Paper Group NANR 42
On Understanding the Relation between Expert Annotations of Text Readability and Target Reader Comprehension. TECHSSN at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks. Towards Disambiguating Contracts for their Successful Execution - A Case from Finance Domain. Towards Universal Semantic R …
On Understanding the Relation between Expert Annotations of Text Readability and Target Reader Comprehension
| Title | On Understanding the Relation between Expert Annotations of Text Readability and Target Reader Comprehension |
| Authors | Sowmya Vajjala, Ivana Lucic |
| Abstract | Automatic readability assessment aims to ensure that readers read texts that they can comprehend. However, computational models are typically trained on texts created from the perspective of the text writer, not the target reader. There is little experimental research on the relationship between expert annotations of readability, reader{'}s language proficiency, and different levels of reading comprehension. To address this gap, we conducted a user study in which over a 100 participants read texts of different reading levels and answered questions created to test three forms of comprehension. Our results indicate that more than readability annotation or reader proficiency, it is the type of comprehension question asked that shows differences between reader responses - inferential questions were difficult for users of all levels of proficiency across reading levels. The data collected from this study will be released with this paper, which will, for the first time, provide a collection of 45 reader bench marked texts to evaluate readability assessment systems developed for adult learners of English. It can also potentially be useful for the development of question generation approaches in intelligent tutoring systems research. |
| Tasks | Question Generation, Reading Comprehension |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-4437/ |
| https://www.aclweb.org/anthology/W19-4437 | |
| PWC | https://paperswithcode.com/paper/on-understanding-the-relation-between-expert |
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TECHSSN at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks
| Title | TECHSSN at SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Tweets using Deep Neural Networks |
| Authors | Angel Suseelan, Rajalakshmi S, Logesh B, Harshini S, Geetika B, Dyaneswaran S, S Milton Rajendram, Mirnalinee T T |
| Abstract | Task 6 of SemEval 2019 involves identifying and categorizing offensive language in social media. The systems developed by TECHSSN team uses multi-level classification techniques. We have developed two systems. In the first system, the first level of classification is done by a multi-branch 2D CNN classifier with Google{'}s pre-trained Word2Vec embedding and the second level of classification by string matching technique supported by offensive and bad words dictionary. The second system uses a multi-branch 1D CNN classifier with Glove pre-trained embedding layer for the first level of classification and string matching for the second level of classification. Input data with a probability of less than 0.70 in the first level are passed on to the second level. The misclassified examples are classified correctly in the second level. |
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| Published | 2019-06-01 |
| URL | https://www.aclweb.org/anthology/S19-2132/ |
| https://www.aclweb.org/anthology/S19-2132 | |
| PWC | https://paperswithcode.com/paper/techssn-at-semeval-2019-task-6-identifying |
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Towards Disambiguating Contracts for their Successful Execution - A Case from Finance Domain
| Title | Towards Disambiguating Contracts for their Successful Execution - A Case from Finance Domain |
| Authors | Preethu Rose Anish, Abhishek Sainani, Nitin Ramrakhiyani, Sachin Pawar, Girish K Palshikar, Smita Ghaisas |
| Abstract | |
| Tasks | |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-5502/ |
| https://www.aclweb.org/anthology/W19-5502 | |
| PWC | https://paperswithcode.com/paper/towards-disambiguating-contracts-for-their |
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Towards Universal Semantic Representation
| Title | Towards Universal Semantic Representation |
| Authors | Huaiyu Zhu, Yunyao Li, Laura Chiticariu |
| Abstract | Natural language understanding at the semantic level and independent of language variations is of great practical value. Existing approaches such as semantic role labeling (SRL) and abstract meaning representation (AMR) still have features related to the peculiarities of the particular language. In this work we describe various challenges and possible solutions in designing a semantic representation that is universal across a variety of languages. |
| Tasks | Semantic Role Labeling |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-3320/ |
| https://www.aclweb.org/anthology/W19-3320 | |
| PWC | https://paperswithcode.com/paper/towards-universal-semantic-representation |
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Corpora of social media in minority Uralic languages
| Title | Corpora of social media in minority Uralic languages |
| Authors | Timofey Arkhangelskiy |
| Abstract | |
| Tasks | Language Identification |
| Published | 2019-01-01 |
| URL | https://www.aclweb.org/anthology/W19-0311/ |
| https://www.aclweb.org/anthology/W19-0311 | |
| PWC | https://paperswithcode.com/paper/corpora-of-social-media-in-minority-uralic |
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A Dependency Structure Annotation for Modality
| Title | A Dependency Structure Annotation for Modality |
| Authors | Meagan Vigus, Jens E. L. Van Gysel, William Croft |
| Abstract | This paper presents an annotation scheme for modality that employs a dependency structure. Events and sources (here, conceivers) are represented as nodes and epistemic strength relations characterize the edges. The epistemic strength values are largely based on Saur{'\i} and Pustejovsky{'}s (2009) FactBank, while the dependency structure mirrors Zhang and Xue{'}s (2018b) approach to temporal relations. Six documents containing 377 events have been annotated by two expert annotators with high levels of agreement. |
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| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-3321/ |
| https://www.aclweb.org/anthology/W19-3321 | |
| PWC | https://paperswithcode.com/paper/a-dependency-structure-annotation-for |
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Deep Single-Image Portrait Relighting
| Title | Deep Single-Image Portrait Relighting |
| Authors | Hao Zhou, Sunil Hadap, Kalyan Sunkavalli, David W. Jacobs |
| Abstract | Conventional physically-based methods for relighting portrait images need to solve an inverse rendering problem, estimating face geometry, reflectance and lighting. However, the inaccurate estimation of face components can cause strong artifacts in relighting, leading to unsatisfactory results. In this work, we apply a physically-based portrait relighting method to generate a large scale, high quality, “in the wild” portrait relighting dataset (DPR). A deep Convolutional Neural Network (CNN) is then trained using this dataset to generate a relit portrait image by using a source image and a target lighting as input. The training procedure regularizes the generated results, removing the artifacts caused by physically-based relighting methods. A GAN loss is further applied to improve the quality of the relit portrait image. Our trained network can relight portrait images with resolutions as high as 1024 x 1024. We evaluate the proposed method on the proposed DPR datset, Flickr portrait dataset and Multi-PIE dataset both qualitatively and quantitatively. Our experiments demonstrate that the proposed method achieves state-of-the-art results. Please refer to https://zhhoper.github.io/dpr.html for dataset and code. |
| Tasks | |
| Published | 2019-10-01 |
| URL | http://openaccess.thecvf.com/content_ICCV_2019/html/Zhou_Deep_Single-Image_Portrait_Relighting_ICCV_2019_paper.html |
| http://openaccess.thecvf.com/content_ICCV_2019/papers/Zhou_Deep_Single-Image_Portrait_Relighting_ICCV_2019_paper.pdf | |
| PWC | https://paperswithcode.com/paper/deep-single-image-portrait-relighting |
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EoANN: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks
| Title | EoANN: Lexical Semantic Relation Classification Using an Ensemble of Artificial Neural Networks |
| Authors | Rayehe Hosseini Pour, Mehrnoush Shamsfard |
| Abstract | Researchers use wordnets as a knowledge base in many natural language processing tasks and applications, such as question answering, textual entailment, discourse classification, and so forth. Lexico-semantic relations among words or concepts are important parts of knowledge encoded in wordnets. As the use of wordnets becomes extensively widespread, extending the existing ones gets more attention. Manually construction and extension of lexico-semantic relations for WordNets or knowledge graphs are very time-consuming. Using automatic relation extraction methods can speed up this process. In this study, we exploit an ensemble of lstm and convolutional neural networks in a supervised manner to capture lexico-semantic relations which can either be used directly in NLP applications or compose the edges of wordnets. The whole procedure of learning vector space representation of relations is language independent. We used Princeton WordNet 3.1, FarsNet 3.0 (the Persian wordnet), Root09 and EVALution as golden standards to evaluate the predictive performance of our model and the results are comparable on the two languages. Empirical results demonstrate that our model outperforms the state of the art models. |
| Tasks | Knowledge Graphs, Natural Language Inference, Question Answering, Relation Classification, Relation Extraction |
| Published | 2019-09-01 |
| URL | https://www.aclweb.org/anthology/R19-1057/ |
| https://www.aclweb.org/anthology/R19-1057 | |
| PWC | https://paperswithcode.com/paper/eoann-lexical-semantic-relation |
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Automated Essay Scoring with Discourse-Aware Neural Models
| Title | Automated Essay Scoring with Discourse-Aware Neural Models |
| Authors | Farah Nadeem, Huy Nguyen, Yang Liu, Mari Ostendorf |
| Abstract | Automated essay scoring systems typically rely on hand-crafted features to predict essay quality, but such systems are limited by the cost of feature engineering. Neural networks offer an alternative to feature engineering, but they typically require more annotated data. This paper explores network structures, contextualized embeddings and pre-training strategies aimed at capturing discourse characteristics of essays. Experiments on three essay scoring tasks show benefits from all three strategies in different combinations, with simpler architectures being more effective when less training data is available. |
| Tasks | Feature Engineering |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-4450/ |
| https://www.aclweb.org/anthology/W19-4450 | |
| PWC | https://paperswithcode.com/paper/automated-essay-scoring-with-discourse-aware |
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a high efficiency fully convolutional networks for pixel wise surface defect detection
| Title | a high efficiency fully convolutional networks for pixel wise surface defect detection |
| Authors | LINGTENG QIU, XIAOJUN WU, ZHIYANG YU |
| Abstract | In this paper, we propose a highly efficient deep learning-based method for pixel-wise surface defect segmentation algorithm in machine vision. Our method is composed of a segmentation stage (stage 1), a detection stage (stage 2), and a matting stage (stage 3). In the segmentation stage, a lightweight fully convolutional network (FCN) is employed to make a pixel-wise prediction of the defect areas. Those predicted defect areas act as the initialization of stage 2, guiding the process of detection to correct the improper segmentation. In the matting stage, a guided filter is utilized to refine the contour of the defect area to reflect the real abnormal region. Besides that, aiming to achieve the tradeoff between efficiency and accuracy, and simultaneously we use depthwise & pointwise convolution layer, strided depthwise convolution layer, and upsample depthwise convolution layer to replace the standard convolution layer, pooling layer, and deconvolution layer, respectively. We validate our findings by analyzing the performance obtained on the dataset of DAGM 2007 |
| Tasks | |
| Published | 2019-01-23 |
| URL | https://ieeexplore.ieee.org/document/8624360 |
| https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8624360 | |
| PWC | https://paperswithcode.com/paper/a-high-efficiency-fully-convolutional |
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AnonyMate: A Toolkit for Anonymizing Unstructured Chat Data
| Title | AnonyMate: A Toolkit for Anonymizing Unstructured Chat Data |
| Authors | Allison Adams, Eric Aili, Daniel Aioanei, Rebecca Jonsson, Lina Mickelsson, Dagmar Mikmekova, Fred Roberts, Javier Fern Valencia, ez, Roger Wechsler |
| Abstract | |
| Tasks | |
| Published | 2019-09-01 |
| URL | https://www.aclweb.org/anthology/W19-6501/ |
| https://www.aclweb.org/anthology/W19-6501 | |
| PWC | https://paperswithcode.com/paper/anonymate-a-toolkit-for-anonymizing |
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RSN: Randomized Subspace Newton
| Title | RSN: Randomized Subspace Newton |
| Authors | Robert Gower, Dmitry Koralev, Felix Lieder, Peter Richtarik |
| Abstract | We develop a randomized Newton method capable of solving learning problems with huge dimensional feature spaces, which is a common setting in applications such as medical imaging, genomics and seismology. Our method leverages randomized sketching in a new way, by finding the Newton direction constrained to the space spanned by a random sketch. We develop a simple global linear convergence theory that holds for practically all sketching techniques, which gives the practitioners the freedom to design custom sketching approaches suitable for particular applications. We perform numerical experiments which demonstrate the efficiency of our method as compared to accelerated gradient descent and the full Newton method. Our method can be seen as a refinement and a randomized extension of the results of Karimireddy, Stich, and Jaggi (2019). |
| Tasks | |
| Published | 2019-12-01 |
| URL | http://papers.nips.cc/paper/8351-rsn-randomized-subspace-newton |
| http://papers.nips.cc/paper/8351-rsn-randomized-subspace-newton.pdf | |
| PWC | https://paperswithcode.com/paper/rsn-randomized-subspace-newton |
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BERT Masked Language Modeling for Co-reference Resolution
| Title | BERT Masked Language Modeling for Co-reference Resolution |
| Authors | Felipe Alfaro, Marta R. Costa-juss{`a}, Jos{'e} A. R. Fonollosa |
| Abstract | This paper explains the TALP-UPC participation for the Gendered Pronoun Resolution shared-task of the 1st ACL Workshop on Gender Bias for Natural Language Processing. We have implemented two models for mask language modeling using pre-trained BERT adjusted to work for a classification problem. The proposed solutions are based on the word probabilities of the original BERT model, but using common English names to replace the original test names. |
| Tasks | Language Modelling |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-3811/ |
| https://www.aclweb.org/anthology/W19-3811 | |
| PWC | https://paperswithcode.com/paper/bert-masked-language-modeling-for-co |
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AddGraph_ Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN
| Title | AddGraph_ Anomaly Detection in Dynamic Graph Using Attention-based Temporal GCN |
| Authors | Li Zheng, Zhenpeng Li, Jian Li, Zhao Li， and Jun Gao |
| Abstract | Anomaly detection in dynamic graphs becomes very critical in many different application scenarios, e.g., recommender systems, while it also raises huge challenges due to the high flexible nature of anomaly and lack of sufficient labelled data. It is better to learn the anomaly patterns by considering all possible hints including the structural, content and temporal features, rather than utilizing heuristic rules over the partial features. In this paper, we propose AddGraph, a general end-to-end anomalous edge detection framework using an extended temporal GCN (Graph Convolutional Network) with an attention model, which can capture both long-term patterns and the short-term patterns in dynamic graphs. In order to cope with insuffi- cient explicit labelled data, we employ a selective negative sampling and margin loss in training of AddGraph in a semi-supervised fashion. We conduct extensive experiments on real-world datasets, and illustrate that AddGraph can outperform the state-of-the-art competitors in anomaly detection significantly |
| Tasks | Anomaly Detection, Edge Detection, Recommendation Systems |
| Published | 2019-06-24 |
| URL | https://www.researchgate.net/publication/334843751_AddGraph_Anomaly_Detection_in_Dynamic_Graph_Using_Attention-based_Temporal_GCN |
| https://www.researchgate.net/publication/334843751_AddGraph_Anomaly_Detection_in_Dynamic_Graph_Using_Attention-based_Temporal_GCN | |
| PWC | https://paperswithcode.com/paper/addgraph_-anomaly-detection-in-dynamic-graph |
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Using Functional Schemas to Understand Social Media Narratives
| Title | Using Functional Schemas to Understand Social Media Narratives |
| Authors | Xinru Yan, Aakanksha Naik, Yohan Jo, Carolyn Rose |
| Abstract | We propose a novel take on understanding narratives in social media, focusing on learning {''}functional story schemas{''}, which consist of sets of stereotypical functional structures. We develop an unsupervised pipeline to extract schemas and apply our method to Reddit posts to detect schematic structures that are characteristic of different subreddits. We validate our schemas through human interpretation and evaluate their utility via a text classification task. Our experiments show that extracted schemas capture distinctive structural patterns in different subreddits, improving classification performance of several models by 2.4{%} on average. We also observe that these schemas serve as lenses that reveal community norms. |
| Tasks | Text Classification |
| Published | 2019-08-01 |
| URL | https://www.aclweb.org/anthology/W19-3403/ |
| https://www.aclweb.org/anthology/W19-3403 | |
| PWC | https://paperswithcode.com/paper/using-functional-schemas-to-understand-social |
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