Reviewerly/exHarmony huggingface dataset:
Controlling Text Complexity in Neural Machine Translation

This work introduces a machine translation task where the output is aimed at audiences of different levels of target language proficiency.We collect a high quality dataset of news articles available in English and Spanish, written for diverse grade levels and propose a method to align segments across comparable bilingual articles.The resulting dataset makes it possible to train multi-task sequence-to-sequence models that translate Spanish into English targeted at an easier reading grade level than the original Spanish.We show that these multitask models outperform pipeline approaches that translate and simplify text independently.
Auto-Encoding Variational Neural Machine Translation

We present a deep generative model of bilingual sentence pairs for machine translation.The model generates source and target sentences jointly from a shared latent representation and is parameterised by neural networks.We perform efficient training using amortised variational inference and reparameterised gradients.Additionally, we discuss the statistical implications of joint modelling and propose an efficient approximation to maximum a posteriori decoding for fast test-time predictions.We demonstrate the effectiveness of our model in three machine translation scenarios: indomain training, mixed-domain training, and learning from a mix of gold-standard and synthetic data.Our experiments show consistently that our joint formulation outperforms conditional modelling (i.e. standard neural machine translation) in all such scenarios.
Customizing Neural Machine Translation for Subtitling

In this work, we customized a neural machine translation system for translation of subtitles in the domain of entertainment.The neural translation model was adapted to the subtitling content and style and extended by a simple, yet effective technique for utilizing intersentence context for short sentences such as dialog turns.The main contribution of the paper is a novel subtitle segmentation algorithm that predicts the end of a subtitle line given the previous word-level context using a recurrent neural network learned from human segmentation decisions.This model is combined with subtitle length and duration constraints established in the subtitling industry.We conducted a thorough human evaluation with two post-editors (English-to-Spanish translation of a documentary and a sitcom).It showed a notable productivity increase of up to 37% as compared to translating from scratch and significant reductions in human translation edit rate in comparison with the post-editing of the baseline non-adapted system without a learned segmentation model.
The Feasibility of Embedding Based Automatic Evaluation for Single Document Summarization

ROUGE is widely used to automatically evaluate summarization systems.However, ROUGE measures semantic overlap between a system summary and a human reference on word-string level, much at odds with the contemporary treatment of semantic meaning.Here we present a suite of experiments on using distributed representations for evaluating summarizers, both in reference-based and in reference-free setting.Our experimental results show that the max value over each dimension of the summary ELMo word embeddings is a good representation that results in high correlation with human ratings.Averaging the cosine similarity of all encoders we tested yields high correlation with manual scores in reference-free setting.The distributed representations outperform ROUGE in recent corpora for abstractive news summarization but are less good on older test data and systems.
Learning to Propagate for Graph Meta-Learning

Meta-learning extracts the common knowledge from learning different tasks and uses it for unseen tasks. It can signiﬁcantly improve tasks that suffer from insufﬁcient training data, e.g., few-shot learning. In most meta-learning methods, tasks are implicitly related by sharing parameters or optimizer. In this paper, we show that a meta-learner that explicitly relates tasks on a graph describing the relations of their output dimensions (e.g., classes) can signiﬁcantly improve few-shot learning. The graph’s structure is usually free or cheap to obtain but has rarely been explored in previous works. We develop a novel meta-learner of this type for prototype based classiﬁcation, in which a prototype is generated for each class, such that the nearest neighbor search among the prototypes produces an accurate classiﬁcation. The meta-learner, called “Gated Propagation Network (GPN)”, learns to propagate messages between prototypes of different classes on the graph, so that learning the prototype of each class beneﬁts from the data of other related classes. In GPN, an attention mechanism aggregates messages from neighboring classes of each class, with a gate choosing between the aggregated message and the message from the class itself. We train GPN on a sequence of tasks from many-shot to few-shot generated by subgraph sampling. During training, it is able to reuse and update previously achieved prototypes from the memory in a life-long learning cycle. In experiments, under different training-test discrepancy and test task generation settings, GPN outperforms recent meta-learning methods on two benchmark datasets. Code of GPN is publicly available at: https://github.com/liulu112601/Gated-Propagation-Net.
WikiCREM: A Large Unsupervised Corpus for Coreference Resolution

Pronoun resolution is a major area of natural language understanding.However, large-scale training sets are still scarce, since manually labelling data is costly.In this work, we introduce WIKICREM (Wikipedia CoREferences Masked) a large-scale, yet accurate dataset of pronoun disambiguation instances.We use a language-model-based approach for pronoun resolution in combination with our WI-KICREM dataset.We compare a series of models on a collection of diverse and challenging coreference resolution problems, where we match or outperform previous state-of-theart approaches on 6 out of 7 datasets, such as GAP, DPR, WNLI, PDP, WINOBIAS, and WINOGENDER.We release our model to be used off-the-shelf for solving pronoun disambiguation.
Generalizing Back-Translation in Neural Machine Translation

Back-translation -data augmentation by translating target monolingual data -is a crucial component in modern neural machine translation (NMT).In this work, we reformulate back-translation in the scope of crossentropy optimization of an NMT model, clarifying its underlying mathematical assumptions and approximations beyond its heuristic usage.Our formulation covers broader synthetic data generation schemes, including sampling from a target-to-source NMT model.With this formulation, we point out fundamental problems of the sampling-based approaches and propose to remedy them by (i) disabling label smoothing for the target-to-source model and (ii) sampling from a restricted search space.Our statements are investigated on the WMT 2018 German ↔ English news translation task.
Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems

We study how to sample negative examples to automatically construct a training set for effective model learning in retrieval-based dialogue systems.Following an idea of dynamically adapting negative examples to matching models in learning, we consider four strategies including minimum sampling, maximum sampling, semi-hard sampling, and decay-hard sampling.Empirical studies on two benchmarks with three matching models indicate that compared with the widely used random sampling strategy, although the first two strategies lead to performance drop, the latter two ones can bring consistent improvement to the performance of all the models on both benchmarks.
The enemy’s gaze: Immersive virtual environments enhance peace promoting attitudes and emotions in violent intergroup conflicts

Perspective-taking is essential for improving intergroup relations. However, it is difficult to implement, especially in violent conflicts. Given that immersive virtual reality (VR) can simulate various points of view (POV), we examined whether it can lead to beneficial outcomes by promoting outgroup perspective-taking, even in armed conflicts. In two studies, Jewish-Israelis watched a 360° VR scene depicting an Israeli-Palestinian confrontation from different POVs-outgroup's, ingroup's while imagining outgroup perspective or ingroup's without imagined perspective-taking. Participants immersed in the outgroup's POV, but not those who imagined the outgroup's perspective, perceived the Palestinians more positively than those immersed in the ingroup's POV. Moreover, participants in the outgroup's POV perceived the Palestinian population in general more favorably and judged a real-life ingroup transgression more strictly than those in the ingroup's POV, even five months after VR intervention. Results suggest that VR can promote conflict resolution by enabling effective perspective-taking.
The Discourse of Online Content Moderation: Investigating Polarized User Responses to Changes in Reddit’s Quarantine Policy

Recent concerns over abusive behavior on their platforms have pressured social media companies to strengthen their content moderation policies.However, user opinions on these policies have been relatively understudied.In this paper, we present an analysis of user responses to a September 27, 2018 announcement about the quarantine policy on Reddit as a case study of to what extent the discourse on content moderation is polarized by users' ideological viewpoint.We introduce a novel partitioning approach for characterizing user polarization based on their distribution of participation across interest subreddits.We then use automated techniques for capturing framing to examine how users with different viewpoints discuss moderation issues, finding that right-leaning users invoked censorship while left-leaning users highlighted inconsistencies on how content policies are applied.Overall, we argue for a more nuanced approach to moderation by highlighting the intersection of behavior and ideology in considering how abusive language is defined and regulated.
Residual Deep Learning System for Mass Segmentation and Classification in Mammography

Automatic extraction of breast mass in mammogram (MG) images is a challenging task due to the varying sizes, shapes, and textures of masses. Moreover, the density of MGs makes mass detection very challenging since masses can be hidden in dense MGs. In this paper, we propose a residual deep learning (DL) system for mass segmentation and classification in mammography. The overall proposed system consists of two cascaded parts: 1) a residual attention U-Net model (RU-Net) to precisely segment mass lesions in MG images, followed by 2) a ResNet classifier to classify the detected binary segmented lesions into benign or malignant. The proposed semantic based CNN model, RU-Net, has the basic architecture of the U-Net model, which extracts contextual information combining low-level feature with high-level ones. We have modified the U-Net structure by adding residual attention modules in order to preserve the spatial and context information, help the network have deeper architecture, and handles the gradient vanishing problem. We compared the performance of the proposed RU-Net model with those of state-of-the-art two semantic segmentation models, and two object detectors using public databases. We also examined the effect of the breast density on the accuracy of localizing and segmenting the breast masses. Our proposed model shows superior performance compared to the other DL methods in detecting and segmenting masses, especially for heterogeneously dense and dense MG images, in terms of intersection over union (IOU) and the Dice index coefficient (DI). Moreover, our results show that the cascaded ResNet model, trained using binary-scale images, classify the masses to benign or malignant with higher accuracy compared to the ResNet model that is trained on gray-scale images.
Co-clustering Ensembles based on Multiple Relevance Measures

Co-clustering aims at discovering groups of both objects and features from a given data matrix. Co-clustering ensembles can produce robust co-clusters by combining multiple base co-clusterings. However, current co-clustering ensemble solutions either ignore the constraints resulting from feature-to-feature and object-to-object relevance information, or ignore feature-to-object relevance information. In this paper, we advocate that all three information sources contribute to the achievement of good consensus solutions, and propose a co-clustering ensemble (CoCE) approach based on multiple relevance measures. CoCE first evaluates the quality of base co-clusters and consequently measures feature-to-object relevance. The latter, along with feature-to-feature and object-to-object relevance measures, contribute to the definition of a hybrid graph. The consensus process uses the resulting hybrid graph; it's formulated as a trace minimization problem and introduces a block-wise matrix multiplication technique to perform the optimization. Experimental results on various datasets show that CoCE not only frequently outperforms other related co-clustering ensembles, but also has reduced runtime cost and is more robust to poor base co-clusterings.
Privacy Preserving Text Representation Learning

Online users generate tremendous amounts of textual information by participating in different online activities. This data provides opportunities for researchers and business partners to understand individuals. However, this user-generated textual data not only can reveal the identity of the user but also may contain individual's private attribute information. Publishing the textual data thus compromises the privacy of users. It is challenging to design effective anonymization techniques for textual information which minimize the chances of re-identification and does not contain private information while retaining the textual semantic meaning. In this paper, we study this problem and propose a novel double privacy preserving text representation learning framework, DPText. We show the effectiveness of DPText in preserving privacy and utility.
Three-dimensional reconstruction with single-shot structured light dot pattern and analytic solutions

Structured light three-dimensional reconstruction is one of the most important techniques in computer vision. However, most existing methods require multiple projections of the designed patterns to achieve the closed form solution, which makes them fail to measure the dynamic objects. In this paper, we propose a two-view based single-shot structured light 3D reconstruction approach that calculates the 3D coordinates of the object with analytical solutions. The designed structured light dot pattern is projected onto the object, distorted and imaged in two cameras. For each point in the structured light dot pattern, there is a ray of light that goes through the optical center and the physically imaged point on the camera’s image plane. Two rays could be determined by two cameras and their intersection solves the unknown point on the object in the 3D world coordinate system. To segment the dot pattern robustly and efficiently, an iterative dot segmentation method is proposed. Experimental results verified the effectiveness of the proposed approach in reconstructing objects without bright colors, rich textures, large discontinuities and obvious occlusions.
Active anomaly detection for time-domain discoveries

Aims. We present the first piece of evidence that adaptive learning techniques can boost the discovery of unusual objects within astronomical light curve data sets. Methods. Our method follows an active learning strategy where the learning algorithm chooses objects that can potentially improve the learner if additional information about them is provided. This new information is subsequently used to update the machine learning model, allowing its accuracy to evolve with each new piece of information. For the case of anomaly detection, the algorithm aims to maximize the number of scientifically interesting anomalies presented to the expert by slightly modifying the weights of a traditional isolation forest (IF) at each iteration. In order to demonstrate the potential of such techniques, we apply the Active Anomaly Discovery algorithm to two data sets: simulated light curves from the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC) and real light curves from the Open Supernova Catalog. We compare the Active Anomaly Discovery results to those of a static IF. For both methods, we performed a detailed analysis for all objects with the ∼2% highest anomaly scores. Results. We show that, in the real data scenario, Active Anomaly Discovery was able to identify ∼80% more true anomalies than the IF. This result is the first piece of evidence that active anomaly detection algorithms can play a central role in the search for new physics in the era of large-scale sky surveys.
A secure and efficient outsourced computation on data sharing scheme for privacy computing

With the development of computer technology, it makes malicious users more easily get data stored in cloud. However, these data are always related to users’ privacy, and it is harmful when the data are acquired by attackers. Ciphertext-policy attribute-based encryption (CP-ABE) is suitable to achieve privacy and security in cloud. In this paper, we put forward a secure and efficient outsourced computation algorithm on data sharing scheme for privacy computing. Existing schemes only outsource decryption computation to the cloud, users still have heavy burden in encryption. In order to reduce the computing burden of users, most encryption and decryption computations are outsourced to the cloud service provider in our construction. At the same time, to apply in practice, we propose efficient user and attribute revocation. Finally, the security analysis and simulation results show that our scheme is secure and efficient compared with existing schemes.
FFESSD: An Accurate and Efficient Single-Shot Detector for Target Detection

The Single Shot MultiBox Detector (SSD) is one of the fastest algorithms in the current target detection field. It has achieved good results in target detection but there are problems such as poor extraction of features in shallow layers and loss of features in deep layers. In this paper, we propose an accurate and efficient target detection method, named Single Shot Object Detection with Feature Enhancement and Fusion (FFESSD), which is to enhance and exploit the shallow and deep features in the feature pyramid structure of the SSD algorithm. To achieve it we introduced the Feature Fusion Module and two Feature Enhancement Modules, and integrated them into the conventional structure of the SSD. Experimental results on the PASCAL VOC 2007 dataset demonstrated that FFESSD achieved 79.1% mean average precision (mAP) at the speed of 54.3 frame per second (FPS) with the input size 300 × 300, while FFESSD with a 512 × 512 sized input achieved 81.8% mAP at 30.2 FPS. The proposed network shows state-of-the-art mAP, which is better than the conventional SSD, Deconvolutional Single Shot Detector (DSSD), Feature-Fusion SSD (FSSD), and other advanced detectors. On extended experiment, the performance of FFESSD in fuzzy target detection was better than the conventional SSD.
Globally Guided Progressive Fusion Network for 3D Pancreas Segmentation

Recently 3D volumetric organ segmentation attracts much research interest in medical image analysis due to its significance in computer aided diagnosis. This paper aims to address the pancreas segmentation task in 3D computed tomography volumes. We propose a novel end-to-end network, Globally Guided Progressive Fusion Network, as an effective and efficient solution to volumetric segmentation, which involves both global features and complicated 3D geometric information. A progressive fusion network is devised to extract 3D information from a moderate number of neighboring slices and predict a probability map for the segmentation of each slice. An independent branch for excavating global features from downsampled slices is further integrated into the network. Extensive experimental results demonstrate that our method achieves state-of-the-art performance on two pancreas datasets.
Rethinking Encrypted Traffic Classification: A Multi-Attribute Associated Fingerprint Approach

With the unprecedented prevalence of mobile network applications, cryptographic protocols, such as the Secure Socket Layer/Transport Layer Security (SSL/TLS), are widely used in mobile network applications for communication security. The proven methods for encrypted video stream classification or encrypted protocol detection are unsuitable for the SSL/TLS traffic. Consequently, application-level traffic classification based networking and security services are facing severe challenges in effectiveness. Existing encrypted traffic classification methods exhibit unsatisfying accuracy for applications with similar state characteristics. In this paper, we propose a multiple-attribute-based encrypted traffic classification system named Multi-Attribute Associated Fingerprints (MAAF). We develop MAAF based on the two key insights that the DNS traces generated during the application runtime contain classification guidance information and that the handshake certificates in the encrypted flows can provide classification clues. Apart from the exploitation of key insights, MAAF employs the context of the encrypted traffic to overcome the attribute-lacking problem during the classification. Our experimental results demonstrate that MAAF achieves 98.69% accuracy on the real-world traceset that consists of 16 applications, supports the early prediction, and is robust to the scale of the training traceset. Besides, MAAF is superior to the state-of-the-art methods in terms of both accuracy and robustness.
When Choosing Plausible Alternatives, Clever Hans can be Clever

Pretrained language models, such as BERT and RoBERTa, have shown large improvements in the commonsense reasoning benchmark COPA.However, recent work found that many improvements in benchmarks of natural language understanding are not due to models learning the task, but due to their increasing ability to exploit superficial cues, such as tokens that occur more often in the correct answer than the wrong one.Are BERT's and RoBERTa's good performance on COPA also caused by this?We find superficial cues in COPA, as well as evidence that BERT exploits these cues.To remedy this problem, we introduce Balanced COPA, an extension of COPA that does not suffer from easy-toexploit single token cues.We analyze BERT's and RoBERTa's performance on original and Balanced COPA, finding that BERT relies on superficial cues when they are present, but still achieves comparable performance once they are made ineffective, suggesting that BERT learns the task to a certain degree when forced to.In contrast, RoBERTa does not appear to rely on superficial cues.