Countering the Influence of Essay Length in Neural Essay Scoring

Previous work has shown that automated essay scoring systems, in particular machine learning-based systems, are not capable of assessing the quality of essays, but are relying on essay length, a factor irrelevant to writing proficiency. In this work, we first show that state-of-the-art systems, recent neural essay scoring systems, might be also influenced by the correlation between essay length and scores in a standard dataset. In our evaluation, a very simple neural model shows the state-of-the-art performance on the standard dataset. To consider essay content without taking essay length into account, we introduce a simple neural model assessing the similarity of content between an input essay and essays assigned different scores. This neural model achieves performance comparable to the state of the art on a standard dataset as well as on a second dataset. Our findings suggest that neural essay scoring systems should consider the characteristics of datasets to focus on text quality.

Transformer-based Multi-Task Learning for Adverse Effect Mention Analysis in Tweets

This paper presents our contribution to the Social Media Mining for Health Applications Shared Task 2021. We addressed all the three subtasks of Task 1: Subtask A (classification of tweets containing adverse effects), Subtask B (extraction of text spans containing adverse effects) and Subtask C (adverse effects resolution). We explored various pre-trained transformer-based language models and we focused on a multi-task training architecture. For the first subtask, we also applied adversarial augmentation techniques and we formed model ensembles in order to improve the robustness of the prediction. Our system ranked first at Subtask B with 0.51 F1 score, 0.514 precision and 0.514 recall. For Subtask A we obtained 0.44 F1 score, 0.49 precision and 0.39 recall and for Subtask C we obtained 0.16 F1 score with 0.16 precision and 0.17 recall.

Text Augmentation Techniques in Drug Adverse Effect Detection Task

The paper researches the problem of drug adverse effect detection in texts of social media. We describe the development of such classification system for Russian tweets. To increase the train dataset we apply a couple of augmentation techniques and analyze their effect in comparison with similar systems presented at 2021 years' SMM4H Workshop.

Assessing Cognitive Linguistic Influences in the Assignment of Blame

Lab studies in cognition and the psychology of morality have proposed some thematic and linguistic factors that influence moral reasoning. This paper assesses how well the findings of these studies generalize to a large corpus of over 22,000 descriptions of fraught situations posted to a dedicated forum. At this social-media site, users judge whether or not an author is in the wrong with respect to the event that the author described. We find that, consistent with lab studies, there are statistically significant differences in uses of first-person passive voice, as well as first-person agents and patients, between descriptions of situations that receive different blame judgments. These features also aid performance in the task of predicting the eventual collective verdicts.

Sifting French Tweets to Investigate the Impact of Covid-19 in Triggering Intense Anxiety

Sifting French Tweets to Investigate the Impact of Covid-19 in Triggering Intense Anxiety. Social media can be leveraged to understand public sentiment and feelings in real-time, and target public health messages based on user interests and emotions. In this paper, we investigate the impact of the COVID-19 pandemic in triggering intense anxiety, relying on messages exchanged on Twitter. More specifically, we provide : i) a quantitative and qualitative analysis of a corpus of tweets in French related to coronavirus, and ii) a pipeline approach (a filtering mechanism followed by Neural Network methods) to satisfactory classify messages expressing intense anxiety on social media, considering the role played by emotions.

Model Compression for Domain Adaptation through Causal Effect Estimation

Abstract Recent improvements in the predictive quality of natural language processing systems are often dependent on a substantial increase in the number of model parameters. This has led to various attempts of compressing such models, but existing methods have not considered the differences in the predictive power of various model components or in the generalizability of the compressed models. To understand the connection between model compression and out-of-distribution generalization, we define the task of compressing language representation models such that they perform best in a domain adaptation setting. We choose to address this problem from a causal perspective, attempting to estimate the average treatment effect (ATE) of a model component, such as a single layer, on the model's predictions. Our proposed ATE-guided Model Compression scheme (AMoC), generates many model candidates, differing by the model components that were removed. Then, we select the best candidate through a stepwise regression model that utilizes the ATE to predict the expected performance on the target domain. AMoC outperforms strong baselines on dozens of domain pairs across three text classification and sequence tagging tasks.1

Role of Transformational Leadership in the Development of Administrative Creativity A Field Study on Syrian Commercial Banks in Hama

This research aimed to study the role of Transformational Leadership Style in Administrative Creativity on Syrian Commercial Banks in Hama. By identifying the role of transformational leadership dimensions in developing creative abilities for administrative cadres working in the banks under study. In order to achieve the goals of this study, the researchers used the deductive approach. The study used a comprehensive inventory method for workers at the administrative (middle-lower) levels, consisting of (123). The researcher used the questionnaire as a main tool to collect data. The study concluded that there is a significant correlation between transformational leadership in its dimensions (ideal effect, inspirational motivation, intellectual stimulation, individualized consideration) and the development of Administrative Creativity of the employees in the banks under study. One of the most important recommendations was to be more concerned with the application of transformational leadership practices and behaviors.

Contribution to study the biochemical effects (Allelepathy) of A ( Euphorbia paralias L.) in germination and growth seedlings of some herbal plants

Laboratory experiments were conducted to study the effect of the aqueou extract of the vegetative parts of the Euphorbia paralias at different concentrations (2%,4%,8%) in seed germination and seeding growth of the Lepidium sativum, Lactuca sativa and Portulaca oleracea. The results indicated increase in germination ratio of Portulaca oleracea. up to 7%. while the effect of the extract was not evident in the germination of the seeds of Lepidium sativum, and decreased the germination ratio of Lactuca sativa seeds by 35.9% at the concentration of 8%.In contrast, the extracts stimulated the length of the stalks of Portulaca oleracea , Lepidium sativum and Lactuca sativa plants at all used concentrations used. The highest rate of stimulated stalks legnth of Lepidium sativum at 2% was 58.2%. inhibitory effect of water extract observed for root length of Portulaca oleracea and Lactuca sativa plants at 2% , and stimulation effect pour Lepidium sativum, The effect became evident at the concentrations of 4% and 8% for the three plants. The most sensitive and affected plants was Portulaca oleracea, where the length of the root decreased by 53.38% at the concentration of 4% and by 72% at the concentration of 8% ,while the length of the root Lepidium sativum and Lactuca sativa decreased by 25.82% and 34.95% was decreased for 4% and 8%respectively. The results suggested that this weed may affect seedling growth, due to inhibitory or stimulatory effect of Allelochemicals , which present in water extract of this weed . and may used as potential Bioherbicide after further experiments.

Expression between Arabic and Semitic languages

This paper deals with the link between Semitic and Arabic languages,that it showed the ancientness of the Arabic , and explained the concept of Semitic languages, and mentioned the most important views in their home country and their common characteristics. The research then presented the historical understanding of the Arab- Semitic relationship, confirming the interaction that emerged in the pre-Islamic era through interaction with the Bible, which was translated into Arabic Whether the Arabs are translators or were the people of these heavenly books; the result proves the principle of interaction between languages that these books were written with and the Arabic language, with the continuation of this interaction clearly in the era of the Prophet and his companions through various forms and ways . The research then discusses the phenomenon of expression, then the ancientness of the phenomenon of expression in Semitic languages in general, and Arabic in particular, through the presentation of the most important proof of authenticity in these languages, Although the Arabic is the most conservative. The research ended with conclusions and recommendations.

Modeling of sloshing in moving cylindrical containers by using ANSYS program

This research is concerned in modeling the problem of sloshing in moving cylindrical containers in ANSYS program where we model the problem on a partially filled cylinder then we find the resonant frequencies in addition to study the interaction between the cylinder and the fluid.