackle the challenging zero-shot domain adaptation (ZSDA) problem, where the target-domain data is non-available in the training stage. For this purpose, we propose conditional coupled generative adversarial networks (CoCoGAN) by extending the coupled generative adversarial networks (CoGAN) into a conditioning model. Compared with the existing state of the arts, our proposed CoCoGAN is able to capture the joint distribution of dual-domain samples in two different tasks, i.e. the relevant task (RT) and an irrelevant task (IRT). We train the CoCoGAN with both source-domain samples in RT and the dual-domain samples in IRT to complete the domain adaptation. While the former provide the high-level concepts of the non-available target-domain data, the latter carry the sharing correlation between the two domains in RT and IRT. To train the CoCoGAN in the absence of the target-domain data for RT, we propose a new supervisory signal, i.e. the alignment between representations across tasks. Extensive experiments carried out demonstrate that our proposed CoCoGAN outperforms existing state of the arts in image classifications. |
| Tasks | Domain Adaptation |
| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Wang_Conditional_Coupled_Generative_Adversarial_Networks_for_Zero-Shot_Domain_Adaptation_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Conditional_Coupled_Generative_Adversarial_Networks_for_Zero-Shot_Domain_Adaptation_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/conditional-coupled-generative-adversarial |
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Character-Based Models for Adversarial Phone Extraction: Preventing Human Sex Trafficking
| Title | Character-Based Models for Adversarial Phone Extraction: Preventing Human Sex Trafficking |
| Authors | Nathanael Chambers, Timothy Forman, Catherine Griswold, Kevin Lu, Yogaish Khastgir, Stephen Steckler |
| Abstract | Illicit activity on the Web often uses noisy text to obscure information between client and seller, such as the seller{'}s phone number. This presents an interesting challenge to language understanding systems; how do we model adversarial noise in a text extraction system? This paper addresses the sex trafficking domain, and proposes some of the first neural network architectures to learn and extract phone numbers from noisy text. We create a new adversarial advertisement dataset, propose several RNN-based models to solve the problem, and most notably propose a visual character language model to interpret unseen unicode characters. We train a CRF jointly with a CNN to improve number recognition by 89{%} over just a CRF. Through data augmentation in this unique model, we present the first results on characters never seen in training. |
| Tasks | Data Augmentation, Language Modelling |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-5507/ |
| https://www.aclweb.org/anthology/D19-5507 | |
| PWC | https://paperswithcode.com/paper/character-based-models-for-adversarial-phone |
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Tkol, Httt, and r/radiohead: High Affinity Terms in Reddit Communities
| Title | Tkol, Httt, and r/radiohead: High Affinity Terms in Reddit Communities |
| Authors | Bh, Abhinav ari, Caitrin Armstrong |
| Abstract | Language is an important marker of a cultural group, large or small. One aspect of language variation between communities is the employment of highly specialized terms with unique significance to the group. We study these high affinity terms across a wide variety of communities by leveraging the rich diversity of Reddit.com. We provide a systematic exploration of high affinity terms, the often rapid semantic shifts they undergo, and their relationship to subreddit characteristics across 2600 diverse subreddits. Our results show that high affinity terms are effective signals of loyal communities, they undergo more semantic shift than low affinity terms, and that they are partial barrier to entry for new users. We conclude that Reddit is a robust and valuable data source for testing further theories about high affinity terms across communities. |
| Tasks | |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-5508/ |
| https://www.aclweb.org/anthology/D19-5508 | |
| PWC | https://paperswithcode.com/paper/tkol-httt-and-rradiohead-high-affinity-terms |
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Early Rumour Detection
| Title | Early Rumour Detection |
| Authors | Kaimin Zhou, Chang Shu, Binyang Li, Jey Han Lau |
| Abstract | Rumours can spread quickly through social media, and malicious ones can bring about significant economical and social impact. Motivated by this, our paper focuses on the task of rumour detection; particularly, we are interested in understanding how early we can detect them. Although there are numerous studies on rumour detection, few are concerned with the timing of the detection. A successfully-detected malicious rumour can still cause significant damage if it isn{'}t detected in a timely manner, and so timing is crucial. To address this, we present a novel methodology for early rumour detection. Our model treats social media posts (e.g. tweets) as a data stream and integrates reinforcement learning to learn the number minimum number of posts required before we classify an event as a rumour. Experiments on Twitter and Weibo demonstrate that our model identifies rumours earlier than state-of-the-art systems while maintaining a comparable accuracy. |
| Tasks | Rumour Detection |
| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/N19-1163/ |
| https://www.aclweb.org/anthology/N19-1163 | |
| PWC | https://paperswithcode.com/paper/early-rumour-detection |
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Recursive Context-Aware Lexical Simplification
| Title | Recursive Context-Aware Lexical Simplification |
| Authors | Sian Gooding, Ekaterina Kochmar |
| Abstract | This paper presents a novel architecture for recursive context-aware lexical simplification, REC-LS, that is capable of (1) making use of the wider context when detecting the words in need of simplification and suggesting alternatives, and (2) taking previous simplification steps into account. We show that our system outputs lexical simplifications that are grammatically correct and semantically appropriate, and outperforms the current state-of-the-art systems in lexical simplification. |
| Tasks | Lexical Simplification |
| Published | 2019-11-01 |
| URL | https://www.aclweb.org/anthology/D19-1491/ |
| https://www.aclweb.org/anthology/D19-1491 | |
| PWC | https://paperswithcode.com/paper/recursive-context-aware-lexical |
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