Reviewerly/exHarmony huggingface dataset:
Multi-Granularity Feature Interaction and Relation Reasoning for 3D Dense Alignment and Face Reconstruction

In this paper, we propose a multi-granularity feature interaction and relation reasoning network (MFIRRN) which can recover a detail-rich 3D face and perform more accurate dense alignment in an unconstrained environment. Traditional 3DMM-based methods directly regress parameters, resulting in the lack of fine-grained details in the reconstruction 3D face. To this end, we use different branches to capture discriminative features at different granularities, especially local features at medium and fine granularities. Meanwhile, the finer-grained branch network shares its information with the adjacent coarser-grained branch network to achieve feature interaction. Our model performs cross-granular information integration and inter-granular relationship reasoning to obtain prediction results. Extensive experiments on AFLW2000-3D and AFLW datasets demonstrate the validity of our method. The code is publicly available at https://github.com/leilimaster/MFIRRN.
Findings of the AmericasNLP 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas

This paper presents the results of the 2021 Shared Task on Open Machine Translation for Indigenous Languages of the Americas.The shared task featured two independent tracks, and participants submitted machine translation systems for up to 10 indigenous languages.Overall, 8 teams participated with a total of 214 submissions.We provided training sets consisting of data collected from various sources, as well as manually translated sentences for the development and test sets.An official baseline trained on this data was also provided.Team submissions featured a variety of architectures, including both statistical and neural models, and for the majority of languages, many teams were able to considerably improve over the baseline.The best performing systems achieved 12.97 ChrF higher than baseline, when averaged across languages.
E-Learning Based on Cloud Computing

Modern technological paradigms of learning give educators an ability to support the development of highly professional human resources. For this reason, teachers of higher educational institutions find advanced technologies more and more useful. Rapid introduction of cloud computing into pedagogical practice creates a professional need to study the technological capabilities of cloud-based learning services to select the best educational solutions among available variants. In view of this, the current article provides a theoretical overview of e-learning cloud architecture layers and models of its deployment in the education system. In order to test the adaptability of learning management systems (LMSs) to a collaborative distance learning model, research among educators of I.M. Sechenov First Moscow State Medical University (Russia), Prydniprovska State Academy of Civil Engineering and Architecture (Ukraine) and Wuxi Institute of Technology (China) was carried out using the Blackboard Learn LMS as a case study. This study involved 100 individuals who tested the Blackboard Learn teaching capabilities. The conducted investigation allowed outlining the main advantages of the use of computer-based e-learning applications in the process of organizing and supporting the educational process. Among them are convenient means of processing educational content, effective educational process organization, effective tools for knowledge monitoring, and effective security and confidentiality system. The present work also shed light on the benefits of introducing cloud-based e-learning in higher educational institutions, as producers of an integrated educational product. The study confirmed that the introduction of cloud computing in education results in the advancement of the training content and can considerably improve students’ academic results due to updated learning technologies, concepts, and tools.
Few-shot Intent Classification and Slot Filling with Retrieved Examples

Few-shot learning arises in important practical scenarios, such as when a natural language understanding system needs to learn new semantic labels for an emerging, resource-scarce domain.In this paper, we explore retrieval-based methods for intent classification and slot filling tasks in few-shot settings.Retrieval-based methods make predictions based on labeled examples in the retrieval index that are similar to the input, and thus can adapt to new domains simply by changing the index without having to retrain the model.However, it is nontrivial to apply such methods on tasks with a complex label space like slot filling.To this end, we propose a span-level retrieval method that learns similar contextualized representations for spans with the same label via a novel batch-softmax objective.At inference time, we use the labels of the retrieved spans to construct the final structure with the highest aggregated score.Our method outperforms previous systems in various few-shot settings on the CLINC and SNIPS benchmarks.
PANDORA Talks: Personality and Demographics on Reddit

Personality and demographics are important variables in social sciences and computational sociolinguistics.However, datasets with both personality and demographic labels are scarce.To address this, we present PANDORA, the first dataset of Reddit comments of 10k users partially labeled with three personality models and demographics (age, gender, and location), including 1.6k users labeled with the wellestablished Big 5 personality model.We showcase the usefulness of this dataset on three experiments, where we leverage the more readily available data from other personality models to predict the Big 5 traits, analyze gender classification biases arising from psychodemographic variables, and carry out a confirmatory and exploratory analysis based on psychological theories.Finally, we present benchmark prediction models for all personality and demographic variables.
WEC: Deriving a Large-scale Cross-document Event Coreference dataset from Wikipedia

Cross-document event coreference resolution is a foundational task for NLP applications involving multi-text processing.However, existing corpora for this task are scarce and relatively small, while annotating only modestsize clusters of documents belonging to the same topic.To complement these resources and enhance future research, we present Wikipedia Event Coreference (WEC), an efficient methodology for gathering a largescale dataset for cross-document event coreference from Wikipedia, where coreference links are not restricted within predefined topics.We apply this methodology to the English Wikipedia and extract our large-scale WEC-Eng dataset.Notably, our dataset creation method is generic and can be applied with relatively little effort to other Wikipedia languages.To set baseline results, we develop an algorithm that adapts components of stateof-the-art models for within-document coreference resolution to the cross-document setting.Our model is suitably efficient and outperforms previously published state-of-the-art results for the task.
Towards Interpreting and Mitigating Shortcut Learning Behavior of NLU models

Recent studies indicate that NLU models are prone to rely on shortcut features for prediction, without achieving true language understanding.As a result, these models fail to generalize to real-world out-of-distribution data.In this work, we show that the words in the NLU training set can be modeled as a longtailed distribution.There are two findings: 1) NLU models have strong preference for features located at the head of the long-tailed distribution, and 2) Shortcut features are picked up during very early few iterations of the model training.These two observations are further employed to formulate a measurement which can quantify the shortcut degree of each training sample.Based on this shortcut measurement, we propose a shortcut mitigation framework LTGR, to suppress the model from making overconfident predictions for samples with large shortcut degree.Experimental results on three NLU benchmarks demonstrate that our long-tailed distribution explanation accurately reflects the shortcut learning behavior of NLU models.Experimental analysis further indicates that LTGR can improve the generalization accuracy on OOD data, while preserving the accuracy on in-distribution data.
TNT-KID: Transformer-based neural tagger for keyword identification

Abstract With growing amounts of available textual data, development of algorithms capable of automatic analysis, categorization, and summarization of these data has become a necessity. In this research, we present a novel algorithm for keyword identification, that is, an extraction of one or multiword phrases representing key aspects of a given document, called Transformer-Based Neural Tagger for Keyword IDentification (TNT-KID). By adapting the transformer architecture for a specific task at hand and leveraging language model pretraining on a domain-specific corpus, the model is capable of overcoming deficiencies of both supervised and unsupervised state-of-the-art approaches to keyword extraction by offering competitive and robust performance on a variety of different datasets while requiring only a fraction of manually labeled data required by the best-performing systems. This study also offers thorough error analysis with valuable insights into the inner workings of the model and an ablation study measuring the influence of specific components of the keyword identification workflow on the overall performance.
Unifying Cross-Lingual Semantic Role Labeling with Heterogeneous Linguistic Resources

While cross-lingual techniques are finding increasing success in a wide range of Natural Language Processing tasks, their application to Semantic Role Labeling (SRL) has been strongly limited by the fact that each language adopts its own linguistic formalism, from Prop-Bank for English to AnCora for Spanish and PDT-Vallex for Czech, inter alia.In this work, we address this issue and present a unified model to perform cross-lingual SRL over heterogeneous linguistic resources.Our model implicitly learns a high-quality mapping for different formalisms across diverse languages without resorting to word alignment and/or translation techniques.We find that, not only is our cross-lingual system competitive with the current state of the art but that it is also robust to low-data scenarios.Most interestingly, our unified model is able to annotate a sentence in a single forward pass with all the inventories it was trained with, providing a tool for the analysis and comparison of linguistic theories across different languages.We release our code and model at https://github.com/SapienzaNLP/unify-srl.
Penggunaan Teknologi Informasi dan Komunikasi dalam Sistem Manajemen Pembelajaran pada Masa Pandemi Covid 19

The purpose of this study is to examine the use of information technology in management systems in tertiary institutions in Indonesia which are still not maximally and evenly utilized both in terms of equipment and management. This research is an international journal study. The results of input resource research include visualizer / document camera, computer, whiteboard, student response system, and application program. Output resources include projectors, interactive whiteboards, and displays: monitors, televisions and more. Finally, other ICT equipment includes digital cameras, switchers, digital recorders, and other innovations. Conclusions, discussion of solutions to overcome obstacles in the form of uneven infrastructure that supports the application of this technology in education and the unpreparedness of human resources to utilize information and communication technology in the learning process.&#x0D; Keywords: Information Technology, Communication, Learning Management
Preview, Attend and Review: Schema-Aware Curriculum Learning for Multi-Domain Dialogue State Tracking

Existing dialog state tracking (DST) models are trained with dialog data in a random order, neglecting rich structural information in a dataset.In this paper, we propose to use curriculum learning (CL) to better leverage both the curriculum structure and schema structure for task-oriented dialogs.Specifically, we propose a model-agnostic framework called Schema-aware Curriculum Learning for Dialog State Tracking (SaCLog), which consists of a preview module that pre-trains a DST model with schema information, a curriculum module that optimizes the model with CL, and a review module that augments mispredicted data to reinforce the CL training.We show that our proposed approach improves DST performance over both a transformerbased and RNN-based DST model (TripPy and TRADE) and achieves new state-of-the-art results on WOZ2.0 and MultiWOZ2.1.
Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering

Many Question-Answering (QA) datasets contain unanswerable questions, but their treatment in QA systems remains primitive.Our analysis of the Natural Questions (Kwiatkowski et al., 2019) dataset reveals that a substantial portion of unanswerable questions (∼21%) can be explained based on the presence of unverifiable presuppositions.Through a user preference study, we demonstrate that the oracle behavior of our proposed system-which provides responses based on presupposition failure-is preferred over the oracle behavior of existing QA systems.Then, we present a novel framework for implementing such a system in three steps: presupposition generation, presupposition verification, and explanation generation, reporting progress on each.Finally, we show that a simple modification of adding presuppositions and their verifiability to the input of a competitive end-to-end QA system yields modest gains in QA performance and unanswerability detection, demonstrating the promise of our approach.
Discriminative Reasoning for Document-level Relation Extraction

Document-level relation extraction (DocRE)models generally use graph networks to implicitly model the reasoning skill (i.e., pattern recognition, logical reasoning, coreference reasoning, etc.) related to the relation between one entity pair in a document.In this paper, we propose a novel discriminative reasoning framework to explicitly model the paths of these reasoning skills between each entity pair in this document.Thus, a discriminative reasoning network is designed to estimate the relation probability distribution of different reasoning paths based on the constructed graph and vectorized document contexts for each entity pair, thereby recognizing their relation.Experimental results show that our method outperforms the previous state-of-theart performance on the large-scale DocRE dataset.
OTTers: One-turn Topic Transitions for Open-Domain Dialogue

Mixed initiative in open-domain dialogue requires a system to pro-actively introduce new topics.The one-turn topic transition task explores how a system connects two topics in a cooperative and coherent manner.The goal of the task is to generate a "bridging" utterance connecting the new topic to the topic of the previous conversation turn.We are especially interested in commonsense explanations of how a new topic relates to what has been mentioned before.We first collect a new dataset of human one-turn topic transitions, which we call OTTers 1 .We then explore different strategies used by humans when asked to complete such a task, and notice that the use of a bridging utterance to connect the two topics is the approach used the most.We finally show how existing state-of-the-art text generation models can be adapted to this task and examine the performance of these baselines on different splits of the OTTers data.A: i like to eat the same thing as ninja turtles.T: I love pizza.I eat it while I skateboard.B: i enjoy riding around on a plank
Diachronic Analysis of Qualitative Data

The requirement that must be met to perform each of the diachronic analysis that are described in this chapter is that the authors have an intrasessional following record, which implies the continuous record of each observational session. And our proposal consists of organizing qualitative data in a systematized structure (matrix of codes), that is qualitative, but that is possible to analyze quantitatively considering their temporal organization. Integration plays a crucial role in the diachronic analysis of qualitative data within the framework of mixed methods. The interest in diachronic studies has been evidenced in various ways and, here, the authors are particularly interested in highlighting the quantitative analysis of qualitative data from direct and indirect systematic observation. The lag sequential analysis, the analysis of polar coordinates, and the TPA offer three different ways for a diachronic analysis, always involving scrutinizing the structure of behavior, and from rigorous quantitative analyses that are very suitable for qualitative data.
Fusing Label Embedding into BERT: An Efficient Improvement for Text Classification

With pre-trained models, such as BERT, gaining more and more attention, plenty of research has been done to further promote their capabilities, from enhancing the experimental procedures (Sun et al., 2019) to improving the mathematical principles.In this paper, we propose a concise method for improving BERT's performance in text classification by utilizing a label embedding technique while keeping almost the same computational cost.Experimental results on six text classification benchmark datasets demonstrate its effectiveness.
Single-Atom Verification of the Noise-Resilient and Fast Characteristics of Universal Nonadiabatic Noncyclic Geometric Quantum Gates

Quantum gates induced by geometric phases are intrinsically robust against noise due to the global properties of their evolution paths. Compared to conventional nonadiabatic geometric quantum computation, the recently proposed nonadiabatic noncyclic geometric quantum computation (NNGQC) works in a faster fashion while still remaining the robust feature of the geometric operations. Here, we experimentally implement the NNGQC in a single trapped ultracold $^{40}{\mathrm{Ca}}^{+}$ ion to verify the noise-resilient and fast feature. By performing unitary operations under imperfect conditions, we witness the advantages of the NNGQC with measured fidelities by quantum process tomography in comparison to other two quantum gates by conventional nonadiabatic geometric quantum computation and by straightforward dynamical evolution. Our results provide the first evidence confirming the possibility of accelerated quantum information processing with limited systematic errors even in an imperfect situation.
DPFPS: Dynamic and Progressive Filter Pruning for Compressing Convolutional Neural Networks from Scratch

Filter pruning is a commonly used method for compressing Convolutional Neural Networks (ConvNets), due to its friendly hardware supporting and flexibility. However, existing methods mostly need a cumbersome procedure, which brings many extra hyper-parameters and training epochs. This is because only using sparsity and pruning stages cannot obtain a satisfying performance. Besides, many works do not consider the difference of pruning ratio across different layers. To overcome these limitations, we propose a novel dynamic and progressive filter pruning (DPFPS) scheme that directly learns a structured sparsity network from Scratch. In particular, DPFPS imposes a new structured sparsity-inducing regularization specifically upon the expected pruning parameters in a dynamic sparsity manner. The dynamic sparsity scheme determines sparsity allocation ratios of different layers and a Taylor series based channel sensitivity criteria is presented to identify the expected pruning parameters. Moreover, we increase the structured sparsity-inducing penalty in a progressive manner. This helps the model to be sparse gradually instead of forcing the model to be sparse at the beginning. Our method solves the pruning ratio based optimization problem by an iterative soft-thresholding algorithm (ISTA) with dynamic sparsity. At the end of the training, we only need to remove the redundant parameters without other stages, such as fine-tuning. Extensive experimental results show that the proposed method is competitive with 11 state-of-the-art methods on both small-scale and large-scale datasets (i.e., CIFAR and ImageNet). Specifically, on ImageNet, we achieve a 44.97% pruning ratio of FLOPs by compressing ResNet-101, even with an increase of 0.12% Top-5 accuracy. Our pruned models and codes are released at https://github.com/taoxvzi/DPFPS.
Towards Robustness of Text-to-SQL Models against Synonym Substitution

Recently, there has been significant progress in studying neural networks to translate text descriptions into SQL queries.Despite achieving good performance on some public benchmarks, existing text-to-SQL models typically rely on the lexical matching between words in natural language (NL) questions and tokens in table schemas, which may render the models vulnerable to attacks that break the schema linking mechanism.In this work, we investigate the robustness of text-to-SQL models to synonym substitution.In particular, we introduce Spider-Syn, a human-curated dataset based on the Spider benchmark for text-to-SQL translation.NL questions in Spider-Syn are modified from Spider, by replacing their schema-related words with manually selected synonyms that reflect real-world question paraphrases.We observe that the accuracy dramatically drops by eliminating such explicit correspondence between NL questions and table schemas, even if the synonyms are not adversarially selected to conduct worst-case adversarial attacks 1 .Finally, we present two categories of approaches to improve the model robustness.The first category of approaches utilizes additional synonym annotations for table schemas by modifying the model input, while the second category is based on adversarial training.We demonstrate that both categories of approaches significantly outperform their counterparts without the defense, and the first category of approaches are more effective. 2
A Theory of Automated Market Makers in DeFi

Automated market makers (AMMs) are one of the most prominent decentralized finance (DeFi) applications. They allow users to exchange units of different types of crypto-assets, without the need to find a counter-party. There are several implementations and models for AMMs, featuring a variety of sophisticated economic mechanisms. We present a theory of AMMs. The core of our theory is an abstract operational model of the interactions between users and AMMs, which can be concretised by instantiating the economic mechanisms. We exploit our theory to formally prove a set of fundamental properties of AMMs, characterizing both structural and economic aspects. We do this by abstracting from the actual economic mechanisms used in implementations and identifying sufficient conditions which ensure the relevant properties. Notably, we devise a general solution to the arbitrage problem, the main game-theoretic foundation behind the economic mechanisms of AMMs.