Reviewerly/exHarmony huggingface dataset:
MOTIVATION PREDICTORS IN TEACHING MUSIC CULTURE

Music Culture is one of the subjects for which many students are not particularly motivated, thus the interest of this research was establishing possible factors that are in positive correlation with motivation for this subject. Major problem of this research was to determine correlation of certain variables with intrinsic and extrinsic motivation for Music Culture teaching, as well as what are the best predictors of this motivation. The differences between the students show that intrinsic motivation is higher with female respondents (F (1.311) = 5.39, p .05), that it declines with age (F (2.313) = 11.97, p .01) and that it is higher in classes where democratic educational style is applied (r = .274). Only the scale of learning presented appeared as a significant predictor of intrinsic motivation (β = .513, p .001), while the scale of self-esteem (β = .316; p .001) and sub-scale of performance goals focused on achieving success (β = .388; p .001) appeared as significant predictors of extrinsic motivation. A positive correlation was also noticed between singing activity during the lesson and intrinsic motivation and between listening to music and extrinsic motivation as well as negative correlation regarding presentations and intrinsic motivation.
An Aerial Image Dehazing Algorithm Using a Prior-Based Dense Attentive Network

&lt;p&gt;To address the problem that the acquired images tend to degrade in clarity and fidelity in aerial hazy conditions to the extent that the target is difficult to detect, this paper proposes an aerial image dehazing algorithm using a prior-based dense attentive network. The network is based on dense blocks and attention blocks with an encoder-decoder structure, which can directly learn the mapping between the input image and the corresponding haze-free image without relying on the traditional atmospheric scattering model. In addition, to better handle inhomogeneous hazy images, the initial fuzzy density map is first extracted from the original hazy images and then used as a common input to the network together with the original hazy images. Finally, this paper synthesizes a large -scale aerial image dehazing dataset containing two subsets of uniform and non -uniform images. The experimental results and data analysis show that the proposed method exhibits better performance of dehazing with other algorithms on both synthetic and real images.&lt;/p&gt;
Web-based information system design of Graveyard licensing Administration in Grogol Selatan sub-district

: The role of service is very important in the current era of globalization, especially in government agencies engaged in services which will be needed by the applicant or the community. With time constraints and the impact of the Covid pandemic which has closed access to direct communication, it will make it difficult for some applicants or the public to apply for permits. The purpose of this research is to build a website that helps the One Stop Integrated Service (PTSP), so that applicants can apply online as one of the government's main functions in efforts to fulfill applications in the field of licensing services. Governor Regulation No. 47 of 2017 concerning instructions for the Implementation of One Stop Integrated Services (PTSP) for submitting applications for the issuance of Graveyard Land Use Permits (IPTM). This research is based on the waterfall software development method and data collection techniques with the results of observing the Web-Based Information System of Graveyard Licensing Administration in Grogol Selatan Sub-District
Іnformation threat. Combating n-type information threats.

An information threat is any activity aimed at violating the integrity, confidentiality, or availability of information in order to harm an individual, organization, or state. In the context of information warfare, information threats can be used to discredit opponents, reduce public trust in certain individuals or organizations, or even interfere with electoral processes. Information threats in information warfare can take various forms, for example: Fake news and information: dissemination of false or speculative information to influence public opinion. Hacker attacks: remote hacking of computer systems to steal sensitive information. Social engineering: abuse of trust in people to obtain confidential information. Phishing: sending emails that appear to be from well-known Internet services in order to gain access to confidential information. Spam: Sending a large number of unnecessary messages, which can affect network bandwidth and cause a denial of service. To combat information threats in the context of information warfare, it is necessary to develop effective security measures. Information threats can spread in society at different levels and through different channels. Here are some of them: Social media: Social media have become the most common means of disseminating information in the modern world. Most information threats are spread through social media, where fake news, speculation, and propaganda can be disseminated. Emails and messages: Information threats can be spread through emails and messages that contain spam, phishing emails, viruses, and other harmful materials. Media: Traditional media, such as newspapers, radio, and television, can also dis- seminate information threats through their channels. Pyramid schemes: Pyramid schemes can be used to spread information threats, such as get-rich schemes and other types of fraud. The Internet: The Internet has become the most widely used means of communication in the world. Information threats can be disseminated through websites, forums, chat rooms, and other online resources. Thus, the need and relevance of studying the spread and perception of information threats at the current stage of development do not require additional proof.
FORMATION OF A CULTURE OF COMMUNICATION AMONG STUDENTS THROUGH NATIONAL EDUCATIONAL VALUES

Ұлттық құндылықтарға негізделген қарым-қатынас мәдениеті білім алушылардың саналы көзқарасын, ізгілікті қарым-қатынас орнатуына, рухани-адамгершілік құндылықтарының қалыптасуына, жүйелі іс-әрекет жасауына мүмкіндік береді. Ұлттық тәрбие құндылықтары негізінде білім алушылардың қарым-қатынас мәдениетін қалыптастыру – кәсіби даярлықтың аса маңызды бөлігі, сондықтан оны жоғары оқу орнындағы тұтас педагогшикалық үдерісте мақсатты түрде жүзеге асыру қажет. Қарым-қатынас мәдениетіндегі осындай көзқарастар кезеңінде зерттеулердің келесі қисынды кезеңі үдерісте қарым-қатынас мәдениетін дамыту болып табылады. Сондықтан, біз білім алушылардың қарым-қатынас мәдениетін ұлттық тәрбие құндылықтары негізінде қалыптасттыру жағдайын қарастырамыз. Білім алушылардың қарым-қатынас мәдениетін қалыптасттыру - бұл педагогикалық үдеріс. Олар қызметте бірлескен, ынтымақтастықпен, оның шығармашылығымен, мәдени мазмұнмен, қарым-қатынас ережелерін игеру әдістерімен сипатталады, ортада тұлғаның өзін-өзі дамытуына ықпал етеді. Білім беру үдерісінде тұлғалардың іс-әрекетіне бағытталған педагогикалық міндет білім алушылардың бойында ізгілік, құрмет көрсетуге, ұйымшылдықты, еңбексүйгіштікті, адамдық қадір-қасиетін тәрбиелеу болып табылады. Ұлттық құндылықтарға негізделген қарым-қатынас мәдениеті білім алушылардың саналы көзқарасын, ізгілікті қарым-қатынас орнатуына, рухани-адамгершілік құндылықтарының қалыптасуына, жүйелі іс-әрекет жасауына мүмкіндік береді. Ұлттық құндылықтарға негізделген қарым-қатынас – бұл жолдастық, достық пен ұлттық pyxты, сананы, сүйіспеншілік, адамгершілік табысқа жету және т.б адами қатынастардың құрудағы өзара әрекеттесуді қамтитын адами қатынастардың күрделі түрі. Жеке тұлға қарым-қатынас процесінде біреуге ұқсау, үлгі ету, өнеге алу, үйрену, ортақ сезімдес, сәйкестендіру арқылы қоғамдық және жеке сананы, эмоцияны, сезімді, мінез-құлық нормаларын көрсетудің формаларын меңгереді.
THEORETICAL AND TERMINOLOGICAL ASPECTS IN CONCEPTUALIZING METHODICS OF EDUCATIONAL WORK AS A PEDAGOGICAL DISCIPLINE

The importance of pedagogy as a science is in exploration and improvement of education as an integral part of the social reality. Theory of education, General pedagogy and Methodics of educational work are the scientific disciplines that are most directly related to examining the educational work, and represent its theoretical and methodological basis in the process of its realization. Educational work provides the essence of the overall education process, since it refers and unites the knowledge, skills, attitudes, beliefs and values of the individual, and its results are seen in developing student's unique personality. In this paper the focus is on the Methodics of educational work, as a pedagogical discipline which is most closely related to the process of realization of educational work. The aim is through analysis of the basic theoretical and terminological aspects to emphasize its role in the overall education process. Taking into consideration the complexity of educational work and difficulties related to the process of assessment of the outcomes, application of the taxonomy of the affective domain is suggested as a possible approach. Also, the importance of some topics that are particularly relevant in pedagogy is emphasized, but which, due to terminological inconsistencies, are not sufficiently recognized as part of the Methodics of educational work.
Long-form Simultaneous Speech Translation: Thesis Proposal

Peter Polák. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: Student Research Workshop. 2023.
Style-sensitive Sentence Embeddings for Evaluating Similarity in Speech Style of Japanese Sentences by Contrastive Learning

Yuki Zenimoto, Shinzan Komata, Takehito Utsuro. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: Student Research Workshop. 2023.
SQUARE: Automatic Question Answering Evaluation using Multiple Positive and Negative References

Matteo Gabburo, Siddhant Garg, Rik Koncel-Kedziorski, Alessandro Moschitti. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers). 2023.
Incorporating Singletons and Mention-based Features in Coreference Resolution via Multi-task Learning for Better Generalization

Yilun Zhu, Siyao Peng, Sameer Pradhan, Amir Zeldes. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers). 2023.
Attacking Open-domain Question Answering by Injecting Misinformation

Liangming Pan, Wenhu Chen, Min-Yen Kan, William Yang Wang. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
The Eval4NLP 2023 Shared Task on Prompting Large Language Models as Explainable Metrics

Generative large language models (LLMs) have seen many breakthroughs over the last year. With an increasing number of parameters and pre-training data, they have shown remarkable capabilities to solve tasks with minimal or no task-related examples. Notably, LLMs have been successfully employed as evaluation metrics in text generation tasks. Strategies employed in this context differ in the choice of input prompts, the selection of samples for demonstration, and the methodology used to construct scores grading the generations. Approaches often differ in the input prompts, the samples that are selected for demonstration and the construction process of scores from the output. Within this context, we introduce the Eval4NLP 2023 shared task that asks participants to explore such approaches for machine translation evaluation and summarization eval- uation. Specifically, we select a list of allowed LLMs and disallow fine-tuning to ensure a focus on prompting. We test the approaches of the participants on a new reference-free test-set spanning 3 language pairs for machine transla- tion as well as a summarization dataset. Further, we present an overview of the approaches taken by the participants, present their results on the test set and analyze paths for future work. Fi- nally, as a separate track, we perform a human evaluation of the plausibility of explanations given by the LLMs and its effect on model performance. We make parts of our code and datasets available.
An Investigation of Warning Erroneous Chat Translations in Cross-lingual Communication

Yunmeng Li, Jun Suzuki, Makoto Morishita, Kaori Abe, Kentaro Inui. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: Student Research Workshop. 2023.
Reference-Free Summarization Evaluation with Large Language Models

With the continuous advancement in unsupervised learning methodologies, text generation has become increasingly pervasive. However, the evaluation of the quality of the generated text remains challenging. Human annotations are expensive and often show high levels of disagreement, in particular for certain tasks characterized by inherent subjectivity, such as translation and summarization.Consequently, the demand for automated metrics that can reliably assess the quality of such generative systems and their outputs has grown more pronounced than ever. In 2023, Eval4NLP organized a shared task dedicated to the automatic evaluation of outputs from two specific categories of generative systems: machine translation and summarization. This evaluation was achieved through the utilization of prompts with Large Language Models. Participating in the summarization evaluation track, we propose an approach that involves prompting LLMs to evaluate six different latent dimensions of summarization quality. In contrast to many previous approaches to summarization assessments, which emphasize lexical overlap with reference text, this method surfaces the importance of correct syntax in summarization evaluation. Our method resulted in the second-highest performance in this shared task, demonstrating its effectiveness as a reference-free evaluation.
Controllable Discovery of Intents: Incremental Deep Clustering Using Semi-Supervised Contrastive Learning

Mrinal Rawat, Hithesh Sankararaman, Victor Barres. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
PACT: Pretraining with Adversarial Contrastive Learning for Text Classification

Md Tawkat Islam Khondaker, Muhammad Abdul-Mageed, Laks Lakshmanan, V.S.. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
Understanding Large Language Model Based Metrics for Text Summarization

This paper compares the two most widely used techniques for evaluating generative tasks with large language models (LLMs): prompt-based evaluation and log-likelihood evaluation as part of the Eval4NLP shared task. We focus on the summarization task and evaluate both small and large LLM models. We also study the impact of LLAMA and LLAMA 2 on summarization, using the same set of prompts and techniques. We used the Eval4NLP dataset for our comparison. This study provides evidence of the advantages of prompt-based evaluation techniques over log-likelihood based techniques, especially for large models and models with better reasoning power.
Implicit Affordance Acquisition via Causal Action–Effect Modeling in the Video Domain

Hsiu-Yu Yang, Carina Silberer. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
Do the Benefits of Joint Models for Relation Extraction Extend to Document-level Tasks?

Pratik Saini, Tapas Nayak, Indrajit Bhattacharya. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers). 2023.
Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval

Nengzheng Jin, Dongfang Li, Junying Chen, Joanna Siebert, Qingcai Chen. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers). 2023.
KoBigBird-large: Transformation of Transformer for Korean Language Understanding

Kisu Yang. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
Exploring Prompting Large Language Models as Explainable Metrics

This paper describes the IUST NLP Lab submission to the Prompting Large Language Models as Explainable Metrics Shared Task at the Eval4NLP 2023 Workshop on Evaluation & Comparison of NLP Systems. We have proposed a zero-shot prompt-based strategy for explainable evaluation of the summarization task using Large Language Models (LLMs). The conducted experiments demonstrate the promising potential of LLMs as evaluation metrics in Natural Language Processing (NLP), particularly in the field of summarization. Both few-shot and zero-shot approaches are employed in these experiments. The performance of our best provided prompts achieved a Kendall correlation of 0.477 with human evaluations in the text summarization task on the test data.
Technology Infusion in Teacher Preparation

A technology-infused preparation program establishes technology integration as a program-deep and program-wide initiative to support incoming teachers to teach with technology from day one as certified teachers. PK-12 preparation programs adopt a technology-infused approach because they want to address technology integration from beginning to end of the preparation experience in a concerted effort to support candidates. Technological Pedagogical Content Knowledge (TPACK; Koehler and Mishra, 2009; Mishra and Koehler, 2006), a theory about the knowledge domains educators need to be proficient in teaching with technology, is foundational to an infused design. Foulger (2020) and Borthwick et al. (2020) advocate that technology-infused preparation programs comprehensively incorporate four pillars in their design: (a) technology integration curriculum, (b) modeled experiences, (c) practice with reflection, and (d) technology self-efficacy. Preparation programs that characterize their design based on the interrelatedness of the pillars (Williamson, 2023) will provide teacher candidates with the experiences necessary to graduate as certified teachers who are technologically capable. Graduates with strength in technology self-efficacy will intend to use technology in their future practice (Buss, 2020).&#x0D;
Only 5% Attention Is All You Need: Efficient Long-range Document-level Neural Machine Translation

Zihan Liu, Zewei Sun, Shanbo Cheng, Shujian Huang, Mingxuan Wang. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
HIT-MI&amp;T Lab’s Submission to Eval4NLP 2023 Shared Task

Recently, Large Language Models (LLMs) have boosted the research in natural language processing and shown impressive capabilities across numerous domains, including machine translation evaluation. This paper presents our methods developed for the machine translation evaluation sub-task of the Eval4NLP 2023 Shared Task. Based on the provided LLMs, we propose a generation-based method as well as a probability-based method to perform evaluation, explore different strategies when selecting the demonstrations for in-context learning, and try different ensemble methods to further improve the evaluation accuracy. The experiment results on the development set and test set demonstrate the effectiveness of our proposed method.
Informative Evidence-guided Prompt-based Fine-tuning for English-Korean Critical Error Detection

DaHyun Jung, Sugyeong Eo, Chanjun Park, Hyeonseok Moon, Jaehyung Seo, Heuiseok Lim. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
FollowupQG: Towards information-seeking follow-up question generation

Yan Meng, Liangming Pan, Yixin Cao, Min-Yen Kan. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
Characterised LLMs Affect its Evaluation of Summary and Translation

In today's widespread use of Large Language Models (LLMs), there have been significant achievements in various text domains such as generating summaries and translations. However, there is still room for development and improvement in evaluating the outputs of LLMs. In this paper, we propose an innovative scoring system that assesses the quality of summaries and translations using multiple metrics, we also enhance LLM's performance in scoring tasks by assigning it different roles, effectively making it act as an expert. We test four roles in the study: a teacher, a proofreader, a travel writer, and an internet troll, comparing the advantages and disadvantages of each role in the scoring task. Our research results demonstrate that emphasizing LLM's multilingual capabilities and strict standards as its identity can effectively boost its performance. Additionally, imbuing LLM with a more critical thinking ability enhances its performance in translation tasks compared to a milder LLM identity. In summary, we show that assigning different identities to LLM can influence its performance in scoring tasks. We believe that this research will contribute to the use of LLMs for scoring purposes.
Reranking for Natural Language Generation from Logical Forms: A Study based on Large Language Models

Levon Haroutunian, Zhuang Li, Lucian Galescu, Philip Cohen, Raj Tumuluri, Gholamreza Haffari. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
LTRC_IIITH’s 2023 Submission for Prompting Large Language Models as Explainable Metrics Task

In this report, we share our contribution to the Eval4NLP Shared Task titled "Prompting Large Language Models as Explainable Metrics." We build our prompts with a primary focus on effective prompting strategies, score-aggregation, and explainability for LLM-based metrics. We participated in the track for smaller models by submitting the scores along with their explanations. According to the Kendall correlation scores on the leaderboard, our MT evaluation submission ranks second-best, while our summarization evaluation submission ranks fourth, with only a 0.06 difference from the leading submission.