Reviewerly/exHarmony huggingface dataset:
Little Giants: Exploring the Potential of Small LLMs as Evaluation Metrics in Summarization in the Eval4NLP 2023 Shared Task

This paper describes and analyzes our participation in the 2023 Eval4NLP shared task, which focuses on assessing the effectiveness of prompt-based techniques to empower Large Language Models to handle the task of quality estimation, particularly in the context of evaluating machine translations and summaries. We conducted systematic experiments with various prompting techniques, including standard prompting, prompts informed by annotator instructions, and innovative chain-of-thought prompting. In addition, we integrated these approaches with zero-shot and one-shot learning methods to maximize the efficacy of our evaluation procedures. Our work reveals that combining these approaches using a "small", open source model (orca_mini_v3_7B) yields competitive results.
mahaNLP: A Marathi Natural Language Processing Library

Vidula Magdum, Omkar Jayant Dhekane, Sharayu Sandeep Hiwarkhedkar, Saloni Sunil Mittal, Raviraj Joshi. 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: System Demonstrations. 2023.
SILVER: Self Data Augmentation for Out-of-Scope Detection in Dialogues

Chunpeng Ma, Takuya Makino. 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.
Analysing Cross-Lingual Transfer in Low-Resourced African Named Entity Recognition

Michael Beukman, Manuel Fokam. 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.
ProMap: Effective Bilingual Lexicon Induction via Language Model Prompting

Abdellah El Mekki, Muhammad Abdul-Mageed, ElMoatez Billah Nagoudi, Ismail Berrada, Ahmed Khoumsi. 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.
SAINE: Scientific Annotation and Inference Engine of Scientific Research

Susie Xi Rao, Yilei Tu, Peter H. Egger. 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: System Demonstrations. 2023.
Assessment of Pre-Trained Models Across Languages and Grammars

Alberto Muñoz-Ortiz, David Vilares, Carlos Gómez-Rodríguez. 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.
LexicoMatic: Automatic Creation of Multilingual Lexical-Semantic Dictionaries

Federico Martelli, Luigi Procopio, Edoardo Barba, Roberto Navigli. 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.
Towards Automatic Construction of Filipino WordNet: Word Sense Induction and Synset Induction Using Sentence Embeddings

Dan John Velasco, Axel Alba, Trisha Gail Pelagio, Bryce Anthony Ramirez, Jan Christian Blaise Cruz, Unisse Chua, Briane Paul Samson, Charibeth Cheng. Proceedings of the First Workshop in South East Asian Language Processing. 2023.
Improving Neural Machine Translation with Offline Evaluations

Min-Kyung Park, Byung-Jun Lee. 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.
Turning Whisper into Real-Time Transcription System

Dominik Macháček, Raj Dabre, Ondřej Bojar. 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: System Demonstrations. 2023.
Uncertainty Estimation for Debiased Models: Does Fairness Hurt Reliability?

Gleb Kuzmin, Artem Vazhentsev, Artem Shelmanov, Xudong Han, Simon Suster, Maxim Panov, Alexander Panchenko, Timothy Baldwin. 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.
Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interaction

Jonathan Pilault, Xavier Garcia, Arthur Bražinskas, Orhan Firat. 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.
GrailQA++: A Challenging Zero-Shot Benchmark for Knowledge Base Question Answering

Ritam Dutt, Sopan Khosla, Vinayshekhar Bannihatti Kumar, Rashmi Gangadharaiah. 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.
We Need to Talk About Classification Evaluation Metrics in NLP

Peter Vickers, Loic Barrault, Emilio Monti, Nikolaos Aletras. 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.
PICK: Polished &amp; Informed Candidate Scoring for Knowledge-Grounded Dialogue Systems

Bryan Wilie, Yan Xu, Willy Chung, Samuel Cahyawijaya, Holy Lovenia, Pascale Fung. 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.
Modeling Collaborative Dialogue in Minecraft with Action-Utterance Model

Takuma Ichikawa, Ryuichiro Higashinaka. 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.
Large Language Models As Annotators: A Preliminary Evaluation For Annotating Low-Resource Language Content

The process of collecting human-generated annotations is time-consuming and resource-hungry. In the case of low-resource (LR) languages such as Indic languages, these efforts are more expensive due to the dearth of data and human experts. Considering their importance in solving downstream applications, there have been concentrated efforts exploring alternatives for human-generated annotations. To that extent, we seek to evaluate multilingual large language models (LLMs) for their potential to substitute or aid human-generated annotation efforts. We use LLMs to re-label publicly available datasets in LR languages for the tasks of natural language inference, sentiment analysis, and news classification. We compare these annotations with existing ground truth labels to analyze the efficacy of using LLMs for annotation tasks. We observe that the performance of these LLMs varies substantially across different tasks and languages. The results show that off-the-shelf use of multilingual LLMs is not appropriate and results in poor performance in two of the three tasks.
Minimum Bayes’ Risk Decoding for System Combination of Grammatical Error Correction Systems

Vyas Raina, Mark Gales. 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.
BREAST CANCER CAD SYSTEM BY USING TRANSFER LEARNING AND ENHANCED ROI

Computer systems are being employed in specialized professions such as medical diagnosis to alleviate some of the costs and to improve dependability and scalability. This paper implements a computer aided breast cancer diagnosis system. It utilizes the publicly available mini MIAS mammography image dataset. Images are preprocessed to clean isolate breast tissue region. Extracted regions are used to adjust and verify a pretrained convolutional deep neural network, the GoogLeNet. The implemented model shows good performance results compared to other published works with accuracy of 86.6%, sensitivity of 75% and specificity of 88.9%.
Generalizations of implication-based fuzzy subalgebras in BCK/BCI-algebras

In [2], Jun discussed implication-based subalgebras in BCK/BCI-algebras. In this article, more general forms than Jun’s results are discussed. We provide an example to show that a fuzzy subalgebra with thresholds 0 and 0.5 is not an implication-based subalgebra under the Łuckasiewicz implication operator, and then conditions for a fuzzy subalgebra with thresholds 0 and 0.5 to be an implication-based subalgebra under the Łuckasiewicz implication operator are provided.
Vision-based Terrain Relative Navigation for Planetary Landing

In this paper, the math involved in pose estimation is presented and examined. This work aims to determine pose (orientation and translation) based on the finding the correspondence between features in a image and a 3D points in the terrain frame using nonlinear least-squares method implemented through MATLAB's fSolve function. The use of Gazebo as a physics simulator to produce an exact lighting environment and simulate the descent of a camera-equipped satellite on a parabolic approach to the crater is also covered in detail. The challenges posed by sensor limitations and camera noise are also mentioned. It is shown that the nonlinear least-squares is able to accurately estimate the pose up to machine precision in the case without any noise, and up to a reasonable tolerance in the presence of noise. Monte-Carlo simulations are performed to validate these results.
THE STATE OF DEVELEPMENT STUDENTS’ SOCIAL ACTIVITY

The article is dedicated to theoretically substantiating key aspects of the social activity development among students pursuing education in the socio-economic field, and to experimentally assessing the level of social activity development among first-level (bachelor's) education seekers. Based on the analysis of scientific sources, the main forms of social activity manifestation in various spheres of personal social life are identified, including political, civic, cultural, creative, labor, communicative, and business activities. The article presents the outcomes of an experimental investigation designed to assess the level of social activity development among students at the bachelor's education level. The findings shed light on the students' social activity, revealing patterns, tendencies, and areas for potential enhancement. This research contributes to a better understanding of the dynamics of social activity development in the student population. The research identifies that social activity manifests in active life attitudes, civic positions, and national participation, involving interaction with the surrounding environment. This interaction is shaped through the social and volitional efforts of individuals during the implementation of planned activities. The article also presents the results of an experimental study conducted among first-level (bachelor's) education seekers. A questionnaire was used to investigate the desires and forms of support for the social activity of youth with a socio-economic profile of education. Additionally, the locus of control was determined using the method of O. Ksenofontova. According to the diagnostic results, the level of social activity development among students is at an average level. Most students exhibited an unstable need for social activity, demonstrating fluctuating emotional attitudes towards it, limited awareness of the concept of "social activity," and situational effectiveness in the socio-political sphere. To summarize, it can be stated that specific behavioral reactions, such as emancipation, grouping with peers, and enthusiasm, have already formed among students. Interaction with peers is a necessary element for achieving common goals, contributing to the development of social interaction skills, and shaping authority, locus of subjective control, and value orientations.
Gradient Calibration for Non-I.I.D. Federated Learning

Federated learning (FL) has yielded impressive results in recent years. However, its effectiveness on non-independently and identically distributed (non-i.i.d) data remains challenging. Existing work aims to address this challenge through client selection strategies or modifications to the client objective function. Despite these efforts, we contend that existing methods are unable to fully leverage client gradients. In this paper, we posit that the challenge of non-i.i.d data stems primarily from conflicting gradients among clients, resulting in deviations in both gradient magnitude and direction. Building on this insight, we propose a simple plug-in, named the Federated Gradient Tailor (FGT), to mitigate the non-i.i.d challenge by identifying and calibrating conflicting gradients before federated aggregation. To evaluate the efficacy of our approach, we conduct a series of experiments on simulated non-i.i.d datasets and demonstrate that the Federated Gradient Tailor significantly improves inference accuracy by 5% and achieves 6 times convergence speedup. Our findings highlight the value of explicitly addressing conflicting gradients in non-i.i.d data, as well as the effectiveness of the proposed Federated Gradient Tailor. This contribution has implications for further enhancing the performance of FL in real-world settings with non-i.i.d data distributions.
Multi-mode Learning

In contrast to federated learning, collaborative learning aims to reduce data transfer caused by frequent model updates by creating lightweight tailored models at the edge nodes. If the model needs to adapt due to environmental changes such as drift in workload characteristics, collaborative learning offers a head start by transferring knowledge from other edges that have experienced similar patterns. This learning paradigm presents a distinct point in a design space of distributed learning, creating opportunities for different tradeoffs. In this paper, we demonstrate that neither learning mode is universally superior (strictly better than the other), discuss the characteristics of the problem space that favor one model over the other, and make a case for a future system that enables seamless support for multi-mode learning.
FedCOM

Personalized federated learning aims to address two key challenges in federated systems: performance degradation of the global model, and the lack of specificity to individual clients. Both of them are caused by client heterogeneity. Previous solutions often assume all clients have similar tasks, and always utilize global or similar models to gain better personalized models for each client. However, we found they can't work well when came to a common task-heterogenic scenario, with skewed label distribution and complementary relationships between clients. To tackle this issue, we propose FedCOM, an innovative workflow that can help each client find its best peers to learn from. Our method consists of two key components: Complementary Client Matching and Personalized Federated Learning. We take gradients of the loss for target minority classes to identify feature channels that make significant contributions to classification scores. Subsequently, we introduce models from complementary peers as regularizers in the local personalization objectives. Experiments demonstrate that FedCOM can achieve faster convergence, while maintaining higher accuracy on MNIST, Fashion-MNIST, and CIFAR-10 with an average improvement of 41.78%, 23.74%, and 1.25% compared to FedProx, the clustering algorithm IFCA and the personalized federated method Ditto.
Escaping Adversarial Attacks with Egyptian Mirrors

Adversarial robustness received significant attention over the past years, due to its critical practical role. Complementary to the existing literature on adversarial training, we explore weight-space ensembles of independently trained models. We propose a defense against adversarial examples which takes advantage of the latest empirical findings on linear mode connectivity of overparameterized models modulo permutation invariance. Egyptian Mirrors defense escapes adversarial attacks by moving along linear paths between pair-wise aligned functionally diverse models, while frequently and arbitrary changing ensembling direction. We evaluate the proposed defense using adversarial examples generated by FGSM and PGD attacks and show improvements up to 8% and 33% test accuracy on 2-layer MLP and VGG11 architectures trained on GTSRB and CIFAR10 datasets respectively.
O najdawniejszej włoskiej leksykografii dwujęzycznej, znanej i nieznanej

On the Earliest Italian Bilingual Lexicography, Known and Unknown Since the Latin language became part of the education system, all kinds of grammatical compendiums, dictionaries, and commentaries on classical works of Greco-Roman literature have been an indispensable tool for exploring its intricacies. The turn of the 14th and 15th centuries was extremely interesting in this respect as it abounded in diverse and original works of this type, especially in Italy. The issues of studiorum humanitatis are discussed in such works as Elegantiae Linguae Latinae by Lorenzo Valle, Regulae grammaticales by Guarino Guarini, Epitoma seu Regulae constructionis and Latini sermonis emporium by Antonio Mancinelli, or the less known bilingual Glossarium by Jacopo Ursello from Roccantica. The books testify to teachers’ intentions to respond to the specific educational needs existing at that time, when it was necessary to educate students in written, spoken, and—to an increasing extent—Vulgar Latin. This article analyzes the above-mentioned Latin textbooks by Antonio Mancinelli and Jacopo Ursello, in which various aspects of Italianization operate in the light of the most important lexicographic sources of the era.
The transnational dimension of organised crime: an investigation into the operational structure of cybercrime in Nigeria

The world of Transnational Organised Crime (TOC) is a complex and multifaceted one. At present, no part of the world is immune from the corrupting influence and destabilising effects of Transnational Organised Crime. Their influence is global, and in certain circumstances, their convergence poses the greatest risk. The purpose of this article is to examine existing literature on TOC and demonstrate that the development of Information &amp; Communication Technology (ICT) and globalisation has led to the expansion and complexity of criminal organisations that take advantage of these changes for financial gain. The study focused on how cybercrime perpetrators in Nigeria learn in practice. Collaboration is found as a pivotal element in the learning process and is commonly characterised by both informal and contractual engagement between young people who are interested in cyber criminality and others who have substantial expertise in the illicit industry. A total of 29 cybercrime perpetrators were recruited on purpose with the semi-structured interview approach to gather qualitative data. A range of themes was identified through the use of NVivo (v.12), a qualitative software, and subsequently analysed using a content qualitative analytical tool. This study employs a qualitative methodology to explore various dimensions of the social learning process,information-sharing practises, group dynamics, and network paradigm of cybercrime perpetrators in Nigeria. Results indicate that the operational framework of cybercrime in Nigeria is complex and heavily reliant on co-offending, criminal apprenticeship, and criminal mentoring. Respondents emphasised that these elements give direct support in the context of criminal activities. The paper concludes that transnational crime cannot be effectively addressed from a national standpoint; rather, a concerted international and regional effort is required to enhance the fragile capacities of states, subregions, and regional organisations
Modality Realization in Covid-19 Academic Papers

Modalities are often used in both spoken and written material. However, people only know the modalities and values manifested in academic written texts once they conduct a study. This research aims to analyze the form and matter of modalities and investigate how these modalities are realized in Covid-19 scholarly articles. This research uses descriptive qualitative research methods and Halliday's theory. The data consists of clauses of six COVID-19 social and political journal articles on various topics, such as (social, economic, political, health, communication, and tourism). Data collection was carried out using a documentary approach. The model used in this research is interactive. The data analysis technique uses qualitative descriptive analysis. Based on the findings of this research, 260 clauses contain modality, and two types of modality are used in them. This research also found the realization of modality as a capital operator that is often used in Covid-19 academic papers, as it is. Found 238 capital operators. Apart from that, authors generally use limited modal operators in their articles because there are 238 modal operators located, and this is followed by additional modals with a total of 18. On the other hand, passive verb predicators and adjectives are rarely found.
Game Interactive Learning: A New Paradigm towards Intelligent Decision-Making

Decision-making plays an essential role in various real-world systems like automatic driving, traffic dispatching, information system management, and emergency command and control. Recent breakthroughs in computer game scenarios using deep reinforcement learning for intelligent decision-making have paved decision-making intelligence as a burgeoning research direction. In complex practical systems, however, factors like coupled distracting features, long-term interact links, and adversarial environments and opponents, make decision-making in practical applications challenging in modeling, computing, and explaining. This work proposes game interactive learning, a novel paradigm as a new approach towards intelligent decision-making in complex and adversarial environments. This novel paradigm highlights the function and role of a human in the process of intelligent decision-making in complex systems. It formalizes a new learning paradigm for exchanging information and knowledge between humans and the machine system. The proposed paradigm first inherits methods in game theory to model the agents and their preferences in the complex decision-making process. It then optimizes the learning objectives from equilibrium analysis using reformed machine learning algorithms to compute and pursue promising decision results for practice. Human interactions are involved when the learning process needs guidance from additional knowledge and instructions, or the human wants to understand the learning machine better. We perform preliminary experimental verification of the proposed paradigm on two challenging decision-making tasks in tactical-level War-game scenarios. Experimental results demonstrate the effectiveness of the proposed learning paradigm.