Miracle-dz/UniFAR-FTUs huggingface dataset:
How Mental Health Program Moderate Remote Audit, Computer Literacy and CAATs Impact on Auditor Work Performance during Pandemic Covid-19

The stress level of working in a public accounting firm is no longer a secret, this happens all over the world, including in Indonesia. The stress level is very high, especially during peak season. Many public accounting firm employees, namely auditors who are not strong and resign, so that the turnover rate is very high. This is still exacerbated by the pandemic of Covid-19, which requires the auditor's work pace to change. What previously worked onsite became online. The use of remote auditing and computer assisted audit techniques is being increased. However, without adequate computer literacy, of course this will make it more difficult for auditors and become a new cause of stress. Researchers are interested in examining the use of mental health programs as moderating variables to moderate the effect of remote audit, computer literacy and computer assisted audit techniques on auditor work performance during the Covid-19 pandemic. Our research is a quantitative research, the data that is processed is primary data. The survey results from the object of our research, namely the auditor at a public accounting firm in Jakarta. Processing the data using statistical software and also the Sobel test to test the moderating effect. We get the results that remote audit, computer literacy and computer assisted audit techniques have a significant effect on auditor work performance during Covid-19 and mental health programs moderate the effect of these three variables.
Developing an Internationalisation Process through Digital Transformation: An Evidence in British MSMEs

This paper gathered British MSMEs data if they are using the appropriate internationalisation theories and DTs as an independent variable for internationalising. This paper also includes criticism concerning internationalisation theories, DTs usage, and how the deployment of DTs might assist these theories as moderators and their relevance to addressing the MSMEs issue amid the COVID-19 epidemic. The dynamic capability is recognised to help MSMEs operate more effectively because it allows them to specialise more goods and readily adjust to changing company demands, and it can help MSMEs internationalise 77 times more than those that do not use this mechanism. Website and electronic sharing use, also known as DTs adoption, may impact MSMEs to internationalise 1750 and 49 times, respectively, since they enable MSMEs to reach a larger region, establish new sales channels, and simplify knowledge transfer to develop for new products. The existence of DTs as internationalisation theories moderators, on the other hand, produces distinct effects. Resource internalisation can benefit MSMEs’ export if paired with a website, online selling, and electronic sharing, albeit only online selling, has a significant influence. However, the dynamic capability, which is proven, can assist MSMEs export individually, but it is less effective in supporting export when combined with DTs since only online selling fits it to support export. In contrast to its partnership with resource internalisation, moderation has little influence on exports.
Assessment of the TREELIM model in predicting present treeline along a longitudinal continentality-maritimity gradient in south-western Norway

ABSTRACT Treelines driven by climatic conditions are characteristic of alpine areas. This study assesses the degree of accuracy with which the TREELIM model predicts present treeline elevation along a regional continentality-maritimity gradient in southwest Norway. The study area consists of 15 sites chosen along this gradient, wherein observed treelines range from nearly sea level to over 1200 m a.s. l. The TREELIM model calculates treeline position based solely on meteorological parameters obtained from gridded climate datasets. The model assumes that tree growth occurs if the length of the growing season (LGS) exceeds a minimum duration and the seasonal mean temperature (SMT) does not fall below a certain value. Six different scenarios were developed in order to determine the effects of changing precipitation and drought within TREELIM. Within these scenarios, three different SMTs were used in order to model the treeline position. Model performance was evaluated by comparing resultant treeline elevation with observed values. Results of experiments using lapse rate-based temperature input data were compared to those using the gridded climate data. Statistical analysis determined that the lapse rate-based temperature scenario removing drought constraints and using a SMT of 6.4°C gave the most accurate prediction of treeline position, and a relatively uniform growing season ranging from 110 to 128 days. Precipitation and snow cover do not significantly affect treeline elevation. The results suggest that TREELIM accurately predicts the treeline in most cases, but is less suited in areas with steeply varying topography.
Fatores do pré-natal e do puerpério que interferem na autoeficácia em amamentação

O objetivo deste estudo foi analisar quais os fatores relacionados ao pre-natal e ao puerperio que interferem na autoeficacia em amamentacao para as puerperas internadas em alojamento conjunto. Metodos: Pesquisa quantitativa, transversal, realizada com 322 puerperas, no periodo de dezembro de 2011 a marco de 2012, com um instrumento composto pela Breastfeeding Self-Efficacy Scale-Short Form e um formulario para caracterizacao das puerperas. A analise bivariada foi efetuada no software Statistical Package for Social Science. Resultados: A maioria das puerperas apresentou alta autoeficacia na amamentacao. Ocorreu associacao estatisticamente significante da autoeficacia na amamentacao com o fato de o bebe ter sido colocado para sugar apos a primeira hora. Conclusao: A promocao da autoeficacia materna em amamentar deve ter inicio no pre-natal, ser estimulada precocemente na maternidade e acompanhada durante o puerperio e puericultura.
Thurka Sangaramoorthy and Karen A. Kroeger, Rapid Ethnographic Assessments: A Practical Approach and Toolkit for Collaborative Community Research

, Routledge: London, 2020;178 pp., ISBN 978-0-367-25228-1, €120 (hbk) The COVID-19 pandemic - while introducing an array of profound challenges to once stable social institutions - highlights the urgent need for the development of rapid forms of social research techniques to comprehend such events and, most critically, to guide effective intervention. Sangaramoothy and Kroeger, as part of their respective roles in the CDC, have worked together in developing, planning and carrying out several important REAs. They have also delivered trainings on REA to CDC staff, state and local health department personnel, and practitioners in non-governmental and community-based organisations, which has culminated in the practical focus of the book and its core community-engagement dimension. [Extracted from the article] Copyright of Irish Journal of Sociology is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
Choose Your Lenses: Flaws in Gender Bias Evaluation

Considerable efforts to measure and mitigate gender bias in recent years have led to the introduction of an abundance of tasks, datasets, and metrics used in this vein. In this position paper, we assess the current paradigm of gender bias evaluation and identify several flaws in it. First, we highlight the importance of extrinsic bias metrics that measure how a model’s performance on some task is affected by gender, as opposed to intrinsic evaluations of model representations, which are less strongly connected to specific harms to people interacting with systems. We find that only a few extrinsic metrics are measured in most studies, although more can be measured. Second, we find that datasets and metrics are often coupled, and discuss how their coupling hinders the ability to obtain reliable conclusions, and how one may decouple them. We then investigate how the choice of the dataset and its composition, as well as the choice of the metric, affect bias measurement, finding significant variations across each of them. Finally, we propose several guidelines for more reliable gender bias evaluation.
On the Dynamics of Gender Learning in Speech Translation

Due to the complexity of bias and the opaque nature of current neural approaches, there is a rising interest in auditing language technologies. In this work, we contribute to such a line of inquiry by exploring the emergence of gender bias in Speech Translation (ST). As a new perspective, rather than focusing on the final systems only, we examine their evolution over the course of training. In this way, we are able to account for different variables related to the learning dynamics of gender translation, and investigate when and how gender divides emerge in ST. Accordingly, for three language pairs (en ? es, fr, it) we compare how ST systems behave for masculine and feminine translation at several levels of granularity. We find that masculine and feminine curves are dissimilar, with the feminine one being characterized by more erratic behaviour and late improvements over the course of training. Also, depending on the considered phenomena, their learning trends can be either antiphase or parallel. Overall, we show how such a progressive analysis can inform on the reliability and time-wise acquisition of gender, which is concealed by static evaluations and standard metrics.
HeiMorph at SIGMORPHON 2022 Shared Task on Morphological Acquisition Trajectories

This paper presents the submission by the HeiMorph team to the SIGMORPHON 2022 task 2 of Morphological Acquisition Trajectories. Across all experimental conditions, we have found no evidence for the so-called Ushaped development trajectory. Our submitted systems achieve an average test accuracies of 55.5% on Arabic, 67% on German and 73.38% on English. We found that, bigram hallucination provides better inferences only for English and Arabic and only when the number of hallucinations remains low.
AnaLog: Testing Analytical and Deductive Logic Learnability in Language Models

We investigate the extent to which pre-trained language models acquire analytical and deductive logical reasoning capabilities as a side effect of learning word prediction. We present AnaLog, a natural language inference task designed to probe models for these capabilities, controlling for different invalid heuristics the models may adopt instead of learning the desired generalisations. We test four languagemodels on AnaLog, finding that they have all learned, to a different extent, to encode information that is predictive of entailment beyond shallow heuristics such as lexical overlap and grammaticality. We closely analyse the best performing language model and show that while it performs more consistently than other language models across logical connectives and reasoning domains, it still is sensitive to lexical and syntactic variations in the realisation of logical statements.
Domain Confused Contrastive Learning for Unsupervised Domain Adaptation

In this work, we study Unsupervised Domain Adaptation (UDA) in a challenging self-supervised approach. One of the difficulties is how to learn task discrimination in the absence of target labels. Unlike previous literature which directly aligns cross-domain distributions or leverages reverse gradient, we propose Domain Confused Contrastive Learning (DCCL), which can bridge the source and target domains via domain puzzles, and retain discriminative representations after adaptation. Technically, DCCL searches for a most domain-challenging direction and exquisitely crafts domain confused augmentations as positive pairs, then it contrastively encourages the model to pull representations towards the other domain, thus learning more stable and effective domain invariances. We also investigate whether contrastive learning necessarily helps with UDA when performing other data augmentations. Extensive experiments demonstrate that DCCL significantly outperforms baselines, further ablation study and analysis also show the effectiveness and availability of DCCL.
Multi-Relational Graph Transformer for Automatic Short Answer Grading

The recent transition to the online educational domain has increased the need for Automatic Short Answer Grading (ASAG). ASAG automatically evaluates a student’s response against a (given) correct response and thus has been a prevalent semantic matching task. Most existing methods utilize sequential context to compare two sentences and ignore the structural context of the sentence; therefore, these methods may not result in the desired performance. In this paper, we overcome this problem by proposing a Multi-Relational Graph Transformer, MitiGaTe, to prepare token representations considering the structural context. Abstract Meaning Representation (AMR) graph is created by parsing the text response and then segregated into multiple subgraphs, each corresponding to a particular relationship in AMR. A Graph Transformer is used to prepare relation-specific token embeddings within each subgraph, then aggregated to obtain a subgraph representation. Finally, we compare the correct answer and the student response subgraph representations to yield a final score. Experimental results on Mohler’s dataset show that our system outperforms the existing state-of-the-art methods. We have released our implementation https://github.com/kvarun07/asag-gt, as we believe that our model can be useful for many future applications.
Structural information in mathematical formulas for exercise difficulty prediction: a comparison of NLP representations

To tailor a learning system to the student’s level and needs, we must consider the characteristics of the learning content, such as its difficulty. While natural language processing allows us to represent text efficiently, the meaningful representation of mathematical formulas in an educational context is still understudied. This paper adopts structural embeddings as a possible way to bridge this gap. Our experiments validate the approach using publicly available datasets to show that incorporating syntactic information can improve performance in predicting the exercise difficulty.
CardiffNLP-Metaphor at SemEval-2022 Task 2: Targeted Fine-tuning of Transformer-based Language Models for Idiomaticity Detection

This paper describes the experiments ran for SemEval-2022 Task 2, subtask A, zero-shot and one-shot settings for idiomaticity detection. Our main approach is based on fine-tuning transformer-based language models as a baseline to perform binary classification. Our system, CardiffNLP-Metaphor, ranked 8th and 7th (respectively on zero- and one-shot settings on this task. Our main contribution lies in the extensive evaluation of transformer-based language models and various configurations, showing, among others, the potential of large multilingual models over base monolingual models. Moreover, we analyse the impact of various input parameters, which offer interesting insights on how language models work in practice.
Exposing the Limits of Video-Text Models through Contrast Sets

Recent video-text models can retrieve relevant videos based on text with a high accuracy, but to what extent do they comprehend the semantics of the text? Can they discriminate between similar entities and actions? To answer this, we propose an evaluation framework that probes video-text models with hard negatives. We automatically build contrast sets, where true textual descriptions are manipulated in ways that change their semantics while maintaining plausibility. Specifically, we leverage a pre-trained language model and a set of heuristics to create verb and person entity focused contrast sets. We apply these in the multiple choice video to-text classification setting. We test the robustness of recent methods on the proposed automatic contrast sets, and compare them to additionally collected human-generated counterparts, to assess their effectiveness. We see that model performance suffers across all methods, erasing the gap between recent CLIP-based methods vs. the earlier methods.
On Gender Biases in Offensive Language Classification Models

We explore whether neural Natural Language Processing models trained to identify offensive language in tweets contain gender biases. We add historically gendered and gender ambiguous American names to an existing offensive language evaluation set to determine whether models? predictions are sensitive or robust to gendered names. While we see some evidence that these models might be prone to biased stereotypes that men use more offensive language than women, our results indicate that these models? binary predictions might not greatly change based upon gendered names.
Sliced at SemEval-2022 Task 11: Bigger, Better? Massively Multilingual LMs for Multilingual Complex NER on an Academic GPU Budget

Massively multilingual language models (MMLMs) have become a widely-used representation method, and multiple large MMLMs were proposed in recent years. A trend is to train MMLMs on larger text corpora or with more layers. In this paper we set out to test recent popular MMLMs on detecting semantically ambiguous and complex named entities with an academic GPU budget. Our submission of a single model for 11 languages on the SemEval Task 11 MultiCoNER shows that a vanilla transformer-CRF with XLM-R_{large} outperforms the more recent RemBERT, ranking 9th from 26 submissions in the multilingual track. Compared to RemBERT, the XLM-R model has the additional advantage to fit on a slice of a multi-instance GPU. As contrary to expectations and recent findings, we found RemBERT to not be the best MMLM, we further set out to investigate this discrepancy with additional experiments on multilingual Wikipedia NER data. While we expected RemBERT to have an edge on that dataset as it is closer to its pre-training data, surprisingly, our results show that this is not the case, suggesting that text domain match does not explain the discrepancy.
Improving Neural Machine Translation with the Abstract Meaning Representation by Combining Graph and Sequence Transformers

Previous studies have shown that the Abstract Meaning Representation (AMR) can improve Neural Machine Translation (NMT). However, there has been little work investigating incorporating AMR graphs into Transformer models. In this work, we propose a novel encoder-decoder architecture which augments the Transformer model with a Heterogeneous Graph Transformer (Yao et al., 2020) which encodes source sentence AMR graphs. Experimental results demonstrate the proposed model outperforms the Transformer model and previous non-Transformer based models on two different language pairs in both the high resource setting and low resource setting. Our source code, training corpus and released models are available at https://github.com/jlab-nlp/amr-nmt.
Crossroads, Buildings and Neighborhoods: A Dataset for Fine-grained Location Recognition

General domain Named Entity Recognition (NER) datasets like CoNLL-2003 mostly annotate coarse-grained location entities such as a country or a city. But many applications require identifying fine-grained locations from texts and mapping them precisely to geographic sites, e.g., a crossroad, an apartment building, or a grocery store. In this paper, we introduce a new dataset HarveyNER with fine-grained locations annotated in tweets. This dataset presents unique challenges and characterizes many complex and long location mentions in informal descriptions. We built strong baseline models using Curriculum Learning and experimented with different heuristic curricula to better recognize difficult location mentions. Experimental results show that the simple curricula can improve the system’s performance on hard cases and its overall performance, and outperform several other baseline systems. The dataset and the baseline models can be found at https://github.com/brickee/HarveyNER.
Sentence-Level Resampling for Named Entity Recognition

As a fundamental task in natural language processing, named entity recognition (NER) aims to locate and classify named entities in unstructured text. However, named entities are always the minority among all tokens in the text. This data imbalance problem presents a challenge to machine learning models as their learning objective is usually dominated by the majority of non-entity tokens. To alleviate data imbalance, we propose a set of sentence-level resampling methods where the importance of each training sentence is computed based on its tokens and entities. We study the generalizability of these resampling methods on a wide variety of NER models (CRF, Bi-LSTM, and BERT) across corpora from diverse domains (general, social, and medical texts). Extensive experiments show that the proposed methods improve span-level macro F1-scores of the evaluated NER models on multiple corpora, frequently outperforming sub-sentence-level resampling, data augmentation, and special loss functions such as focal and Dice loss.
MT-Speech at SemEval-2022 Task 10: Incorporating Data Augmentation and Auxiliary Task with Cross-Lingual Pretrained Language Model for Structured Sentiment Analysis

Sentiment analysis is a fundamental task, and structure sentiment analysis (SSA) is an important component of sentiment analysis. However, traditional SSA is suffering from some important issues: (1) lack of interactive knowledge of different languages; (2) small amount of annotation data or even no annotation data. To address the above problems, we incorporate data augment and auxiliary tasks within a cross-lingual pretrained language model into SSA. Specifically, we employ XLM-Roberta to enhance mutually interactive information when parallel data is available in the pretraining stage. Furthermore, we leverage two data augment strategies and auxiliary tasks to improve the performance on few-label data and zero-shot cross-lingual settings. Experiments demonstrate the effectiveness of our models. Our models rank first on the cross-lingual sub-task and rank second on the monolingual sub-task of SemEval-2022 task 10.
Towards an open-domain chatbot for language practice

State-of-the-art chatbots for English are now able to hold conversations on virtually any topic (e.g. Adiwardana et al., 2020; Roller et al., 2021). However, existing dialogue systems in the language learning domain still use hand-crafted rules and pattern matching, and are much more limited in scope. In this paper, we make an initial foray into adapting open-domain dialogue generation for second language learning. We propose and implement decoding strategies that can adjust the difficulty level of the chatbot according to the learner’s needs, without requiring further training of the chatbot. These strategies are then evaluated using judgements from human examiners trained in language education. Our results show that re-ranking candidate outputs is a particularly effective strategy, and performance can be further improved by adding sub-token penalties and filtering.