Reviewerly/exHarmony huggingface dataset:
Scene Graph Parsing via Abstract Meaning Representation in Pre-trained Language Models

In this work, we propose the application of abstract meaning representation (AMR) based semantic parsing models to parse textual descriptions of a visual scene into scene graphs, which is the first work to the best of our knowledge.Previous works examined scene graph parsing from textual descriptions using dependency parsing and left the AMR parsing approach as future work since sophisticated methods are required to apply AMR.Hence, we use pre-trained AMR parsing models to parse the region descriptions of visual scenes (i.e.images) into AMR graphs and pre-trained language models (PLM), BART and T5, to parse AMR graphs into scene graphs.The experimental results show that our approach explicitly captures high-level semantics from textual descriptions of visual scenes, such as objects, attributes of objects, and relationships between objects.Our textual scene graph parsing approach outperforms the previous state-of-the-art results by 9.3% in the SPICE metric score.
UAlberta at SemEval 2022 Task 2: Leveraging Glosses and Translations for Multilingual Idiomaticity Detection

We describe the University of Alberta systems for the SemEval-2022 Task 2 on multilingual idiomaticity detection.Working under the assumption that idiomatic expressions are noncompositional, our first method integrates information on the meanings of the individual words of an expression into a binary classifier.Further hypothesizing that literal and idiomatic expressions translate differently, our second method translates an expression in context, and uses a lexical knowledge base to determine if the translation is literal.Our approaches are grounded in linguistic phenomena, and leverage existing sources of lexical knowledge.Our results offer support for both approaches, particularly the former.
TCU at SemEval-2022 Task 8: A Stacking Ensemble Transformer Model for Multilingual News Article Similarity

Previous studies focus on measuring the degree of similarity of texts by using traditional machine learning methods, such as Support Vector Regression (SVR).Based on Transformers, this paper describes our contribution to SemEval-2022 Task 8 Multilingual News Article Similarity.The similarity of multilingual news articles requires a regression prediction on the similarity of multilingual articles, rather than a classification for judging text similarity.This paper mainly describes the architecture of the model and how to adjust the parameters in the experiment and strengthen the generalization ability.In this paper, we implement and construct different models through transformer-based models.We applied different transformer-based models, as well as ensemble them together by using ensemble learning.To avoid the overfit, we focus on the adjustment of parameters and the increase of generalization ability in our experiments.In the last submitted contest, we achieve a score of 0.715 and rank the 21st place.
Trajectory tracking control of mobile robot based on interval type II fuzzy sliding mode

Aiming at the trajectory tracking problem of mobile robot with typical nonlinear characteristics, a method combining interval type-2 fuzzy control and sliding mode control is proposed to track its trajectory. By properly selecting the sliding surface and reaching law of sliding mode control, the system can quickly approach and converge to the equilibrium state in a certain time. The interval type II fuzzy control method is used to adjust the reaching law parameters in real time to deal with unknown interference, so that the system has good resistance to external interference. The stability is proved by Lyapunov function. Finally, the simulation results show that the proposed method can track the trajectory of mobile robot better and have better motion quality under the action of unknown interference.
DABANGG: A Case for Noise Resilient Flush-Based Cache Attacks

Flush-based cache attacks like Flush+Reload and Flush+Flush are highly precise and effective. Most of the flush-based attacks provide high accuracy in controlled and isolated environments where attacker and victim share OS pages. However, we observe that these attacks are prone to low accuracy on a noisy multi-core system with co-running applications. Two root causes for the varying accuracy of flush-based attacks are: (i) the dynamic nature of core frequencies that fluctuate depending on the system load, and (ii) the relative placement of victim and attacker threads in the processor, like same or different physical cores. These dynamic factors critically affect the execution latency of key instructions like clflush and mov, rendering the pre-attack calibration step ineffective.We propose DABANGG, a set of novel refinements to make flush-based attacks resilient to system noise by making them aware of frequency and thread placement. First, we introduce pre-attack calibration that is aware of instruction latency variation. Second, we use low-cost attack-time optimizations like fine-grained busy waiting and periodic feedback about the latency thresholds to improve the effectiveness of the attack. Finally, we provide victim-specific parameters that significantly improve the attack accuracy. We evaluate DABANGG-enabled Flush+Reload and Flush+Flush attacks against the standard attacks in side-channel and covert-channel experiments with varying levels of compute, memory, and IO-intensive system noise. In all scenarios, DABANGG+Flush+Reload and DABANGG+Flush+Flush outperform the standard attacks in stealth and accuracy.
Hitachi at SemEval-2022 Task 10: Comparing Graph- and Seq2Seq-based Models Highlights Difficulty in Structured Sentiment Analysis

This paper describes our participation in SemEval-2022 Task 10, a structured sentiment analysis.In this task, we have to parse opinions considering both structure-and contextdependent subjective aspects, which is different from typical dependency parsing.Some of the major parser types have recently been used for semantic and syntactic parsing, while it is still unknown which type can capture structured sentiments well due to their subjective aspects.To this end, we compared two different types of state-of-the-art parser, namely graph-based and seq2seq-based.Our in-depth analyses suggest that, even though graph-based parser generally outperforms the seq2seq-based one, with strong pre-trained language models both parsers can essentially output acceptable and reasonable predictions.The analyses highlight that the difficulty derived from subjective aspects in structured sentiment analysis remains an essential challenge.
I2C at SemEval-2022 Task 5: Identification of misogyny in internet memes

In this paper we present our approach and system description on Task 5 A in MAMI: Multimedia Automatic Misogyny Identification.In our experiments we compared several architectures based on deep learning algorithms with various other approaches to binary classification using Transformers, combined with a nudity image detection algorithm to provide better results.With this approach, we achieved a test accuracy of 0.665.
Modal Dependency Parsing via Language Model Priming

The task of modal dependency parsing aims to parse a text into its modal dependency structure, which is a representation for the factuality of events in the text.We design a modal dependency parser that is based on priming pre-trained language models, and evaluate the parser on two data sets.Compared to baselines, we show an improvement of 2.6% in F-score for English and 4.6% for Chinese.To the best of our knowledge, this is also the first work on Chinese modal dependency parsing.
TIB-VA at SemEval-2022 Task 5: A Multimodal Architecture for the Detection and Classification of Misogynous Memes

The detection of offensive, hateful content on social media is a challenging problem that affects many online users on a daily basis.Hateful content is often used to target a group of people based on ethnicity, gender, religion and other factors.The hate or contempt toward women has been increasing on social platforms.Misogynous content detection is especially challenging when textual and visual modalities are combined to form a single context, e.g., an overlay text embedded on top of an image, also known as meme.In this paper, we present a multimodal architecture that combines textual and visual features to detect misogynous memes.The proposed architecture is evaluated in the SemEval-2022 Task 5: MAMI -Multimedia Automatic Misogyny Identification challenge under the team name TIB-VA.We obtained the best result in the Task-B where the challenge is to classify whether a given document is misogynous and further identify the following sub-classes: shaming, stereotype, objectification, and violence.
Non-Autoregressive Chinese ASR Error Correction with Phonological Training

Automatic Speech Recognition (ASR) is an efficient and widely used input method that transcribes speech signals into text.As the errors introduced by ASR systems will impair the performance of downstream tasks, we introduce a post-processing error correction method, PhVEC, to correct errors in text space.For the errors in ASR result, existing works mainly focus on fixed-length corrections, modifying each wrong token to a correct one (one-to-one correction), but rarely consider the variablelength correction (one-to-many or many-to-one correction).In this paper, we propose an efficient non-autoregressive (NAR) method for Chinese ASR error correction for both cases.Instead of conventionally predicting the sentence length in NAR methods, we propose a novel approach that uses phonological tokens to extend the source sentence for variable-length correction, enabling our model to generate phonetically similar corrections.Experimental results on datasets of different domains show that our method achieves significant improvement in word error rate reduction and speeds up the inference by 6.2 times compared with the autoregressive model.
TurkishDelightNLP: A Neural Turkish NLP Toolkit

We introduce a neural Turkish NLP toolkit called TurkishDelightNLP that performs computational linguistic analyses from morphological level to semantic level that involves tasks such as stemming, morphological segmentation, morphological tagging, part-of-speech tagging, dependency parsing, and semantic parsing, as well as high-level NLP tasks such as named entity recognition.We publicly share the open-source Turkish NLP toolkit through a web interface that allows an input text to be analysed in real-time, as well as the open source implementation of the components provided in the toolkit, an API, and several annotated datasets such as word similarity test set to evaluate word embeddings and UCCA-based semantic annotation in Turkish.This will be the first open-source Turkish NLP toolkit that involves a range of NLP tasks in all levels.We believe that it will be useful for other researchers in Turkish NLP and will be also beneficial for other high-level NLP tasks in Turkish.
‘Meet me at the ribary’ – Acceptability of spelling variants in free-text answers to listening comprehension prompts

When listening comprehension is tested as a free-text production task, a challenge for scoring the answers is the resulting wide range of spelling variants.When judging whether a variant is acceptable or not, human raters perform a complex holistic decision.In this paper, we present a corpus study in which we analyze human acceptability decisions in a high stakes test for German.We show that for human experts, spelling variants are harder to score consistently than other answer variants.Furthermore, we examine how the decision can be operationalized using features that could be applied by an automatic scoring system.We show that simple measures like edit distance and phonetic similarity between a given answer and the target answer can model the human acceptability decisions with the same inter-annotator agreement as humans, and discuss implications of the remaining inconsistencies.
Multi-Domain Targeted Sentiment Analysis

Targeted Sentiment Analysis (TSA) is a central task for generating insights from consumer reviews.Such content is extremely diverse, with sites like Amazon or Yelp containing reviews on products and businesses from many different domains.A real-world TSA system should gracefully handle that diversity.This can be achieved by a multi-domain model -one that is robust to the domain of the analyzed texts, and performs well on various domains.To address this scenario, we present a multi-domain TSA system based on augmenting a given training set with diverse weak labels from assorted domains.These are obtained through self-training on the YELP reviews corpus.Extensive experiments with our approach on three evaluation datasets across different domains demonstrate the effectiveness of our solution.We further analyze how restrictions imposed on the available labeled data affect the performance, and compare the proposed method to the costly alternative of manually gathering diverse TSA labeled data.Our results and analysis show that our approach is a promising step towards a practical domain-robust TSA system.
On Systematic Style Differences between Unsupervised and Supervised MT and an Application for High-Resource Machine Translation

Modern unsupervised machine translation (MT) systems reach reasonable translation quality under clean and controlled data conditions.As the performance gap between supervised and unsupervised MT narrows, it is interesting to ask whether the different training methods result in systematically different output beyond what is visible via quality metrics like adequacy or BLEU.We compare translations from supervised and unsupervised MT systems of similar quality, finding that unsupervised output is more fluent and more structurally different in comparison to human translation than is supervised MT.We then demonstrate a way to combine the benefits of both methods into a single system which results in improved adequacy and fluency as rated by human evaluators.Our results open the door to interesting discussions about how supervised and unsupervised MT might be different yet mutually-beneficial.
UPB at SemEval-2022 Task 5: Enhancing UNITER with Image Sentiment and Graph Convolutional Networks for Multimedia Automatic Misogyny Identification

In recent times, the detection of hate-speech, offensive, or abusive language in online media has become an important topic in NLP research due to the exponential growth of social media and the propagation of such messages, as well as their impact.Misogyny detection, even though it plays an important part in hatespeech detection, has not received the same attention.In this paper, we describe our classification systems submitted to the SemEval-2022 Task 5: MAMI -Multimedia Automatic Misogyny Identification.The shared task aimed to identify misogynous content in a multi-modal setting by analysing meme images together with their textual captions.To this end, we propose two models based on the pre-trained UNITER model, one enhanced with an image sentiment classifier, whereas the second leverages a Vocabulary Graph Convolutional Network (VGCN).Additionally, we explore an ensemble using the aforementioned models.Our best model reaches an F1-score of 71.4% in Sub-task A and 67.3% for Sub-task B positioning our team in the upper third of the leaderboard.We release the code and experiments for our models on GitHub 1 .
StereoKG: Data-Driven Knowledge Graph Construction For Cultural Knowledge and Stereotypes

Analyzing ethnic or religious bias is important for improving fairness, accountability, and transparency of natural language processing models.However, many techniques rely on human-compiled lists of bias terms, which are expensive to create and are limited in coverage.In this study, we present a fully datadriven pipeline for generating a knowledge graph (KG) of cultural knowledge and stereotypes.Our resulting KG covers 5 religious groups and 5 nationalities and can easily be extended to include more entities.Our human evaluation shows that the majority (59.2%) of non-singleton entries are coherent and complete stereotypes.We further show that performing intermediate masked language model training on the verbalized KG leads to a higher level of cultural awareness in the model and has the potential to increase classification performance on knowledge-crucial samples on a related task, i.e., hate speech detection.
Are You Really Okay? A Transfer Learning-based Approach for Identification of Underlying Mental Illnesses

Evidence has demonstrated the presence of similarities in language use across people with various mental health conditions.In this work we investigate these relationships both as described in literature and as a data analysis problem.We also introduce a novel transfer learning based approach that learns from linguistic feature spaces of previous conditions and predicts unknown ones.Our model achieves strong performance, with F 1 scores of 0.75, 0.80, and 0.76 at detecting depression, stress, and suicidal ideation in a first-of-its-kind transfer task and offering promising evidence that language models can harness learned patterns from known mental health conditions to aid in their prediction of others that may lie latent.
Unsupervised Domain Adaptation for Question Generation with DomainData Selection and Self-training

Question generation (QG) approaches based on large neural models require (i) large-scale and (ii) high-quality training data.These two requirements pose difficulties for specific application domains where training data is expensive and difficult to obtain.The trained QG models' effectiveness can degrade significantly when they are applied on a different domain due to domain shift.In this paper, we explore an unsupervised domain adaptation approach to combat the lack of training data and domain shift issue with domain data selection and self-training.We first present a novel answer-aware strategy for domain data selection to select data with the most similarity to a new domain.The selected data are then used as pseudo in-domain data to retrain the QG model.We then present generation confidenceguided self-training with two generation confidence modeling methods: (i) generated questions' perplexity and (ii) the fluency score.We test our approaches on three large public datasets with different domain similarities, using a transformer-based pre-trained QG model.The results show that our proposed approaches outperform the baselines, and show the viability of unsupervised domain adaptation with answer-aware data selection and self-training on the QG task.The code is available at https://github.com/zpeide/transfer_qg.
Fewer Errors, but More Stereotypes? The Effect of Model Size on Gender Bias

The size of pretrained models is increasing, and so is their performance on a variety of NLP tasks.However, as their memorization capacity grows, they might pick up more social biases.In this work, we examine the connection between model size and its gender bias (specifically, occupational gender bias).We measure bias in three masked language model families (RoBERTa, DeBERTa, and T5) in two setups: directly using prompt based method, and using a downstream task (Winogender).We find on the one hand that larger models receive higher bias scores on the former task, but when evaluated on the latter, they make fewer gender errors.To examine these potentially conflicting results, we carefully investigate the behavior of the different models on Winogender.We find that while larger models outperform smaller ones, the probability that their mistakes are caused by gender bias is higher.Moreover, we find that the proportion of stereotypical errors compared to anti-stereotypical ones grows with the model size.Our findings highlight the potential risks that can arise from increasing model size. 1
Web Design of TPQ Zahrotul Jannah through Digital Marketing Strategy

Al-Qur'an Education Park (TPQ) Zahrotul Jannah is an educational institution located in Sidoarjo which has a private Al-Qur'an learning program for all ages with the advantage of flexible time and place.The number of students studying at TPQ has not yet reached the expected target because they do not have advertising media.Based on this, advertising media that has the advantage of being easily accessible and capable of displaying complete, fast and targeted information is needed as a means of promoting TPQ.Digital marketing advertising media in the form of a website was chosen because it has the necessary advantages.To increase the number of website visits through search engines, On-Page and Off-Page SEO optimization techniques are needed.
An Analysis of Students’ Difficulties in Reading Comprehension

This study was aimed to find out the students’ difficulties in learning reading comprehension at a public high school in Bangka and the factors students have the difficulties. The subject of this study were the students of eighth grade in Bangka. Descriptive qualitative method was used. After the students’ test scores were obtained, then the students’ difficulties were analyzed. After that, the number of difficulties was counted by tabulating and calculating the results. The difficulties faced by the students were difficulty in answering main idea questions (53,67%), vocabulary questions (47,84%), and making inference questions (45,34%). Four factors were found causing the students’ difficulties, those are students’ interest in learning reading because they do not have the desire to read English text and they are not interested in reading. The second factor is students’ difficulties in reading text and translating the English meaning. The third factor is lack of facility in their school since it did not provide enough reading material for them. And the last factor is house environment, the role of a parent at home to give child support is crucial.
NLP4NLP+5: The Deep (R)evolution in Speech and Language Processing

This paper aims at analyzing the changes in the fields of speech and natural language processing over the recent past 5 years (2016–2020). It is in continuation of a series of two papers that we published in 2019 on the analysis of the NLP4NLP corpus, which contained articles published in 34 major conferences and journals in the field of speech and natural language processing, over a period of 50 years (1965–2015), and analyzed with the methods developed in the field of NLP, hence its name. The extended NLP4NLP+5 corpus now covers 55 years, comprising close to 90,000 documents [+30% compared with NLP4NLP: as many articles have been published in the single year 2020 than over the first 25 years (1965–1989)], 67,000 authors (+40%), 590,000 references (+80%), and approximately 380 million words (+40%). These analyses are conducted globally or comparatively among sources and also with the general scientific literature, with a focus on the past 5 years. It concludes in identifying profound changes in research topics as well as in the emergence of a new generation of authors and the appearance of new publications around artificial intelligence, neural networks, machine learning, and word embedding.