Reviewerly/exHarmony huggingface dataset:
Document-level Event Extraction with Efficient End-to-end Learning of Cross-event Dependencies

Fully understanding narratives often requires identifying events in the context of whole documents and modeling the event relations.However, document-level event extraction is a challenging task as it requires the extraction of event and entity coreference, and capturing arguments that span across different sentences.Existing works on event extraction usually confine on extracting events from single sentences, which fail to capture the relationships between the event mentions at the scale of a document, as well as the event arguments that appear in a different sentence than the event trigger.In this paper, we propose an end-to-end model leveraging Deep Value Networks (DVN), a structured prediction algorithm, to efficiently capture cross-event dependencies for document-level event extraction.Experimental results show that our approach achieves comparable performance to CRF-based models on ACE05, while enjoys significantly higher computational efficiency.
An Efficient Transformer Decoder with Compressed Sub-layers

The large attention-based encoder-decoder network (Transformer) has become prevailing recently due to its effectiveness. But the high computation complexity of its decoder raises the inefficiency issue. By examining the mathematic formulation of the decoder, we show that under some mild conditions, the architecture could be simplified by compressing its sub-layers, the basic building block of Transformer, and achieves a higher parallelism. We thereby propose Compressed Attention Network, whose decoder layer consists of only one sub-layer instead of three. Extensive experiments on 14 WMT machine translation tasks show that our model is 1.42x faster with performance on par with a strong baseline. This strong baseline is already 2x faster than the widely used standard baseline without loss in performance.
Learning Contextualized Knowledge Structures for Commonsense Reasoning

Recently, knowledge graph (KG) augmented models have achieved noteworthy success on various commonsense reasoning tasks.However, KG edge (fact) sparsity and noisy edge extraction/generation often hinder models from obtaining useful knowledge to reason over.To address these issues, we propose a new KG-augmented model: Hybrid Graph Network (HGN).Unlike prior methods, HGN learns to jointly contextualize extracted and generated knowledge by reasoning over both within a unified graph structure.Given the task input context and an extracted KG subgraph, HGN is trained to generate embeddings for the subgraph's missing edges to form a "hybrid" graph, then reason over the hybrid graph while filtering out context-irrelevant edges.We demonstrate HGN's effectiveness through considerable performance gains across four commonsense reasoning benchmarks, plus a user study on edge validness and helpfulness. 1
A Large-Scale Chinese Multimodal NER Dataset with Speech Clues

In this paper, we aim to explore an uncharted territory, which is Chinese multimodal named entity recognition (NER) with both textual and acoustic contents.To achieve this, we construct a large-scale human-annotated Chinese multimodal NER dataset, named CNERTA.Our corpus totally contains 42,987 annotated sentences accompanying by 71 hours of speech data.Based on this dataset, we propose a family of strong and representative baseline models, which can leverage textual features or multimodal features.Upon these baselines, to capture the natural monotonic alignment between the textual modality and the acoustic modality, we further propose a simple multimodal multitask model by introducing a speech-to-text alignment auxiliary task.Through extensive experiments, we observe that: (1) Progressive performance boosts as we move from unimodal to multimodal, verifying the necessity of integrating speech clues into Chinese NER.(2) Our proposed model yields state-of-the-art (SoTA) results on CNERTA, demonstrating its effectiveness.For further research, the annotated
On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory

This paper tackles the problem of Lipschitz regularization of Convolutional Neural Networks. Lipschitz regularity is now established as a key property of modern deep learning with implications in training stability, generalization, robustness against adversarial examples, etc. However, computing the exact value of the Lipschitz constant of a neural network is known to be NP-hard. Recent attempts from the literature introduce upper bounds to approximate this constant that are either efficient but loose or accurate but computationally expensive. In this work, by leveraging the theory of Toeplitz matrices, we introduce a new upper bound for convolutional layers that is both tight and easy to compute. Based on this result we devise an algorithm to train Lipschitz regularized Convolutional Neural Networks.
Unsupervised Out-of-Domain Detection via Pre-trained Transformers

Deployed real-world machine learning applications are often subject to uncontrolled and even potentially malicious inputs.Those outof-domain inputs can lead to unpredictable outputs and sometimes catastrophic safety issues.Prior studies on out-of-domain detection require in-domain task labels and are limited to supervised classification scenarios.Our work tackles the problem of detecting out-ofdomain samples with only unsupervised indomain data.We utilize the latent representations of pre-trained transformers and propose a simple yet effective method to transform features across all layers to construct outof-domain detectors efficiently.Two domainspecific fine-tuning approaches are further proposed to boost detection accuracy.Our empirical evaluations of related methods on two datasets validate that our method greatly improves out-of-domain detection ability in a more general scenario. 1
Keep It Simple: Unsupervised Simplification of Multi-Paragraph Text

This work presents Keep it Simple (KiS), a new approach to unsupervised text simplification which learns to balance a reward across three properties: fluency, salience and simplicity.We train the model with a novel algorithm to optimize the reward (k-SCST), in which the model proposes several candidate simplifications, computes each candidate's reward, and encourages candidates that outperform the mean reward.Finally, we propose a realistic text comprehension task as an evaluation method for text simplification.When tested on the English news domain, the KiS model outperforms strong supervised baselines by more than 4 SARI points, and can help people complete a comprehension task an average of 18% faster while retaining accuracy, when compared to the original text.
Language Models Use Monotonicity to Assess NPI Licensing

We investigate the semantic knowledge of language models (LMs), focusing on (1) whether these LMs create categories of linguistic environments based on their semantic monotonicity properties, and (2) whether these categories play a similar role in LMs as in human language understanding, using negative polarity item licensing as a case study.We introduce a series of experiments consisting of probing with diagnostic classifiers (DCs), linguistic acceptability tasks, as well as a novel DC ranking method that tightly connects the probing results to the inner workings of the LM.By applying our experimental pipeline to LMs trained on various filtered corpora, we are able to gain stronger insights into the semantic generalizations that are acquired by these models.1
Activity Image-to-Video Retrieval by Disentangling Appearance and Motion

With the rapid emergence of video data, image-to-video retrieval has attracted much attention. There are two types of image-to-video retrieval: instance-based and activity-based. The former task aims to retrieve videos containing the same main objects as the query image, while the latter focuses on finding the similar activity. Since dynamic information plays a significant role in the video, we pay attention to the latter task to explore the motion relation between images and videos. In this paper, we propose a Motion-assisted Activity Proposal-based Image-to-Video Retrieval (MAP-IVR) approach to disentangle the video features into motion features and appearance features and obtain appearance features from the images. Then, we perform image-to-video translation to improve the disentanglement quality. The retrieval is performed in both appearance and video feature spaces. Extensive experiments demonstrate that our MAP-IVR approach remarkably outperforms the state-of-the-art approaches on two benchmark activity-based video datasets.
Obstacles to Applying Electronic Exams amidst the COVID-19 Pandemic: An Exploratory Study in the Palestinian Universities in Gaza

In the context of the COVID-19 pandemic, we aim to identify and understand the obstacles and barriers in applying electronic exams successfully in the process of distance education. We followed an exploratory descriptive approach through a questionnaire (one general, open question) with a sample of university teachers and students in four of the largest universities Palestinian in Gaza. A total of 152 were returned from 300 distributed questionnaires. The results indicate that the university teachers and students faced 13 obstacles, of which 9 were shown to be shared between teachers and students, with a significant agreement in the regression analysis. Several of the obstacles perceived by respondents are in line with the literature and can be addressed by improved examination design, training, and preparation, or use of suitable software. Other obstacles related to infrastructure issues, leading to frequent power outages and unreliable internet access. Difficult living conditions in students’ homes and disparities in access to suitable devices or the internet make social equity in connection with high-stakes examinations a major concern. Some recommendations and suggestions are listed at the end of this study, considering local conditions in the Gaza governorates.
O2NA: An Object-Oriented Non-Autoregressive Approach for Controllable Video Captioning

Video captioning combines video understanding and language generation.Different from image captioning that describes a static image with details of almost every object, video captioning usually considers a sequence of frames and biases towards focused objects, e.g., the objects that stay in focus regardless of the changing background.Therefore, detecting and properly accommodating focused objects is critical in video captioning.To enforce the description of focused objects and achieve controllable video captioning, we propose an Object-Oriented Non-Autoregressive approach (O2NA), which performs caption generation in three steps: 1) identify the focused objects and predict their locations in the target caption; 2) generate the related attribute words and relation words of these focused objects to form a draft caption; and 3) combine video information to refine the draft caption to a fluent final caption.Since the focused objects are generated and located ahead of other words, it is difficult to apply the word-by-word autoregressive generation process; instead, we adopt a non-autoregressive approach.The experiments on two benchmark datasets, i.e., MSR-VTT and MSVD, demonstrate the effectiveness of O2NA, which achieves results competitive with the state-of-the-arts but with both higher diversity and higher inference speed.* Equal Contributions. Conventional Video Captioning Modela man is watching people ride down a road.
Investigating cross-lingual training for offensive language detection

Platforms that feature user-generated content (social media, online forums, newspaper comment sections etc.) have to detect and filter offensive speech within large, fast-changing datasets. While many automatic methods have been proposed and achieve good accuracies, most of these focus on the English language, and are hard to apply directly to languages in which few labeled datasets exist. Recent work has therefore investigated the use of cross-lingual transfer learning to solve this problem, training a model in a well-resourced language and transferring to a less-resourced target language; but performance has so far been significantly less impressive. In this paper, we investigate the reasons for this performance drop, via a systematic comparison of pre-trained models and intermediate training regimes on five different languages. We show that using a better pre-trained language model results in a large gain in overall performance and in zero-shot transfer, and that intermediate training on other languages is effective when little target-language data is available. We then use multiple analyses of classifier confidence and language model vocabulary to shed light on exactly where these gains come from and gain insight into the sources of the most typical mistakes.
Entanglement-Free Parameter Estimation of Generalized Pauli Channels

We propose a parameter estimation protocol for generalized Pauli channels acting on $d$-dimensional Hilbert space. The salient features of the proposed method include product probe states and measurements, the number of measurement configurations linear in $d$, minimal post-processing, and the scaling of the mean square error comparable to that of the entanglement-based parameter estimation scheme for generalized Pauli channels. We also show that while measuring generalized Pauli operators the errors caused by the Pauli noise can be modeled as measurement errors. This makes it possible to utilize the measurement error mitigation framework to mitigate the errors caused by the generalized Pauli channels. We use this result to mitigate noise on the probe states and recover the scaling of the noiseless probes, except with a noise strength-dependent constant factor. This method of modeling Pauli channel as measurement noise can also be of independent interest in other NISQ tasks, e.g., state tomography problems, variational quantum algorithms, and other channel estimation problems where Pauli measurements have the central role.
Damped Newton Stochastic Gradient Descent Method for Neural Networks Training

First-order methods such as stochastic gradient descent (SGD) have recently become popular optimization methods to train deep neural networks (DNNs) for good generalization; however, they need a long training time. Second-order methods which can lower the training time are scarcely used on account of their overpriced computing cost to obtain the second-order information. Thus, many works have approximated the Hessian matrix to cut the cost of computing while the approximate Hessian matrix has large deviation. In this paper, we explore the convexity of the Hessian matrix of partial parameters and propose the damped Newton stochastic gradient descent (DN-SGD) method and stochastic gradient descent damped Newton (SGD-DN) method to train DNNs for regression problems with mean square error (MSE) and classification problems with cross-entropy loss (CEL). In contrast to other second-order methods for estimating the Hessian matrix of all parameters, our methods only accurately compute a small part of the parameters, which greatly reduces the computational cost and makes the convergence of the learning process much faster and more accurate than SGD and Adagrad. Several numerical experiments on real datasets were performed to verify the effectiveness of our methods for regression and classification problems.
A Study on Mitigating the Communication and Coordination Challenges During Requirements Change Management in Global Software Development

Global Software Development (GSD) is widely used by software development organizations to ensure the development of a cost-effective software product. GSD has now become a common engineering practice adopted by a significant number of multinational software development organizations, and even individuals (freelancers) are seeking numerous benefits including low development cost, highly skilled workers, and access to better development ideas. However, communication and coordination challenges remain a prominent research issue in the GSD context, while performing different project-related activities especially for Requirements Change Management (RCM). As a result, improper communication and coordination during RCM require additional time, cost, and development resources. Thus, it is of vital importance to ensure proper communication and coordination before initiating a software project. Inspired by this, current work aims at exploring and mitigating the communication and coordination challenges during RCM in the GSD context. To accomplish the targeted research objective, we performed a tertiary study to provide a landscape of the challenges that occurred during RCM in the context of GSD. Based on the performed study, we found 62 communication and 14 coordination challenges. In total, 107 mitigation strategies are explored and reported that effectively address the categorized sub-challenges of communication and coordination. Moreover, we proposed a conceptual model useful to address the communication and coordination challenges for the RCM process in GSD. Furthermore, we consulted the domain experts for the validation of the proposed conceptual model. Based on the promising results, we believe that this work supports the project managers in managing the cost and time-related issues in the GSD context. Consequently, the proposed conceptual model would help in optimally utilizing the scared software development resources.
Convolutional Neural Opacity Radiance Fields

Photo-realistic modeling and rendering of fuzzy objects with complex opacity are critical for numerous immersive VR/AR applications, but it suffers from strong view-dependent brightness, color. In this paper, we propose a novel scheme to generate opacity radiance fields with a convolutional neural renderer for fuzzy objects, which is the first to combine both explicit opacity supervision and convolutional mechanism into the neural radiance field framework so as to enable high-quality appearance and global consistent alpha mattes generation in arbitrary novel views. More specifically, we propose an efficient sampling strategy along with both the camera rays and image plane, which enables efficient radiance field sampling and learning in a patch-wise manner, as well as a novel volumetric feature integration scheme that generates per-patch hybrid feature embeddings to reconstruct the view-consistent fine-detailed appearance and opacity output. We further adopt a patch-wise adversarial training scheme to preserve both high-frequency appearance and opacity details in a self-supervised framework. We also introduce an effective multi-view image capture system to capture high-quality color and alpha maps for challenging fuzzy objects. Extensive experiments on existing and our new challenging fuzzy object dataset demonstrate that our method achieves photo-realistic, globally consistent, and fined detailed appearance and opacity free-viewpoint rendering for various fuzzy objects.
Automated Framework for Real-Time Sentiment Analysis

There are many fascinating headings that can be investigated. Newspapers, blogs, articles print a lot of content and depicts a lot about a person’s positive and negative aspects. Through this project we can estimate those numbers of positivity, negativity or neutrality. This is the way toward recognizing and arranging sentiments communicated in a bit of content, particularly to decide if an author’s disposition towards a point/item is sure, negative or impartial. It is utilized to check how positive or how negative an announcement is. This analysis is like manner called sentiment mining or opinion AI, is the path toward deciding if a touch of making is sure, negative, or unprejudiced. A run of the mill use case for this development is to discover how people feel about a subject. Sentiment analysis is broadly connected to audits and internet-based life for an assortment of utilizations. Sentiment analysis can be performed from various perspectives. Numerous brands and advertisers use watchword-based apparatuses that arrange information as positive/negative/impartial. We are keen on how assumption can change by statistic gathering, news source or geographic area. By extending our spatial examination of news elements to feeling maps, we can recognize topographical locales of good or unfavorable sentiments for given elements. We are additionally examining how much our opinion lists helps us predicting future changes in society or market conduct. With the quick development of web-based life, sentiment analysis, additionally called opinion mining, has turned out to be a standout amongst the most dynamic research regions in natural language processing. Its application is additionally boundless, from business administrations to political campaigns. It is used to manage Twitter and its APIs which provide relationships a fast and reliable mechanism to screen the people’s beliefs and emotions towards their brand, business, chiefs, and so on. A wide scope of highlights and strategies for training sentiment classifiers for Twitter datasets have been explored as of late with changing outcomes. In this paper, for each removed substance from tweets, we include its semantic idea as an extra element and measure the connection of the agent idea with negative/positive sentiment. We apply this way to deal with foresee sentiment from various Twitter datasets. In our outcome, after characterizing the Negative, Neutral and positive sentiments, we print the most regularly utilized words in our sentiment in the bar graph and Pie-chart, helping the client to think about the sentiments more productively and successfully. It is one of the speediest creating investigation zones in programming designing, making it attempt to screen all of the activities in the area. Starting late, assumption examination has moved from exploring on the web thing studies to web-based life compositions from Twitter and Facebook. Various focuses past thing reviews like securities trades, races, fiascos, tranquilize, programming building and cyberbullying grow the use of assumption investigation.
Graph Neural Networks Using Local Descriptions in Attributed Graphs: An Application to Symbol Recognition and Hand Written Character Recognition

Graph-based methods have been widely used by the document image analysis and recognition community, as the different objects and the content in document images is best represented by this powerful structural representation. Designing of novel computation tools for processing these graph-based structural representations has always remained a hot topic of research. Recently, Graph Neural Network (GNN) have been used for solving different problems in the domain of document image analysis and recognition. In this article we take forward the state of the art by presenting a new approach to gather the symbolic and numeric information from the nodes and edges of a graph. We use this information to learn a Graph Neural Network (GNN). The experimentation on the recognition of handwritten letters and graphical symbols shows that the proposed approach is an interesting contribution to the growing set of GNN-based methods for document image analysis and recognition.