Reviewerly/exHarmony huggingface dataset:
Mitigation of Disability Hate Content

Popularity of Yoga increased rehabilitation opportunities at home. While Internet provides important rehabilitation information hate content may exclude persons with disabilities from online platforms.  The purpose of the paper is to outline how the practice of Yoga can be used as a tool to reduce online hate content. The literature encompassing Yoga with emotions and emotions with hate content is reviewed. With the help of mathematical transitive relation, the relationship between Yoga practice and hate content control is established. The practice of Yoga indicated that the behavior of an individual can be improved by controlling emotions. On the other hand, studies shown that control of emotions reduces hate content in cyberworld. Transitively, it is observed that the practice of Yoga may reduce online hate content. The researchers, stakeholders of Yoga, governments and rehabilitation centers will get opportunity to improve understanding about usefulness of Yoga in hate content reduction. Policy recommendations for reducing hate content and enhancing rehabilitation capacity using Yoga will be made. The presented approach to mitigate hate content is a triangular theory/concept involving the components Yoga, emotions and hate content.
Automatic Error Type Annotation for Arabic

We present ARETA, an automatic error type annotation system for Modern Standard Arabic. We design ARETA to address Arabic’s morphological richness and orthographic ambiguity. We base our error taxonomy on the Arabic Learner Corpus (ALC) Error Tagset with some modifications. ARETA achieves a performance of 85.8% (micro average F1 score) on a manually annotated blind test portion of ALC. We also demonstrate ARETA’s usability by applying it to a number of submissions from the QALB 2014 shared task for Arabic grammatical error correction. The resulting analyses give helpful insights on the strengths and weaknesses of different submissions, which is more useful than the opaque M2 scoring metrics used in the shared task. ARETA employs a large Arabic morphological analyzer, but is completely unsupervised otherwise. We make ARETA publicly available.
Beltrami Flow and Neural Diffusion on Graphs

We propose a novel class of graph neural networks based on the discretised Beltrami flow, a non-Euclidean diffusion PDE. In our model, node features are supplemented with positional encodings derived from the graph topology and jointly evolved by the Beltrami flow, producing simultaneously continuous feature learning and topology evolution. The resulting model generalises many popular graph neural networks and achieves state-of-the-art results on several benchmarks.
Generate & Rank: A Multi-task Framework for Math Word Problems

Math word problem (MWP) is a challenging and critical task in natural language processing. Many recent studies formalize MWP as a generation task and have adopted sequence-to-sequence models to transform problem descriptions to mathematical expressions. However, mathematical expressions are prone to minor mistakes while the generation objective does not explicitly handle such mistakes. To address this limitation, we devise a new ranking task for MWP and propose Generate & Rank, a multi-task framework based on a generative pre-trained language model. By joint training with generation and ranking, the model learns from its own mistakes and is able to distinguish between correct and incorrect expressions. Meanwhile, we perform tree-based disturbance specially designed for MWP and an online update to boost the ranker. We demonstrate the effectiveness of our proposed method on the benchmark and the results show that our method consistently outperforms baselines in all datasets. Particularly, in the classical Math23k, our method is 7% (78.4% to 85.4%) higher than the state-of-the-art. Code could be found at https://github.com/huawei-noah/noah-research.
Robust Optimization for Multilingual Translation with Imbalanced Data

Multilingual models are parameter-efficient with the prospect improving low-resource languages by leveraging crosslingual transfer. Despite recent advance in massive multilingual translation with ever-growing model and data, how to effectively train multilingual models has not been well understood. In this paper, we show that a common situation in multilingual training, data imbalance among languages, poses optimization tension between high resource and low resource languages where the found multilingual solution is often sub-optimal for low resources. We show that common training method which upsamples low resources can not robustly optimize population loss with risks of either underfitting high resource languages or overfitting low resource ones. Drawing on recent findings on the geometry of loss landscape and its effect on generalization, we propose a principled optimization algorithm, Curvature Aware Task Scaling (CATS), which adaptively rescales gradients from different tasks with a meta objective of guiding multilingual training to low-curvature neighborhoods with uniformly low loss for all languages. We ran experiments on common benchmarks (TED, WMT and OPUS-100) with varying degrees of data imbalance. CATS effectively improved multilingual optimization and as a result demonstrated consistent gains on low resources ( to BLEU) without hurting high resources. In addition, CATS is robust to overparameterization and large batch size training, making it a promising training method for massive multilingual models that truly improve low resource languages.
Introduction to the 37th International Conference on Logic Programming Special Issue I

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Span Pointer Networks for Non-Autoregressive Task-Oriented Semantic Parsing

An effective recipe for building seq2seq, non-autoregressive, task-oriented parsers to map utterances to semantic frames proceeds in three steps: encoding an utterance x, predicting a frame’s length |y|, and decoding a |y|-sized frame with utterance and ontology tokens. Though empirically strong, these models are typically bottlenecked by length prediction, as even small inaccuracies change the syntactic and semantic characteristics of resulting frames. In our work, we propose span pointer networks, non-autoregressive parsers which shift the decoding task from text generation to span prediction; that is, when imputing utterance spans into frame slots, our model produces endpoints (e.g., [i, j]) as opposed to text (e.g., “6pm”). This natural quantization of the output space reduces the variability of gold frames, therefore improving length prediction and, ultimately, exact match. Furthermore, length prediction is now responsible for frame syntax and the decoder is responsible for frame semantics, resulting in a coarse-to-fine model. We evaluate our approach on several task-oriented semantic parsing datasets. Notably, we bridge the quality gap between non-autogressive and autoregressive parsers, achieving 87 EM on TOPv2 (Chen et al. 2020). Furthermore, due to our more consistent gold frames, we show strong improvements in model generalization in both cross-domain and cross-lingual transfer in low-resource settings. Finally, due to our diminished output vocabulary, we observe 70% reduction in latency and 83% reduction in memory at beam size 5 compared to prior non-autoregressive parsers.
Understanding Mention Detector-Linker Interaction for Neural Coreference Resolution.

Despite significant recent progress in coreference resolution, the quality of current state-of-the-art systems still considerably trails behind human-level performance. Using the CoNLL-2012 and PreCo datasets, we dissect the best instantiation of the mainstream end-to-end coreference resolution model that underlies most current best-performing coreference systems, and empirically analyze the behavior of its two components: mention detector and mention linker. While the detector traditionally focuses heavily on recall as a design decision, we demonstrate the importance of precision, calling for their balance. However, we point out the difficulty in building a precise detector due to its inability to make important anaphoricity decisions. We also highlight the enormous room for improving the linker and show that the rest of its errors mainly involve pronoun resolution. We propose promising next steps and hope our findings will help future research in coreference resolution.
Object-aware Contrastive Learning for Debiased Scene Representation

Contrastive self-supervised learning has shown impressive results in learningvisual representations from unlabeled images by enforcing invariance againstdifferent data augmentations. However, the learned representations are oftencontextually biased to the spurious scene correlations of different objects orobject and background, which may harm their generalization on the downstreamtasks. To tackle the issue, we develop a novel object-aware contrastivelearning framework that first (a) localizes objects in a self-supervised mannerand then (b) debias scene correlations via appropriate data augmentationsconsidering the inferred object locations. For (a), we propose the contrastiveclass activation map (ContraCAM), which finds the most discriminative regions(e.g., objects) in the image compared to the other images using thecontrastively trained models. We further improve the ContraCAM to detectmultiple objects and entire shapes via an iterative refinement procedure. For(b), we introduce two data augmentations based on ContraCAM, object-awarerandom crop and background mixup, which reduce contextual and background biasesduring contrastive self-supervised learning, respectively. Our experimentsdemonstrate the effectiveness of our representation learning framework,particularly when trained under multi-object images or evaluated under thebackground (and distribution) shifted images.
Short- and Long-Term Mortality Prediction in Critically Ill Cancer Patients Admitted to the Intensive Care Unit (CanICU) Using Machine Learning

Background: Although cancer patients are increasingly admitted to the intensive care unit (ICU) for cancer- or treatment-related complications, improved mortality prediction remains a big challenge. Current prognostic models for a general ICU population underestimate the mortality risk in cancer patients.Methods: We developed CanICU, a machine learning-based 28-day and 1-year mortality prediction program in adult cancer patients admitted to ICU from Medical Information Mart for Intensive Care (MIMIC) database in the USA (n=766), Yonsei Cancer Center (YCC, n=3,571), and Samsung Medical Center in Korea (SMC, n=2,563). We constructed the classifier and compared it with a general prognostic model.Findings: A total of 6,900 patients were included, with a 28-day mortality of 10.2%/12.7%/36.6% and 1-year mortality of 30.0%/31.4%/58.5% in the MIMIC, YCC, and SMC cohort, respectively. Nine clinical and laboratory factors were used to construct the classifier using a random forest machine-learning algorithm. CanICU had the area under the receiver operating characteristic (AUROC) of 0.95 (95% CI 0.93-0.97) for 28-day and 0.79 (95% CI 0.76-0.82) for 1-year mortality, showing better performance than a current prognostic model (Sequential Organ Failure Assessment [SOFA], 0.77 [95% CI 0.72-0.81] for 28-day and 0.67 [95% CI 0.64-0.70] for 1-year mortality). Application of CanICU in external data sets across the countries yielded better AUROC for 28-day mortality than SOFA (95% CI, 0.75-0.80 vs. 0.57-0.63 with SOFA in SMC; 95% CI, 0.72-0.79 vs. 0.64-0.72 with SOFA in MIMIC).Interpretation: CanICU oﬀers improved performance for predicting short- and long-term mortality in critically ill cancer patients admitted to ICU. A user-friendly online implementation is available and should be valuable for better mortality risk stratification. This might help physicians to determine to allocate ICU care for patients with cancer.Funding Information: This study was supported in part by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (No. 2019R1C1C1006709, 018R1A5A2025079, 2020M3F7A1094093), a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, the Republic of Korea (No. KHIDIHI19C1015010020), and was also supported by the ational R&D Program for Cancer Control through the National Cancer Center (NCC) funded by the Ministry of Health & Welfare, Republic of Korea (HA21C0065).Declaration of Interests: The authors have no potential conflicts of interest to disclose.Ethics Approval Statement: The institutional review boards of all participating hospitals approved this study and waived the requirement for informed consent because of the observational nature of the research. All patient records and data were anonymized and de-identified before analysis.
Government Speech, Distorted Science, and the First Amendment

Throughout the COVID-19 pandemic, Americans have had to confront an extraordinary speech phenomenon: an onslaught of misinformation and recurring lies from government officials, including the former President and his top health officials, about the pandemic. This phenomenon intersects in potentially novel ways with enduring questions about the regulation of government speech. Ordinarily, the government is free to articulate its own message to the exclusion of others. It can be pro-democracy or anti-tobacco without running afoul of the First Amendment. Whereas the requirement of content neutrality applies when the government polices the public speech of non-governmental actors, neither the government nor government officials are required to be neutral in their own messaging. Nor would we want them to be neutral regarding scientific and factual issues. Rather, we expect that government health agencies, such as the CDC or FDA and their officials, express only one side – the side best supported by science.In this sense, we have traditionally treated government speech relating to health, safety and scientific matters as a particular form of expert speech. The expectation of content neutrality also does not apply to non-governmental experts, such as physicians and other health professionals. They are expected to ground the information they offer in the best science available. But unlike government officials, they are subject to malpractice law when they are offering advice to particular patients and clients. Government speakers have traditionally faced no such consequences for giving bad advice to the public. The torts of public health malpractice or public official informational fraud are not recognized. That raises the critical question: should they be? Or, do First Amendment values demand that government speakers have free reign, even when they deliberately distort scientific information related to the health of the citizenry? In this Article, we engage with such questions. We begin by offering a typology that disaggregates speakers and types of speech through the lens of the range of misinformation that officials have offered during the COVID-19 pandemic. We distinguish among government speakers who echo experts, government experts who speak outside of their realm of expertise, and government speakers who lack expertise and issue official statements contra to expertise. We then explore the First Amendment’s relationship to government health misinformation, and consider whether private law should play a similar demarcation between protected and unprotected speech for government health officials as it does for privately practicing health professionals. We then argue that given the strong similarities between certain types of official health-related misinformation and professional speech, the legal regime that applies to the latter, more specifically malpractice law, provides a helpful model for thinking about and potentially policing the former.
MUSEUM PEDAGOGY IN THE CONTEXT OF ART EDUCATION

The article substantiates the features of the content and methods of work of the teacher in the context of museum pedagogy. In the conditions of dynamic social progress, the direction of museum pedagogy becomes more and more relevant and crystallizes into an independent type of art pedagogy, which is realized in close connection with the relevant institutions of culture and art. Now the basic principles, methodological approaches, separate technologies and methods of this direction of pedagogical science are substantiated. The experience of organizing and conducting museum classes for students of general secondary and higher education is revealed. Among the main forms of organization of work with pupils and students in museums, the most common are educational and thematic excursions (survey, differentiated), aimed at studying a certain section of the program or specific subject material, in accordance with the curriculum and competency requirements. Thus, the nature of the influence and interaction of museum pedagogy in school and university museums is determined by many factors. But two defining aspects remain obvious from the point of view of cultural and educational action. First of all, museum funds, expositions, museum environment must be formed on the basis of socially significant tasks, national interests, universal values, essentially influencing the cognitive, educational process. Secondly, an extremely important component in this process is the personality of the museologist, museum teacher.
Research on Influence Mechanism of Transmission Accuracy for Harmonic Drive Mechanism Considering Multi-factor Coupling

The influence mechanism of transmission accuracy for harmonic drive mechanism considering multi-factor coupling was studied. According to analysis of influence factors of transmission accuracy for harmonic drive mechanism, it was obtained that the transmission errors of harmonic drive mechanism include processing errors and installation errors. The transmission error is produced by eccentric vector, it directly affects the rotation angle of output shaft and it makes harmonic drive mechanism produce backlash. Then analyze the movement error caused by the rigid wheel machining error, the flexible wheel machining error, the assembly error of the rigid wheel and the flexible wheel, the wave generator component, and the comprehensive expression method of motion error generated by each error source was obtained. The performance test device of space drive mechanism was used to test, and the law of the transmission accuracy of harmonic drive mechanism with temperature, speed and assembly clearance was obtained. The test results show that the transmission accuracy of harmonic drive mechanism decreases with increasing temperature, and the speed has little effect on the transmission accuracy of harmonic drive mechanism; the assembly quality has a significant impact on harmonic drive accuracy.
Russian constructions with nu i in parallel corpora

The present paper describes a family of Russian constructions on the basis of parallel corpus data. We focus on the constructional pattern nu i X (literally: well and X). This pattern is a phrasal template with an abundance of instances. The two basic groups of nu i N consist of constructions expressing surprise, bewilderment or admiration, plus these with a negative nuance. Constructions in both groups have different degrees of idiomaticity ranging from full idioms to free word combinations. Another aspect of our investigation concerns the degree of language specificity of the Russian constructions under analysis. The higher the degree of language specificity, the more approximate is any way to translate a given unit into another language. Our analysis shows that the pattern nu i X is a language specific unit of the Russian constructicon, and that the degree of language specificity is more important for bilingual lexicography, teaching of foreign languages and translation studies that the degree of phraseologicity. For these contrastive purposes it is more effective to describe such units in terms of Construction Grammar. The empirical data have been collected from the corpus query system Sketch Engine, subcorpora of parallel texts OPUS2, and the Russian National Corpus (RNC), subcorpora of parallel texts.
Think about it! Improving defeasible reasoning by first modeling the question scenario

Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence. Existing cognitive science literature on defeasible reasoning suggests that a person forms a “mental model” of the problem scenario before answering questions. Our research goal asks whether neural models can similarly benefit from envisioning the question scenario before answering a defeasible query. Our approach is, given a question, to have a model first create a graph of relevant influences, and then leverage that graph as an additional input when answering the question. Our system, CURIOUS, achieves a new state-of-the-art on three different defeasible reasoning datasets. This result is significant as it illustrates that performance can be improved by guiding a system to “think about” a question and explicitly model the scenario, rather than answering reflexively.
Codificación de mensajes: Actividad de estudio e investigación utilizando praxeologías de álgebra lineal

This article is presented as part of the partial results of the design and implementation of a Study and Research Activity, for the teaching of message coding, as a practical case, with Linear Algebra Mathematical Organizations in engineering courses. At the methodological level, a qualitative, descriptive, and interpretive research was adopted, with ethnographic elements, and participant observation method. It is based on the Anthropological Theory of the Didactic, together with its theoretical and didactic constructs, a Praxeological Reference Model was designed, in which mathematical works related to the study of praxeologies, the generating question Q0 and their respective derived questions Qi present were described. in the development of the Study and Research Activity. The Study and Research Activity was developed with the emergence of various ways to encode and decode messages as it was studied and investigated to find possible answers to the generating questions and their derivatives. With them, a new way of teaching Mathematical Organizations of linear Algebra was evidenced, different from what is usually carried out in higher education in engineering schools. Finally, it is evident that taking this didactic device to the classroom allows to minimize to a great extent the monumentalization of knowledge, as it is conceived within the Anthropological Theory of Didactics
SPECTRA: Sparse Structured Text Rationalization

Selective rationalization aims to produce decisions along with rationales (e.g., text highlights or word alignments between two sentences). Commonly, rationales are modeled as stochastic binary masks, requiring sampling-based gradient estimators, which complicates training and requires careful hyperparameter tuning. Sparse attention mechanisms are a deterministic alternative, but they lack a way to regularize the rationale extraction (e.g., to control the sparsity of a text highlight or the number of alignments). In this paper, we present a unified framework for deterministic extraction of structured explanations via constrained inference on a factor graph, forming a differentiable layer. Our approach greatly eases training and rationale regularization, generally outperforming previous work on what comes to performance and plausibility of the extracted rationales. We further provide a comparative study of stochastic and deterministic methods for rationale extraction for classification and natural language inference tasks, jointly assessing their predictive power, quality of the explanations, and model variability.