Reviewerly/exHarmony huggingface dataset:
JRLV at SemEval-2022 Task 5: The Importance of Visual Elements for Misogyny Identification in Memes

Gender discrimination is a serious and widespread problem on social media and online in general.Besides offensive messages, memes are one of the main means of dissemination for such content.With these premises, the MAMI task was proposed at the SemEval-2022, which consists of identifying memes with misogynous characteristics.In this work, we propose a solution to this problem based on Mask R-CNN and VisualBERT that leverages the multimodal nature of the task.Our study focuses on observing how the two sources of data in memes (text and image) and their possible combinations impact performances.Our best result slightly exceeds the higher baseline, but the experiments allowed us to draw important considerations regarding the importance of correctly exploiting the visual information and the relevance of the elements present in the memes images.
Team Innovators at SemEval-2022 for Task 8: Multi-Task Training with Hyperpartisan and Semantic Relation for Multi-Lingual News Article Similarity

This work represents the system proposed by team Innovators for SemEval 2022 Task 8: Multilingual News Article Similarity (Chen et al., 2022).Similar multilingual news articles should match irrespective of the style of writing, the language of conveyance, and subjective decisions and biases induced by medium/outlet.The proposed architecture includes a machine translation system that translates multilingual news articles into English and presents a multitask learning model trained simultaneously on three distinct datasets.The system leverages the PageRank algorithm for Long-form text alignment.Multitask learning approach allows simultaneous training of multiple tasks while sharing the same encoder during training, facilitating knowledge transfer between tasks.Our best model is ranked 16 with a Pearson score of 0.733.We make our code accessible here 1
UFRGSent at SemEval-2022 Task 10: Structured Sentiment Analysis using a Question Answering Model

This paper describes the system submitted by our team (UFRGSent) to SemEval-2022 Task 10: Structured Sentiment Analysis.We propose a multilingual approach that relies on a Question Answering model to find tuples consisting of holder, target, and opinion expression.The approach starts from general questions and uses the extracted tuple elements to find the remaining components.Finally, we employ an aspect sentiment classification model to classify the polarity of the entire tuple.Despite our method being in a mid-rank position in the Se-mEval competition, we show that the questionanswering approach can achieve good coverage retrieving sentiment tuples, allowing room for improvements in the technique.
Human-Centered Evaluation of Explanations

Jordan Boyd-Graber, Samuel Carton, Shi Feng, Q. Vera Liao, Tania Lombrozo, Alison Smith-Renner, Chenhao Tan. Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts. 2022.
Efficient Learning of Multiple NLP Tasks via Collective Weight Factorization on BERT

The Transformer architecture continues to show remarkable performance gains in many Natural Language Processing tasks.However, obtaining such state-of-the-art performance in different tasks requires fine-tuning the same model separately for each task.Clearly, such an approach is demanding in terms of both memory requirements and computing power.In this paper, aiming to improve training efficiency across multiple tasks, we propose to collectively factorize the weighs of the multi-head attention module of a pre-trained Transformer.We test our proposed method on finetuning multiple natural language understanding tasks by employing BERT-Large as an instantiation of the Transformer and the GLUE as the evaluation benchmark.Experimental results show that our method requires training and storing only 1% of the initial model parameters for each task and matches or improves the original fine-tuned model's performance for each task while effectively decreasing the parameter requirements by two orders of magnitude.Furthermore, compared to well-known adapter-based alternatives on the GLUE benchmark, our method consistently reaches the same levels of performance while requiring approximately four times fewer total and trainable parameters per task.
GUTS at SemEval-2022 Task 4: Adversarial Training and Balancing Methods for Patronizing and Condescending Language Detection

Patronizing and Condescending Language (PCL) towards vulnerable communities in general media has been shown to have potentially harmful effects.Due to its subtlety and the good intentions behind its use, the audience is not aware of the language's toxicity.In this paper, we present our method for the SemEval-2022 Task4 titled "Patronizing and Condescending Language Detection".In Subtask A, a binary classification task, we introduce adversarial training based on Fast Gradient Method (FGM) and employ pre-trained model in a unified architecture.For Subtask B, framed as a multi-label classification problem, we utilize various improved multi-label cross-entropy loss functions and analyze the performance of our method.In the final evaluation, our system achieved official rankings of 17/79 and 16/49 on Subtask A and Subtask B, respectively.In addition, we explore the relationship between PCL and emotional polarity and intensity it contains.Our code is available on Github 1 .
CL-ReLKT: Cross-lingual Language Knowledge Transfer for Multilingual Retrieval Question Answering

Cross-Lingual Retrieval Question Answering (CL-ReQA) is concerned with retrieving answer documents or passages to a question written in a different language.A common approach to CL-ReQA is to create a multilingual sentence embedding space such that questionanswer pairs across different languages are close to each other.In this paper, we propose a novel CL-ReQA method utilizing the concept of language knowledge transfer and a new cross-lingual consistency training technique to create a multilingual embedding space for ReQA.To assess the effectiveness of our work, we conducted comprehensive experiments on CL-ReQA and a downstream task, machine reading QA.We compared our proposed method with the current state-of-the-art solutions across three public CL-ReQA corpora.Our method outperforms competitors in 19 out of 21 settings of CL-ReQA.When used with a downstream machine reading QA task, our method outperforms the best existing language-model-based method by 10% in F1 while being 10 times faster in sentence embedding computation.The code and models are available at https://github.com/mrpeerat/CL-ReLKT.
CMB AI Lab at SemEval-2022 Task 11: A Two-Stage Approach for Complex Named Entity Recognition via Span Boundary Detection and Span Classification

This paper presents a solution for the SemEval-2022 Task 11 Multilingual Complex Named Entity Recognition.What is challenging in this task is detecting semantically ambiguous and complex entities in short and low-context settings.Our team (CMB AI Lab) propose a two-stage method to recognize the named entities: first, a model based on biaffine layer is built to predict span boundaries, and then a span classification model based on pooling layer is built to predict semantic tags of the spans.The basic pre-trained models we choose are XLM-RoBERTa and mT5.The evaluation result of our approach achieves an F1 score of 84.62 on sub-task 13, which ranks the third on the learder board.
Label Definitions Improve Semantic Role Labeling

Argument classification is at the core of Semantic Role Labeling.Given a sentence and the predicate, a semantic role label is assigned to each argument of the predicate.While semantic roles come with meaningful definitions, existing work has treated them as symbolic.Learning symbolic labels usually requires ample training data, which is frequently unavailable due to the cost of annotation.We instead propose to retrieve and leverage the definitions of these labels from the annotation guidelines.For example, the verb predicate "work" has arguments defined as "worker", "job", "employer", etc.Our model achieves state-of-theart performance on the CoNLL09 English SRL dataset injected with label definitions given the predicate senses.The performance improvement is even more pronounced in low-resource settings when training data is scarce.
The Why and The How: A Survey on Natural Language Interaction in Visualization

Natural language as a modality of interaction is becoming increasingly popular in the field of visualization.In addition to the popular query interfaces, other language-based interactions such as annotations, recommendations, explanations, or documentation experience growing interest.In this survey, we provide an overview of natural language-based interaction in the research area of visualization.We discuss a renowned taxonomy of visualization tasks and classify 119 related works to illustrate the stateof-the-art of how current natural language interfaces support their performance.We examine applied NLP methods and discuss humanmachine dialogue structures with a focus on initiative, duration, and communicative functions in recent visualization-oriented dialogue interfaces.Based on this overview, we point out interesting areas for the future application of NLP methods in the field of visualization.
Low-resource Entity Set Expansion: A Comprehensive Study on User-generated Text

Entity set expansion (ESE) aims at obtaining a more complete set of entities given a textual corpus and a seed set of entities of a concept.Although it is a critical task in many NLP applications, existing benchmarks are limited to well-formed text (e.g., Wikipedia) and welldefined concepts (e.g., countries and diseases).Furthermore, only a small number of predictions are evaluated compared to the actual size of an entity set.A rigorous assessment of ESE methods warrants more comprehensive benchmarks and evaluation.In this paper, we consider user-generated text to understand the generalizability of ESE methods.We develop new benchmarks and propose more rigorous evaluation metrics for assessing performance of ESE methods.Additionally, we identify phenomena such as non-named entities, multifaceted entities, vague concepts that are more prevalent in user-generated text than well-formed text, and use them to profile ESE methods.We observe that the strong performance of state-of-the-art ESE methods does not generalize well to user-generated text.We conduct comprehensive empirical analysis and draw insights from the findings.
Craft an Iron Sword: Dynamically Generating Interactive Game Characters by Prompting Large Language Models Tuned on Code

Non-Player Characters (NPCs) significantly enhance the player experience in many games.Historically, players' interactions with NPCs have tended to be highly scripted, to be limited to natural language responses to be selected by the player, and to not involve dynamic change in game state.In this work, we demonstrate that use of a few example conversational prompts can power a conversational agent to generate both natural language and novel code.This approach can permit development of NPCs with which players can have grounded conversations that are free-form and less repetitive.We demonstrate our approach using OpenAI Codex (GPT-3 finetuned on GitHub), with Minecraft game development as our test bed.We show that with a few example prompts 1 , a Codexbased agent can generate novel code, hold multi-turn conversations and answer questions about structured data.We evaluate this application using experienced gamers in a Minecraft realm and provide analysis of failure cases and suggest possible directions for solutions.
Generic and Trend-aware Curriculum Learning for Relation Extraction

We present a generic and trend-aware curriculum learning approach for graph neural networks.It extends existing approaches by incorporating sample-level loss trends to better discriminate easier from harder samples and schedule them for training.The model effectively integrates textual and structural information for relation extraction in text graphs.Experimental results show that the model provides robust estimations of sample difficulty and shows sizable improvement over the stateof-the-art approaches across several datasets.
Modern Tools for Developing the Scientific Potential of the University

One of the key support mechanisms for research in universities is national scientific foundations, which often represent the main source of funding. However, the capacity of young universities to compete in national foundations’ calls is severely limited due to fierce competition and high qualification barriers. This hinders young universities’ chances to raise research funding and strengthens the Matthew effect. The establishment of proprietary research support infrastructure, in particular, internal research support funds may contribute to the development of human resources and reduce the qualification gap between young universities and classical or research-oriented ones. The purpose of the article is to analyze the practices of creating research support mechanisms in young universities. The study addresses the establishment of the Research and Development Fund at the North-West Institute of Management of RANEPA. We analyze the organizational model of the fund and the algorithm of the application campaign using the pilot call for funding literature reviews as an illustrative example. The main contribution of the article is a detailed description of the model of organizational processes of the fund and the management practices that support it. The article may be relevant for research managers of young universities and university branches, as well as for researchers interested in studying research support mechanisms.
Temporal Knowledge Graph Embedding Model based on Additive Time Series  Decomposition

Knowledge Graph (KG) embedding has attracted more attention in recent years. Most KG embedding models learn from time-unaware triples. However, the inclusion of temporal information beside triples would further improve the performance of a KGE model. In this regard, we propose ATiSE, a temporal KG embedding model which incorporates time information into entity/relation representations by using Additive Time Series decomposition. Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions. The mean of each entity/relation embedding at a time step shows the current expected position, whereas its covariance (which is temporally stationary) represents its temporal uncertainty. Experimental results show that ATiSE chieves the state-of-the-art on link prediction over four temporal KGs.
Learning Generalized Policies without Supervision Using GNNs

We consider the problem of learning generalized policies for classical planning domains using graph neural networks from small instances represented in lifted STRIPS. The problem has been considered before but the proposed neural architectures are complex and the results are often mixed. In this work, we use a simple and general GNN architecture and aim at obtaining crisp experimental results and a deeper understanding: either the policy greedy in the learned value function achieves close to 100% generalization over instances larger than those used in training, or the failure must be understood, and possibly fixed, logically. For this, we exploit the relation established between the expressive power of GNNs and the C2 fragment of first-order logic (namely, FOL with 2 variables and counting quantifiers). We find for example that domains with general policies that require more expressive features can be solved with GNNs once the states are extended with suitable "derived atoms" encoding role compositions and transitive closures that do not fit into C2. The work follows an existing approach based on GNNs for learning optimal general policies in a supervised fashion, but the learned policies are no longer required to be optimal (which expands the scope, as many planning domains do not have general optimal policies) and are learned without supervision. Interestingly, value-based reinforcement learning methods that aim to produce optimal policies, do not always yield policies that generalize, as the goals of optimality and generality are in conflict in domains where optimal planning is NP-hard.
Sum-Product Loop Programming: From Probabilistic Circuits to Loop Programming

Recently, Probabilistic Circuits such as Sum-Product Networks have received growing attention, as they can represent complex features but still provide tractable inference. Although quite successful, unfortunately, they lack the capability of handling control structures, such as for and while loops. In this work, we introduce Sum-Product Loop Language (SPLL), a novel programming language that is capable of tractable inference on complex probabilistic code that includes loops. SPLL has dual semantics: every program has generative semantics familiar to most programmers and probabilistic semantics that assign a probability to each possible result. This way, the programmer can describe how to generate samples almost like in any standard programming language. The language takes care of computing the probability values of all results for free at run time. We demonstrate that SPLL inherits the beneficial properties of PCs, namely tractability and differentiability, while generalizing to other distributions and programs, and retains substantial computational similarities.
Neural-Probabilistic Answer Set Programming

The goal of combining the robustness of neural networks and the expressivity of symbolic methods has rekindled the interest in Neuro-Symbolic AI. One specifically interesting branch of research is deep probabilistic programming languages (DPPLs) which carry out probabilistic logical programming via the probability estimations of deep neural networks. However, recent SOTA DPPL approaches allow only for limited conditional probabilistic queries and do not offer the power of true joint probability estimation. In our work, we propose an easy integration of tractable probabilistic inference within a DPPL. To this end we introduce SLASH, a novel DPPL that consists of Neural-Probabilistic Predicates (NPPs) and a logical program, united via answer set programming. NPPs are a novel design principle allowing for the unification of all deep model types and combinations thereof to be represented as a single probabilistic predicate. In this context, we introduce a novel +/- notation for answering various types of probabilistic queries by adjusting the atom notations of a predicate. We evaluate SLASH on the benchmark task of MNIST addition as well as novel tasks for DPPLs such as missing data prediction, generative learning and set prediction with state-of-the-art performance, thereby showing the effectiveness and generality of our method.
ALASPO: An Adaptive Large-Neighbourhood ASP Optimiser

We present the system ALASPO which implements Adaptive Large-neighbourhood search for Answer Set Programming (ASP) Optimisation. Large-neighbourhood search (LNS) is a meta-heuristic where parts of a solution are destroyed and reconstructed in an attempt to improve an overall objective. ALASPO currently supports the ASP solver clingo, as well as its extensions clingo-dl and clingcon for difference and full integer constraints, and multi-shot solving for an efficient implementation of the LNS loop. Neighbourhoods can be defined in code or declaratively as part of the ASP encoding. While the method underlying ALASPO has been described in previous work, ALASPO also incorporates portfolios for the LNS operators along with self-adaptive selection strategies as a technical novelty. This improves usability considerably at no loss of solution quality, but on the contrary often yields benefits. To demonstrate this, we evaluate ALASPO on different optimisation benchmarks.
Pengaruh Media Pembelajaran Google Classroom Terintegrasi dengan Kahoot Terhadap Minat dan Hasil Belajar Siswa Mata Pelajaran IPA Materi Siklus Air

Googgle Classroom learning integrated with Kahoot is online learning that utilizes two learning media into a series of fun and meaningful learning process flows for students. Google Classroom as a classroom for teachers and students that is integrated with Kahoot which is a quiz game application where each question refers to the learning material. This study aims to determine the effect of learning google classroom integrated with kahoot on the interest and learning outcomes of students in science subjects water cycle material. This is quantitative study with quasi-experimental research design (Quasi Experiment) with sample of 52 fifth grade students consisting of 26 class V A (experimental class) and 26 class V B (control class) at MI Miftahul Akhlaqiyah, Ngaliyan, Semarang City. Analysis using normality test, homogeneity, and independent t test. First, this learning can increase students' interest in learning where the calculation results are obtained t = 7.071; df= 50; p= 0.000. Second, this learning can improve learning outcomes where the calculation results are obtained t = 3.919; df= 50; p= 0.000. Thus, this learning can be used to increase student interest and learning outcomes.
Generating Personalized Behavioral Feedback for a Virtual Job Interview Training System Through Adversarial Learning

Job interviews are usually high-stakes social situations where professional and behavioral skills are required for a satisfactory outcome. In order to increase the chances of recruitment technological approaches have emerged to generate meaningful feedback for job candidates. We extended an interactive virtual job interview training system with a Generative Adversarial Network (GAN)-based approach that first detects behavioral weaknesses and subsequently generates personalized feedback. To evaluate the usefulness of the generated feedback, we conducted a mixed-methods pilot study using mock-ups from the job interview training system. The overall study results indicate that the GAN-based generated behavioral feedback is helpful. Moreover, participants assessed that the feedback would improve their job interview performance.
Optimal management of an active community for fair selection of electric vehicles in a V2G event

Smart Grids concept allows the development of new technologies, such as Vehicle to Grid (V2G) interaction, making it possible to transfer energy from an Electric Vehicle (EV) to the grid and vice versa, thus facilitating the implementation of electric mobility. However, the system&apos;s reliability and security can be jeopardized by emerging such uncertain players. To aid the complex management of active local communities and reduce the response uncertainty, the authors propose a methodology capable of dealing with both active consumers and EV by choosing the proper participants using a fairness model according to the context in which the events are triggered. Furthermore, the authors introduced a contextual remuneration for continuous participation, studying the impact from the Aggregator perspective.