Reviewerly/exHarmony huggingface dataset:
First-year students’ problems affecting adaptation to a public university in Kazakhstan

This study examines some of the difficulties that first-year students in higher education face as they adjust to the university life. The primary objective of this study is to determine which events and programs aided first-year university students to integrate into their new educational environment and become accustomed to their new surroundings. The participants in this research are first-year students at M.Kh. Dulaty Taraz Regional University who initiated their hybrid (online and offline) studies in the 2021-2022 academic year. For the purpose of acquiring data, the authors provided a survey to 270 first-year students and conducted additional interviews with 15 of them. The findings examine the attitudes, expectations, and experiences of first-year university learners, as well as the support from institutional agents, interaction with their instructors, and community involvement, among other factors related to a successful and productive first year of education. Findings of this study will be helpful to the institution’s administration, faculty, and staff in determining the most effective method for easing first-year students into the routines and culture of university life. Based on the findings and results, a number of recommendations for the assimilation of first-year university students have been developed and presented in this paper.
Use of STEM innovative methods in Chemistry lessons with the implementation of intersubject communications

STEM is an integrated, interdisciplinary learning approach in which academic scientific and technical concepts are studied in the context of real life. Within the framework of the installations of this method, the student learns to find solutions to specific problems and create prototypes for new mechanisms, techniques, programs. The article describes a study aimed at establishing the effectiveness of the implementation of the STEM approach in chemistry lessons in 8th grade as part of the updated content of education based on the results of checking the progress of students. The results showed that the use of STEM technologies in chemistry lessons had a positive impact on the academic performance of the participants of the experiment. At the same time, it has been proven that the use of STEM increases the motivation of students to study and understand the theoretical and practical aspects of the topic being studied. The application of the method will also make it possible to establish strong links between school, society, work and the whole world, contributing to the development of STEM literacy and competitiveness in the global economy.
Development of environmental competence of students through facilitation technology

This article is a part of the long-term work of the author, which continues to this day, devoted to the study of the problems of the formation and development of environmental culture and environmental competence of the individual. The authors share their experience in developing students’ environmental competence through facilitation technology. The authors reveal the concept of ‘facilitation technology‘, as well as its essence and main methods. Directly, the technologies ‘World Cafe‘ and ‘dynamic facilitation‘ are revealed, which were used by the authors in the course of work with students. In the article describes in detail the methodology for using ‘dynamic facilitation‘, ‘World Cafe‘ in the educational process with students in extracurricular work to develop the environmental competence of the individual. The author reveals their essence, as well as the role of a teacherfacilitator in the organization and use of these technologies for the development of students’ environmental competence. Also, the authors reveal the concepts of ‘competence‘ and ‘environmental competence‘, their essence through the analysis of research and opinions of scientists. Further, the article provides a description and results of the study, which in turn proves the effectiveness of facilitation technology in the development of students’ environmental competence.
THE ROLE OF INFORMATION TECHNOLOGY (IT) IN KNOWLEDGE MANAGEMENT PROCESS AT BUSINESS ADMINISTRATION DEPARTMENT LECTURERS, STATE POLYTECHNIC OF MALANG

The role of universities in providing knowledge and learning in various scientific disciplines has positioned universities as knowledge-based organizations. This suggests that universities have an important level of knowledge management (KM) activity although they may not have formal KM practices. This study aims to investigate the knowledge management process with its various dimensions namely knowledge creation, acquisition, organization, sharing and implementation in the Business Administration Department State Polytechnic of Malang. It also to identify the use of IT (hardware, software, security and usability) including the impotance of IT in supporting KM process to increase the competitiveness of Business Administration Department, Malang State Polytechnic. This research is qualitative research using case study approach. The research subject is Business Administration Department, State Polytechnic of Malang. Unit analyses are head of Department, secretary of department, head of study program, lecturers and administratif staffs. Data collection methods use interview, observation, and document analysis. Data analysis use within case analysis. The research findings showed that activities held by the Business Administration Department are in accordance with a knowledge management system such as capture, store, change and share knowledge from the department to other members of the organization. The IT components including hardware, software, intranet, and internet networks as well as integrated applications facilitate all the activities. Suggestions were provided related to updating IT that should be conducted regularly including increasing internet network capacity to support knowledge management process that will increase organization performance.
Fast Whitespace Correction with Encoder-Only Transformers

The goal of whitespace correction is to fix space errors in arbitrary given text.For example, given the text whi te space correctio nwithTransf or mers, produce whitespace correction with Transformers.We compare two Transformer-based models, a characterlevel encoder-decoder model and a byte-level encoder-only model.We find that the encoderonly model is both faster and achieves higher quality.We provide an easy-to-use tool that is over 900 times faster than the previous best tool, with the same high quality.Our tool repairs text at a rate of over 200 kB/s on GPU, with a sequence-averaged F 1 -score ranging from 87.5% for hard-to-correct text up to 99% for text without any spaces.
HAHE: Hierarchical Attention for Hyper-Relational Knowledge Graphs in Global and Local Level

Link Prediction on Hyper-relational Knowledge Graphs (HKG) is a worthwhile endeavor.HKG consists of hyper-relational facts (H-Facts), composed of a main triple and several auxiliary attribute-value qualifiers, which can effectively represent factually comprehensive information.The internal structure of HKG can be represented as a hypergraphbased representation globally and a semantic sequence-based representation locally.However, existing research seldom simultaneously models the graphical and sequential structure of HKGs, limiting HKGs' representation.To overcome this limitation, we propose a novel Hierarchical Attention model for HKG Embedding (HAHE), including global-level and local-level attention.The global-level attention can model the graphical structure of HKG using hypergraph dual-attention layers, while the local-level attention can learn the sequential structure inside H-Facts via heterogeneous self-attention layers.Experiment results indicate that HAHE achieves state-of-the-art performance in link prediction tasks on HKG standard datasets.In addition, HAHE addresses the issue of HKG multi-position prediction for the first time, increasing the applicability of the HKG link prediction task.Our code is publicly available 1 .
BiSync: A Bilingual Editor for Synchronized Monolingual Texts

In our globalized world, a growing number of situations arise where people are required to communicate in one or several foreign languages.In the case of written communication, users with a good command of a foreign language may find assistance from computeraided translation (CAT) technologies.These technologies often allow users to access external resources, such as dictionaries, terminologies or bilingual concordancers, thereby interrupting and considerably hindering the writing process.In addition, CAT systems assume that the source sentence is fixed and also restrict the possible changes on the target side.In order to make the writing process smoother, we present BiSync, a bilingual writing assistant that allows users to freely compose text in two languages, while maintaining the two monolingual texts synchronized.We also include additional functionalities, such as the display of alternative prefix translations and paraphrases, which are intended to facilitate the authoring of texts.We detail the model architecture used for synchronization and evaluate the resulting tool, showing that high accuracy can be attained with limited computational resources.The interface and models are publicly available at https://github.com/jmcrego/BiSync and a demonstration video can be watched on YouTube.
Massively Multi-Lingual Event Understanding: Extraction, Visualization, and Search

In this paper, we present ISI-CLEAR, a stateof-the-art, cross-lingual, zero-shot event extraction system and accompanying user interface for event visualization & search.Using only English training data, ISI-CLEAR makes global events available on-demand, processing user-supplied text in 100 languages ranging from Afrikaans to Yiddish.We provide multiple event-centric views of extracted events, including both a graphical representation and a document-level summary.We also integrate existing cross-lingual search algorithms with event extraction capabilities to provide crosslingual event-centric search, allowing Englishspeaking users to search over events automatically extracted from a corpus of non-English documents, using either English natural language queries (e.g.cholera outbreaks in Iran) or structured queries (e.g.find all events of type Disease-Outbreak with agent cholera and location Iran).
Enhancing Translation for Indigenous Languages: Experiments with Multilingual Models

This paper describes CIC NLP's submission to the AmericasNLP 2023 Shared Task on machine translation systems for indigenous languages of the Americas.We present the system descriptions for three methods.We used two multilingual models, namely M2M-100 and mBART50, and one bilingual (one-to-one) -Helsinki NLP Spanish-English translation model, and experimented with different transfer learning setups.We experimented with 11 languages from America and report the setups we used as well as the results we achieved.Overall, the mBART setup was able to improve upon the baseline for three out of the eleven languages.
CWSeg: An Efficient and General Approach to Chinese Word Segmentation

In this work, we report our efforts in advancing Chinese Word Segmentation for the purpose of rapid deployment in different applications.The pre-trained language model (PLM) based segmentation methods have achieved state-of-the-art (SOTA) performance, whereas this paradigm also poses challenges in the deployment.It includes the balance between performance and cost, segmentation ambiguity due to domain diversity and vague words boundary, and multi-grained segmentation.In this context, we propose a simple yet effective approach, namely CWSeg, to augment PLMbased schemes by developing cohort training and versatile decoding strategies.Extensive experiments on benchmark datasets demonstrate the efficiency and generalization of our approach.The corresponding segmentation system is also implemented for practical usage and the demo is recorded.
XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models

NLP models are susceptible to learning spurious biases (i.e., bugs) that work on some datasets but do not properly reflect the underlying task.Explanation-based model debugging aims to resolve spurious biases by showing human users explanations of model behavior, asking users to give feedback on the behavior, then using the feedback to update the model.While existing model debugging methods have shown promise, their prototype-level implementations provide limited practical utility.Thus, we propose XMD : the first open-source, end-toend framework for explanation-based model debugging.Given task-or instance-level explanations, users can flexibly provide various forms of feedback via an intuitive, web-based UI.After receiving user feedback, XMD automatically updates the model in real time, by regularizing the model so that its explanations align with the user feedback.The new model can then be easily deployed into real-world applications via Hugging Face.Using XMD , we can improve the model's OOD performance on text classification tasks by up to 18%.
CARE: Collaborative AI-Assisted Reading Environment

Recent years have seen impressive progress in AI-assisted writing, yet the developments in AI-assisted reading are lacking.We propose inline commentary as a natural vehicle for AIbased reading assistance, and present CARE: the first open integrated platform for the study of inline commentary and reading.CARE facilitates data collection for inline commentaries in a commonplace collaborative reading environment, and provides a framework for enhancing reading with NLP-based assistance, such as text classification, generation or question answering.The extensible behavioral logging allows unique insights into the reading and commenting behavior, and flexible configuration makes the platform easy to deploy in new scenarios.To evaluate CARE in action, we apply the platform in a user study dedicated to scholarly peer review.CARE facilitates the data collection and study of inline commentary in NLP, extrinsic evaluation of NLP assistance, and application prototyping.We invite the community to explore and build upon the open source implementation of CARE 1 .
Generate-then-Retrieve: Intent-Aware FAQ Retrieval in Product Search

Customers interacting with product search engines are increasingly formulating informationseeking queries.Frequently Asked Question (FAQ) retrieval aims to retrieve common question-answer pairs for a user query with question intent.Integrating FAQ retrieval in product search can not only empower users to make more informed purchase decisions, but also enhance user retention through efficient post-purchase support.Determining when an FAQ entry can satisfy a user's information need within product search, without disrupting their shopping experience, represents an important challenge.We propose an intent-aware FAQ retrieval system consisting of (1) an intent classifier that predicts when a user's information need can be answered by an FAQ; (2) a reformulation model that rewrites a query into a natural question.Offline evaluation demonstrates that our approach improves Hit@1 by 13% on retrieving ground-truth FAQs, while reducing latency by 95% compared to baseline systems.These improvements are further validated by real user feedback, where 71% of displayed FAQs on top of product search results received explicit positive user feedback.Overall, our findings show promising directions for integrating FAQ retrieval into product search at scale.
Domain-specific transformer models for query translation

Due to the democratization of e-commerce, many product companies are listing their goods for online shopping.For periodic buying within a domain such as Grocery, consumers are generally inclined to buy certain brands of products.Due to a large non-English speaking population in India, we observe a significant percentage of code-mix Hinglish search queries e.g., sasta atta.An intuitive approach to dealing with code-mix queries is to train an encoder-decoder model to translate the query to English to perform the search.However, the problem becomes non-trivial when the brand names themselves have Hinglish names and possibly have a literal English translation.In such queries, only the context (non-brand name) Hinglish words needs to be translated.In this paper, we propose a simple yet effective modification to the transformer training to preserve/correct Grocery brand names in the output while selectively translating the context words.To achieve this, we use an additional dataset of popular Grocery brand names.Brand names are added as tokens to the model vocabulary, and the token embeddings are randomly initialized.Further, we introduce a Brand loss in training the translation model.Brand loss is a cross entropy loss computed using a denoising auto-encoder objective with brand name data.We warmstart the training from a public pre-trained checkpoint (such as BART/T5) and further adapt it for query translation using the domain data.The proposed model is generic and can be used with English as well as code-mix Hinglish queries alleviating the need for language detection.To reduce the latency of the model for the production deployment, we use knowledge distillation and quantization.Experimental evaluation indicates that the proposed approach improves translation results by preserving/correcting English/Hinglish brand names.After positive results with A/B testing, the model is currently deployed in production.
Targeted Data Generation: Finding and Fixing Model Weaknesses

Even when aggregate accuracy is high, stateof-the-art NLP models often fail systematically on specific subgroups of data, resulting in unfair outcomes and eroding user trust.Additional data collection may not help in addressing these weaknesses, as such challenging subgroups may be unknown to users, and underrepresented in the existing and new data.We propose Targeted Data Generation (TDG), a framework that automatically identifies challenging subgroups, and generates new data for those subgroups using large language models (LLMs) with a human in the loop.TDG estimates the expected benefit and potential harm of data augmentation for each subgroup, and selects the ones most likely to improve withingroup performance without hurting overall performance.In our experiments, TDG 1 significantly improves the accuracy on challenging subgroups for state-of-the-art sentiment analysis and natural language inference models, while also improving overall test accuracy.
Finspector: A Human-Centered Visual Inspection Tool for Exploring and Comparing Biases among Foundation Models

Figure 1: An overview of Finspector.Users can launch Finspector in a Python notebook (e.g., Jupyter).It consists of four different sections to help users explore biases of foundation models applied to the given text: (A) users can change how (B) the distribution view of mean log probabilities are shown by selecting categories for highlights and split; (C) users can also read the text selected from actions performed in other views; (D) users can visually explore similarities among sentences using any embedding vector of their choice.
Reviewriter: AI-Generated Instructions For Peer Review Writing

Large Language Models (LLMs) offer novel opportunities for educational applications that have the potential to transform traditional learning for students.Despite AI-enhanced applications having the potential to provide personalized learning experiences, more studies are needed on the design of generative AI systems and evidence for using them in real educational settings.In this paper, we design, implement and evaluate Reviewriter, a novel tool to provide students with AI-generated instructions for writing peer reviews in German.Our study identifies three key aspects: a) we provide insights into student needs when writing peer reviews with generative models which we then use to develop a novel system to provide adaptive instructions b) we fine-tune three German language models on a selected corpus of 11,925 student-written peer review texts in German and choose German-GPT2 based on quantitative measures and human evaluation, and c) we evaluate our tool with fourteen students, revealing positive technology acceptance based on quantitative measures.Additionally, the qualitative feedback presents the benefits and limitations of generative AI in peer review writing.
Log-linear Guardedness and its Implications

Methods for erasing human-interpretable concepts from neural representations that assume linearity have been found to be tractable and useful.However, the impact of this removal on the behavior of downstream classifiers trained on the modified representations is not fully understood.In this work, we formally define the notion of log-linear guardedness as the inability of an adversary to predict the concept directly from the representation, and study its implications.We show that, in the binary case, under certain assumptions, a downstream log-linear model cannot recover the erased concept.However, we demonstrate that a multiclass log-linear model can be constructed that indirectly recovers the concept in some cases, pointing to the inherent limitations of log-linear guardedness as a downstream bias mitigation technique.These findings shed light on the theoretical limitations of linear erasure methods and highlight the need for further research on the connections between intrinsic and extrinsic bias in neural models.
ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit

ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the broadening interests of the spoken language translation community.ESPnet-ST-v2 supports 1) offline speech-to-text translation (ST), 2) simultaneous speech-to-text translation (SST), and 3) offline speech-to-speech translation (S2ST) -each task is supported with a wide variety of approaches, differentiating ESPnet-ST-v2 from other open source spoken language translation toolkits.This toolkit offers state-ofthe-art architectures such as transducers, hybrid CTC/attention, multi-decoders with searchable intermediates, time-synchronous blockwise CTC/attention, Translatotron models, and direct discrete unit models.In this paper, we describe the overall design, example models for each task, and performance benchmarking behind ESPnet-ST-v2, which is publicly available at https://github.com/espnet/espnet.
CocaCLIP: Exploring Distillation of Fully-Connected Knowledge Interaction Graph for Lightweight Text-Image Retrieval

Large-scale pre-trained text-image models with dual-encoder architectures (such as CLIP (Radford et al., 2021)) are typically adopted for various vision-language applications, including text-image retrieval.However, these models are still less practical on edge devices or for real-time situations, due to the substantial indexing and inference time and the large consumption of computational resources.Although knowledge distillation techniques have been widely utilized for uni-modal model compression, how to expand them to the situation when the numbers of modalities and teachers/students are doubled has been rarely studied.In this paper, we conduct comprehensive experiments on this topic and propose the fully-Connected knowledge interaction graph (Cona) technique for cross-modal pre-training distillation.Based on our findings, the resulting ConaCLIP achieves SOTA performances on the widely-used Flickr30K and MSCOCO benchmarks under the lightweight setting.An industry application of our method on an ecommercial platform further demonstrates the significant effectiveness of ConaCLIP.