Reviewerly/exHarmony huggingface dataset:
Parameter-efficient Continual Learning Framework in Industrial Real-time Text Classification System

Catastrophic forgetting is a challenge for model deployment in industrial real-time systems, which requires the model to quickly master a new task without forgetting the old one.Continual learning aims to solve this problem; however, it usually updates all the model parameters, resulting in extensive training times and the inability to deploy quickly.To address this challenge, we propose a parameter-efficient continual learning framework, in which efficient parameters are selected through an offline parameter selection strategy and then trained using an online regularization method.In our framework, only a few parameters need to be updated, which not only alleviates catastrophic forgetting, but also allows the model to be saved with the changed parameters instead of all parameters.Extensive experiments are conducted to examine the effectiveness of our proposal.We believe this paper will provide useful insights and experiences on developing deep learning-based online real-time systems.
Neural Networks in a Product of Hyperbolic Spaces

Machine learning in hyperbolic spaces has attracted much attention in natural language processing and many other fields.In particular, Hyperbolic Neural Networks (HNNs) have improved a wide variety of tasks, from machine translation to knowledge graph embedding.Although some studies have reported the effectiveness of embedding into the product of multiple hyperbolic spaces, HNNs have mainly been constructed in a single hyperbolic space, and their extension to product spaces has not been sufficiently studied.Therefore, we propose a novel method to extend a given HNN in a single space to a product of hyperbolic spaces.We apply our method to Hyperbolic Graph Convolutional Networks (HGCNs), extending several HNNs.Our model improved the graph node classification accuracy especially on datasets with tree-like structures.The results suggest that neural networks in a product of hyperbolic spaces can be more effective than in a single space in representing structural data.
FSD-10: A Dataset for Competitive Sports Content Analysis

Action recognition is an important and challenging problem in video analysis. Although the past decade has witnessed progress in action recognition with the development of deep learning, such process has been slow in competitive sports content analysis. To promote the research on action recognition from competitive sports video clips, we introduce a Figure Skating Dataset (FSD-10) for finegrained sports content analysis. To this end, we collect 1484 clips from the worldwide figure skating championships in 2017-2018, which consist of 10 different actions in men/ladies programs. Each clip is at a rate of 30 frames per second with resolution 1080 $\times$ 720. These clips are then annotated by experts in type, grade of execution, skater info, .etc. To build a baseline for action recognition in figure skating, we evaluate state-of-the-art action recognition methods on FSD-10. Motivated by the idea that domain knowledge is of great concern in sports field, we propose a keyframe based temporal segment network (KTSN) for classification and achieve remarkable performance. Experimental results demonstrate that FSD-10 is an ideal dataset for benchmarking action recognition algorithms, as it requires to accurately extract action motions rather than action poses. We hope FSD-10, which is designed to have a large collection of finegrained actions, can serve as a new challenge to develop more robust and advanced action recognition models.
Training on the Introduction of TOEFL (Test of English as Foreign Language) to High School Students in Bandung

The Test of English as a Foreign Language (TOEFL) is a type of language test of which results are often used as a reference for new student admission in Universities. However, this test is not included in the English language learning curriculum at the secondary level so high school students are not familiar with it. Therefore, this community service activity aims at introducing the test to middle-level students so that they have knowledge and are familiar with the types of questions tested in this test. To determine the progress of students' knowledge and abilities, pre and post-tests were conducted, and the result was the improvement of the scores indicating that the learning approach chosen in this activity is in accordance with the objective of the training. In addition, a simple information system for test assessment was created using Microsoft Excel. This information system helps record student grades and makes it easier to convert TOEFL scores.
FORECASTING OF CURRENCY CIRCULATION IN INDONESIA USING HYBRID EXTREME LEARNING MACHINE

Forecasting currency circulation, including inflow and outflow, is one of Bank Indonesia's strategies to maintain the Rupiah value's stability. The characteristic of inflow and outflow data is that they have seasonal variations. This study proposes a hybrid model by combining decomposition techniques and Extreme Learning Machine to overcome data that has seasonal variations. The forecasting results of the proposed model are compared with the original Extreme Learning Machine. The comparison results show that the forecasting results with the hybrid model have the smallest errors. Thus, the hybrid model can predict data with seasonal variations better than the original Extreme Learning Machine.
A Self-embedding Robust Watermarking Scheme for Image Authentication

A self-embedding robust watermarking scheme is designed in this paper. Construct a robust zero watermarking with the coefficients of three-level integer discrete wavelet transform (IWT) on the original image and self-embed into the original image to realize the blind authentication of digital images: Impose three-level IWT on the original image to get the low frequency sub-band (LL3); then, transform coefficients of LL3 into a binary image as the robust zero watermarking. After that, a self-embedding scheme is proposed to embed the constructed zero watermarking into the coefficients of U in the SVD on the low frequency sub-band (LL <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> ). In order to improve the robustness and security of the whole scheme, the zero watermarking is permutated and encrypted based on hyper-chaotic system. Experimental results have demonstrated the validity and efficiency of the scheme.
Path Planning of Electric Emergency Robot based on Analytic Hierarchy Process

Electric emergency robot can replace manual entry into power dangerous scene for emergency rescue operation. The rationality of robot path planning has a great impact on the timeliness and accuracy of its operation. A path planning method of emergency robot based on analytic hierarchy process, improved a * algorithm and gradient descent method is proposed. Firstly, the operation objectives in complex scenes are selected by analytic hierarchy process, and then the cost function of a * algorithm is improved by combining the hazard risk curve function and robot protection threshold to realize the global path planning focusing on risk, and then the local dynamic path planning in the movement process is completed based on the gradient descent method. Simulation results show that this method can independently select operation objectives, avoid operation risks and quickly reach the objectives, and effectively improve the timeliness and safety of power emergency operation.
CONTENT VALIDITY ANALYSIS OF ENGLISH SUMMATIVE TEST BASED ON THE JUNIOR HIGH SCHOOL SYLLABUS

The objective of this research is to find the empirical evidence of the English summative test administered to the students for the seventh grade at Junior High School of Unismuh Makassar that is appropriate with the indicators of reading and writing skills in the English syllabus and has good content validity. The method of this research is descriptive research. The data was collected by observing and analyzing the English summative test. To know the content validity of the test, this research used the comparative analytic method, that is, by comparing the contents of the test with the English syllabus focusing on reading and writing indicators. The finding of the research was that the test items in the English summative test in the odd semester for the seventh grade students of Junior High School of Unismuh Makassar were appropriate with the recommended indicators in reading and writing skills in the English syllabus. They cover 11/18 x 100% = 61.11%. This percentage obviously falls into the level of 56%-75%, which means sufficient. This research proved that items of the English summative test for the odd semester of the seventh grade students in the Junior High School of Unismuh Makassar have sufficient content validity. It showed that most of the materials of the English summative test are appropriate to the recommended English syllabus referring to reading and writing standard competence.
Concept Mapping Techniques to Support Learning Activities in TK Az-Zahra, Kabupaten Karawang

This Community Service (PKM) aims at analyzing teachers’ difficulties in making interesting concept maps in the learning process namely teachers’ lack of understanding of various applications for making concept maps of subject matter, teachers’ difficulties in writing mathematical symbols used in creating learning instruments, as well as inadequate quality in the process of implementing learning activities. This PKM is located at Az-Zahra Kindergarten, Karawang, West Java. Data were collected by means of observation, interviews, and Zoom. The method used by the PKM team to solve problems was to provide training activities on a scheduled basis for one semester. The material presented was the introduction to and use of Mind Map and Math Type application software. The results of this PKM for partners are teachers’ ability to create interesting concept maps and the improvement of learning activities in education units. While the output for the PKM team is the fulfillment of responsibility stated in the Tri Dharma that must be carried out by lecturers indicated by receiving certificate from a partner.
I2C at SemEval-2022 Task 6: Intended Sarcasm Detection on Social Networks with Deep Learning

In this paper we present our approach and system description on iSarcasmEval: a SemEval task for intended sarcasm detection on social networks.This derives from our participation in SubTask A: Given a text, determine whether it is sarcastic or non-sarcastic.In our approach to complete the task, a comparison of several machine learning and deep learning algorithms using two datasets was conducted.The model which obtained the highest values of F1-score was a BERT-base-cased model.With this one, an F1-score of 0.2451 for the sarcastic class in the evaluation process was achieved.Finally, our team reached the 30 th position.
TEAM-Atreides at SemEval-2022 Task 11: On leveraging data augmentation and ensemble to recognize complex Named Entities in Bangla

Biological and healthcare domains, artistic works, and organization names can all have nested, overlapping, discontinuous entity mentions that may be syntactically or semantically ambiguous in practice.Traditional sequence tagging algorithms are unable to recognize these complex mentions because they violate the assumptions upon which sequence tagging schemes are founded.In this paper, we describe our contribution to SemEval 2022 Task 11 on identifying such complex named entities.We leveraged an ensemble of ELECTRA-based models exclusively pretrained on the Bangla language with ELECTRA-based monolingual models pretrained on English to achieve competitive performance.Besides providing a system description, we also present the outcomes of our experiments on architectural decisions, dataset augmentations and post-competition findings.
UoR-NCL at SemEval-2022 Task 3: Fine-Tuning the BERT-Based Models for Validating Taxonomic Relations

In human languages, there are many presuppositional constructions that impose a constrain on the taxonomic relations between two nouns depending on their order.These constructions create a challenge in validating taxonomic relations in real-world contexts.In SemEval2022-Task3 Presupposed Taxonomies: Evaluating Neural Network Semantics (PreTENS), the organizers introduced a task regarding validating the taxonomic relations within a variety of presuppositional constructions.This task is divided into two subtasks: classification and regression.Each subtask contains three datasets in multiple languages, i.e., English, Italian and French.To tackle this task, this work proposes to fine-tune different BERT-based models pre-trained on different languages.According to the experimental results, the fine-tuned BERT-based models are effective compared to the baselines for classification.For regression, the fine-tuned models show promising performances with the possibility of improvement.
UMUTeam at SemEval-2022 Task 6: Evaluating Transformers for detecting Sarcasm in English and Arabic

In this manuscript we detail the participation of the UMUTeam in the iSarcasm shared task (SemEval-2022).This shared task is related to the identification of sarcasm in English and Arabic documents.Our team achieve in the first challenge, a binary classification task, a F1 score of the sarcastic class of 17.97 for English and 31.75 for Arabic.For the second challenge, a multi-label classification, our results are not recorded due to an unknown problem.Therefore, we report the results of each sarcastic mechanism with the validation split.For our proposal, several neural networks that combine language-independent linguistic features with pre-trained embeddings are trained.The embeddings are based on different schemes, such as word and sentence embeddings, and contextual and non-contextual embeddings.Besides, we evaluate different techniques for the integration of the feature sets, such as ensemble learning and knowledge integration.In general, our best results are achieved using the knowledge integration strategy.
IIITH at SemEval-2022 Task 5: A comparative study of deep learning models for identifying misogynous memes

This paper provides a comparison of different deep learning methods for identifying misogynous memes for SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification.In this task, we experiment with architectures in the identification of misogynous content in memes by making use of text and image-based information.The different deep learning methods compared in this paper are: (i) unimodal image or text models (ii) fusion of unimodal models (iii) multimodal transformers models and (iv) transformers further pretrained on a multimodal task.From our experiments, we found pretrained multimodal transformer architectures to strongly outperform the models involving fusion of representation from both the modalities.
UMass PCL at SemEval-2022 Task 4: Pre-trained Language Model Ensembles for Detecting Patronizing and Condescending Language

Patronizing and condescending language (PCL) is everywhere, but rarely is the focus on its use by media towards vulnerable communities.Accurately detecting PCL of this form is a difficult task due to limited labeled data and how subtle it can be.In this paper, we describe our system for detecting such language which was submitted to SemEval 2022 Task 4: Patronizing and Condescending Language Detection.Our approach uses an ensemble of pre-trained language models, data augmentation, and optimizing the threshold for detection.Experimental results on the evaluation dataset released by the competition hosts show that our work is reliably able to detect PCL, achieving an F1 score of 55.47% on the binary classification task and a macro F1 score of 36.25% on the fine-grained, multi-label detection task.
Compositional Generalization for Kinship Prediction through Data Augmentation

Transformer-based models have shown promising performance in numerous NLP tasks.However, recent work has shown the limitation of such models in showing compositional generalization, which requires models to generalize to novel compositions of known concepts.In this work, we explore two strategies for compositional generalization on the task of kinship prediction from stories: (1) data augmentation and (2) predicting and using intermediate structured representation (in form of kinship graphs).Our experiments show that data augmentation boosts generalization performance by around 20% on average relative to a baseline model from prior work not using these strategies.However, predicting and using intermediate kinship graphs leads to a deterioration in the generalization of kinship prediction by around 50% on average relative to models that only leverage data augmentation.
Generalizing Morphological Inflection Systems to Unseen Lemmas

This paper presents experiments on morphological inflection using data from the SIGMORPHON-UniMorph 2022 Shared Task 0: Generalization and Typologically Diverse Morphological Inflection.We present a transformer inflection system, which enriches the standard transformer architecture with reverse positional encoding and type embeddings.We further apply data hallucination and lemma copying to augment training data.We train models using a two-stage procedure: (1) We first train on the augmented training data using standard backpropagation and teacher forcing.(2) We then continue training with a variant of the scheduled sampling algorithm dubbed student forcing.Our system delivers competitive performance under the small and large data conditions on the shared task datasets.
RNRE-NLP at SemEval-2022 Task 4: Patronizing and Condescending Language Detection

An understanding of patronizing and condescending language detection is an important part of identifying and addressing discrimination and prejudice in various forms of communication.In this paper, we investigate several methods for detecting patronizing and condescending language in short statements as part of SemEval-2022 Task 4. For Task 1a, we investigate applying both lightweight (tree-based and linear) machine learning classification models and fine-tuned pre-trained large language models.Our final system achieves an F1-score of 0.4321, recall-score of 0.5016, and a precisionscore of 0.3795 (ranked 53 / 78) on Task 1a.
stce at SemEval-2022 Task 6: Sarcasm Detection in English Tweets

This paper describes the systematic approach applied in "SemEval-2022 Task 6 (iSarcas-mEval) : Intended Sarcasm Detection in English and Arabic".In particular, we illustrate the proposed system in detail for SubTask-A about determining a given text as sarcastic or non-sarcastic in English.We start with the training data from the officially released data and then experiment with different combinations of public datasets to improve the model generalization.Additional experiments conducted on the task demonstrate our strategies are effective in completing the task.Different transformerbased language models, as well as some popular plug-and-play proirs, are mixed into our system to enhance the model's robustness.Furthermore, statistical and lexical-based text features are mined to improve the accuracy of the sarcasm detection.Our final submission achieves an F1-score for the sarcastic class of 0.6052 on the official test set (the top 1 of the 43 teams in "SubTask-A-English" on the leaderboard).
Unsupervised Mitigating Gender Bias by Character Components: A Case Study of Chinese Word Embedding

Word embeddings learned from massive text collections have demonstrated significant levels of discriminative biases.However, debiasing on the Chinese language, one of the most spoken languages, has been less explored.Meanwhile, existing literature relies on manually created supplementary data, which is time-and energy-consuming.In this work, we propose the first Chinese Gender-neutral word Embedding model (CGE) based on Word2vec, which learns gender-neutral word embeddings without any labeled data.Concretely, CGE utilizes and emphasizes the rich feminine and masculine information contained in radicals, i.e., a kind of component in Chinese characters, during the training procedure.This consequently alleviates discriminative gender biases.Experimental results show that our unsupervised method outperforms the state-of-the-art supervised debiased word embedding models without sacrificing the functionality of the embedding model.
Distributional properties of political dogwhistle representations in Swedish BERT

Dogwhistles" are expressions intended by the speaker to have two messages: a sociallyunacceptable "in-group" message understood by a subset of listeners and a benign message intended for the out-group.We take the result of a word-replacement survey of the Swedish population intended to reveal how dogwhistles are understood, and we show that the difficulty of annotating dogwhistles is reflected in the separability of the space of a sentence-transformer Swedish BERT trained on general data. 1
Tweaking UD Annotations to Investigate the Placement of Determiners, Quantifiers and Numerals in the Noun Phrase

We describe a methodology to extract with finer accuracy word order patterns from texts automatically annotated with Universal Dependency (UD) trained parsers.We use the methodology to quantify the word order entropy of determiners, quantifiers and numerals in ten Indo-European languages, using UDparsed texts from a parallel corpus of prosaic texts.Our results suggest that the combinations of different UD annotation layers, such as UD Relations, Universal Parts of Speech and lemma, and the introduction of languagespecific lists of closed-category lemmata has the two-fold effect of improving the quality of analysis and unveiling hidden areas of variability in word order patterns.
How to be Helpful on Online Support Forums?

Internet forums such as Reddit offer people a platform to ask for advice when they encounter various issues at work, school or in relationships.Telling helpful comments apart from unhelpful comments to these advice-seeking posts can help people and dialogue agents to become more helpful in offering advice.We propose a dataset that contains both helpful and unhelpful comments in response to such requests.We then relate helpfulness to the closely related construct of empathy.Finally, we analyze the language features that are associated with helpful and unhelpful comments.
kpfriends at SemEval-2022 Task 2: NEAMER - Named Entity Augmented Multi-word Expression Recognizer

We present NEAMER - Named Entity Augmented Multi-word Expression Recognizer. This system is inspired by non-compositionality characteristics shared between Named Entity and Idiomatic Expressions. We utilize transfer learning and locality features to enhance idiom classification task. This system is our submission for SemEval Task 2: Multilingual Idiomaticity Detection and Sentence Embedding Subtask A OneShot shared task. We achieve SOTA with F1 0.9395 during post-evaluation phase. We also observe improvement in training stability. Lastly, we experiment with non-compositionality knowledge transfer, cross-lingual fine-tuning and locality features, which we also introduce in this paper.
Taxonomy Builder: a Data-driven and User-centric Tool for Streamlining Taxonomy Construction

An existing domain taxonomy for normalizing content is often assumed when discussing approaches to information extraction, yet often in real-world scenarios there is none.When one does exist, as the information needs shift, it must be continually extended.This is a slow and tedious task, and one that does not scale well.Here we propose an interactive tool that allows a taxonomy to be built or extended rapidly and with a human in the loop to control precision.We apply insights from text summarization and information extraction to reduce the search space dramatically, then leverage modern pretrained language models to perform contextualized clustering of the remaining concepts to yield candidate nodes for the user to review.We show this allows a user to consider as many as 200 taxonomy concept candidates an hour to quickly build or extend a taxonomy to better fit information needs.