Reviewerly/exHarmony huggingface dataset:
Document-Level Event Argument Extraction by Leveraging Redundant Information and Closed Boundary Loss

In document-level event argument extraction, an argument is likely to appear multiple times in different expressions in the document.The redundancy of arguments underlying multiple sentences is beneficial but is often overlooked.In addition, in event argument extraction, most entities are regarded as class "others", i.e.Universum class, which is defined as a collection of samples that do not belong to any class of interest.Universum class is composed of heterogeneous entities without typical common features.Classifiers trained by cross entropy loss could easily misclassify the Universum class because of their open decision boundary.In this paper, to make use of redundant event information underlying a document, we build an entity coreference graph with the graph2token module to produce a comprehensive and coreference-aware representation for every entity and then build an entity summary graph to merge the multiple extraction results.To better classify Universum class, we propose a new loss function to build classifiers with closed boundaries.Experimental results show that our model outperforms the previous state-of-the-art models by 3.35% in F1-score.
Few-shot Subgoal Planning with Language Models

Pre-trained language models have shown successful progress in many text understanding benchmarks.This work explores the capability of these models to predict actionable plans in real-world environments.Given a text instruction, we show that language priors encoded in pre-trained models allow us to infer finegrained subgoal sequences.In contrast to recent methods which make strong assumptions about subgoal supervision, our experiments show that language models can infer detailed subgoal sequences from few training sequences without any fine-tuning.We further propose a simple strategy to re-rank language model predictions based on interaction and feedback from the environment.Combined with pretrained navigation and visual reasoning components, our approach demonstrates competitive performance on subgoal prediction and task completion in the ALFRED benchmark compared to prior methods that assume more subgoal supervision.
Defending Compositionality in Emergent Languages

Compositionality has traditionally been understood as a major factor in productivity of language and, more broadly, human cognition.Yet, recently, some research started to question its status, showing that artificial neural networks are good at generalization even without noticeable compositional behavior.We argue that some of these conclusions are too strong and/or incomplete.In the context of a twoagent communication game, we show that compositionality indeed seems essential for successful generalization when the evaluation is done on a proper dataset.
Impact of Training Instance Selection on Domain-Specific Entity Extraction using BERT

State of the art performances for entity extraction tasks are achieved by supervised learning, specifically, by fine-tuning pretrained language models such as BERT.As a result, annotating application specific data is the first step in many use cases.However, no practical guidelines are available for annotation requirements.This work supports practitioners by empirically answering the frequently asked questions (1) how many training samples to annotate?( 2) which examples to annotate?We found that BERT achieves up to 80% F1 when fine-tuned on only 70 training examples, especially on biomedical domain.The key features for guiding the selection of high performing training instances are identified to be pseudo-perplexity and sentencelength.The best training dataset constructed using our proposed selection strategy shows F1 score that is equivalent to a random selection with twice the sample size.The requirement of only a small number of training data implies cheaper implementations and opens door to wider range of applications.
On Curriculum Learning for Commonsense Reasoning

Commonsense reasoning tasks follow a standard paradigm of finetuning pretrained language models on the target task data, where samples are introduced to the model in a random order during training.However, recent research suggests that data order can have a significant impact on the performance of finetuned models for natural language understanding.Hence, we examine the effect of a humanlike easy-to-difficult curriculum during finetuning of language models for commonsense reasoning tasks.We use paced curriculum learning to rank data and sample training mini-batches with increasing levels of difficulty from the ranked dataset during finetuning.Further, we investigate the effect of an adaptive curriculum, i.e., the data ranking is dynamically updated during training based on the current state of the learner model.We use a teacher model to measure difficulty of each sample and experiment with three measures based on question answering probability, variability and out-ofdistribution.To understand the effectiveness of curriculum learning in various scenarios, we apply it on full model fine-tuning as well as parameter-efficient prompt-tuning settings.Our results show that fixed as well as adaptive curriculum learning significantly improve performance for five commonsense reasoning tasks, i.e., SocialIQA, CosmosQA, CODAH, HellaSwag, WinoGrande in both tuning settings.Further, we find that prioritizing the difficult samples in the tail end of training improves generalization to unseen in-domain data as well as out-of-domain data.Our work provides evidence and encourages research into curriculum learning for commonsense reasoning. 1.
HateU at SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification

Hate speech expressions in social media are not limited to textual messages; they can appear in videos, images, or multimodal formats like memes.Existing work towards detecting such expressions has been conducted almost exclusively over textual content, and the analysis of pictures and videos has been very scarce.This paper describes our team proposal in the Multimedia Automatic Misogyny Identification (MAMI) task at SemEval 2022.The challenge consisted of identifying misogynous memes from a dataset where images and text transcriptions were provided.We reported a 71% of F-score using a multimodal system based on the CLIP model.
Cleansing &amp; expanding the HURTLEX(el) with a multidimensional categorization of offensive words

We present a cleansed version of the Modern Greek branch of the multilingual lexicon HURTLEX. 1 The new version contains 737 offensive words.We worked bottom-up in two annotation rounds and developed detailed diagnostics of "offensiveness" by cross-classifying words on three dimensions: context, reference, and thematic domain.Our work reveals a wider spectrum of thematic domains concerning the study of offensive language than those identified in the Greek lexicographic literature as well as social and cultural aspects that are not included in the original HURTLEX categories.
LM-CORE: Language Models with Contextually Relevant External Knowledge

Large transformer-based pre-trained language models have achieved impressive performance on a variety of knowledge-intensive tasks and can capture factual knowledge in their parameters.We argue that storing large amounts of knowledge in the model parameters is suboptimal given the ever-growing amounts of knowledge and resource requirements.We posit that a more efficient alternative is to provide explicit access to contextually relevant structured knowledge to the model and train it to use that knowledge.We present LM-CORE -a general framework to achieve this-that allows decoupling of the language model training from the external knowledge source and allows the latter to be updated without affecting the already trained model.Experimental results show that LM-CORE, having access to external knowledge, achieves significant and robust outperformance over state-of-the-art knowledgeenhanced language models on knowledge probing tasks; can effectively handle knowledge updates; and performs well on two downstream tasks.We also present a thorough error analysis highlighting the successes and failures of LM-CORE.Our code and model checkpoints are publicly available 1 .
Zero-shot cross-lingual open domain question answering

People speaking different kinds of languages search for information in a cross-lingual manner.They tend to ask questions in their language and expect the answer to be in the same language, despite the evidence lying in another language.In this paper, we present our approach for this task of cross-lingual opendomain question-answering.Our proposed method employs a passage reranker, the fusionin-decoder technique for generation, and a wiki data entity-based post-processing system to tackle the inability to generate entities across all languages.Our end-2-end pipeline shows an improvement of 3 and 4.6 points on F1 and EM metrics respectively, when compared with the baseline CORA model on the XOR-TyDi dataset.We also evaluate the effectiveness of our proposed techniques in the zero-shot setting using the MKQA dataset and show an improvement of 5 points in F1 for high-resource and 3 points improvement for low-resource zero-shot languages.Our team, CMUmQA's submission in the MIA-Shared task ranked 1st in the constrained setup for the dev and 2nd in the test setting.
Improving Generalization of Hate Speech Detection Systems to Novel Target Groups via Domain Adaptation

Despite recent advances in machine learning based hate speech detection, classifiers still struggle with generalizing knowledge to outof-domain data samples.In this paper, we investigate the generalization capabilities of deep learning models to different target groups of hate speech under clean experimental settings.Furthermore, we assess the efficacy of three different strategies of unsupervised domain adaptation to improve these capabilities.Given the diversity of hate and its rapid dynamics in the online world (e.g. the evolution of new target groups like virologists during the COVID-19 pandemic), robustly detecting hate aimed at newly identified target groups is a highly relevant research question.We show that naively trained models suffer from a target group specific bias, which can be reduced via domain adaptation.We were able to achieve a relative improvement of the F1-score between 5.8% and 10.7% for out-of-domain target groups of hate speech compared to baseline approaches by utilizing domain adaptation.
silpa_nlp at SemEval-2022 Tasks 11: Transformer based NER models for Hindi and Bangla languages

We present Transformer based pretrained models, which are fine-tuned for Named Entity Recognition (NER) task.Our team participated in SemEval-2022 Task 11 Multi-CoNER: Multilingual Complex Named Entity Recognition task for Hindi and Bangla.Result comparison of six models (mBERT, IndicBERT, MuRIL (Base), MuRIL (Large), XLM-RoBERTa (Base) and XLM-RoBERTa (Large) ) has been performed.It is found that among these models MuRIL (Large) model performs better for both the Hindi and Bangla languages.Its F1-Scores for Hindi and Bangla are 0.69 and 0.59 respectively.
CRUSH: Contextually Regularized and User anchored Self-supervised Hate speech Detection

The last decade has witnessed a surge in the interaction of people through social networking platforms.While there are several positive aspects of these social platforms, their proliferation has led them to become the breeding ground for cyber-bullying and hate speech.Recent advances in NLP have often been used to mitigate the spread of such hateful content.Since the task of hate speech detection is usually applicable in the context of social networks, we introduce CRUSH, a framework for hate speech detection using User Anchored selfsupervision and contextual regularization.Our proposed approach secures ≈ 1-12% improvement in test set metrics over best performing previous approaches on two types of tasks and multiple popular English language social networking datasets.
Strong Heuristics for Named Entity Linking

Named entity linking (NEL) in news is a challenging endeavour due to the frequency of unseen and emerging entities, which necessitates the use of unsupervised or zero-shot methods.However, such methods tend to come with caveats, such as no integration of suitable knowledge bases (like Wikidata) for emerging entities, a lack of scalability, and poor interpretability.Here, we consider person disambiguation in QUOTEBANK, a massive corpus of speaker-attributed quotations from the news, and investigate the suitability of intuitive, lightweight, and scalable heuristics for NEL in web-scale corpora.Our best performing heuristic disambiguates 94% and 63% of the mentions on QUOTEBANK and the AIDA-CoNLL benchmark, respectively.Additionally, the proposed heuristics compare favourably to the state-of-the-art unsupervised and zero-shot methods, EIGENTHEMES and mGENRE, respectively, thereby serving as strong baselines for unsupervised and zero-shot entity linking.
“Zo Grof !”: A Comprehensive Corpus for Offensive and Abusive Language in Dutch

This paper presents a comprehensive corpus for the study of socially unacceptable language in Dutch.The corpus extends and revise an existing resource with more data and introduces a new annotation dimension for offensive language, making it a unique resource in the Dutch language panorama.Each language phenomenon (abusive and offensive language) in the corpus has been annotated with a multilayer annotation scheme modelling the explicitness and the target(s) of the abuse/offence in the message.We have conducted a new set of experiments with different classification algorithms on all annotation dimensions.Monolingual Pre-Trained Language Models prove as the best systems, obtaining a macro-average F1 of 0.828 for binary classification of offensive language, and 0.579 for the targets of offensive messages.Furthermore, the best system obtains a macro-average F1 of 0.637 for distinguishing between abusive and offensive messages.Table 10: DALC v2.0: distribution of the sources across
Data Augmentation with Dual Training for Offensive Span Detection

Recognizing offensive text is an important requirement for every content management system, especially for social networks.While the majority of the prior work formulate this problem as text classification, i.e., if a text excerpt is offensive or not, in this work we propose a novel model for offensive span detection (OSD), whose goal is to identify the spans responsible for the offensive tone of the text.One of the challenges to train a model for this novel setting is the lack of enough training data.To address this limitation, in this work we propose a novel method in which the large-scale pretrained language model GPT-2 is employed to generate synthetic training data for OSD.In particular, we propose to train the GPT-2 model in a dual-training setting using the REINFORCE algorithm to generate in-domain, natural and diverse training samples.Extensive experiments on the benchmark dataset for OSD reveal the effectiveness of the proposed method.
Leveraging Dependency Grammar for Fine-Grained Offensive Language Detection using Graph Convolutional Networks

The last few years have witnessed an exponential rise in the propagation of offensive text on social media.Identification of this text with high precision is crucial for the well-being of society.Most of the existing approaches tend to give high toxicity scores to innocuous statements (e.g., "I am a gay man").These false positives result from over-generalization on the training data where specific terms in the statement may have been used in a pejorative sense (e.g., "gay").Emphasis on such words alone can lead to discrimination against the classes these systems are designed to protect.In this paper, we address the problem of offensive language detection on Twitter, while also detecting the type and the target of the offense.We propose a novel approach called SyLSTM, which integrates syntactic features in the form of the dependency parse tree of a sentence and semantic features in the form of word embeddings into a deep learning architecture using a Graph Convolutional Network.Results show that the proposed approach significantly outperforms the state-of-the-art BERT model with orders of magnitude fewer number of parameters.
SIGMORPHON 2022 Shared Task on Morpheme Segmentation Submission Description: Sequence Labelling for Word-Level Morpheme Segmentation

We propose a sequence labelling approach to word-level morpheme segmentation.Segmentation labels are edit operations derived from a modified minimum edit distance alignment.We show that sequence labelling performs well for "shallow segmentation" and "canonical segmentation", achieving 96.06 f1 score (macroaveraged over all languages in the shared task) and ranking 3rd among all participating teams.Therefore, we conclude that sequence labelling is a promising approach to morpheme segmentation.
Prompt Augmented Generative Replay via Supervised Contrastive Learning for Lifelong Intent Detection

Identifying all possible user intents for a dialog system at design time is challenging even for skilled domain experts.For practical applications, novel intents may have to be inferred incrementally on the fly.This typically entails repeated retraining of the intent detector on both the existing and novel intents which can be expensive and would require storage of all past data corresponding to prior intents.In this paper, the objective is to continually train an intent detector on new intents while maintaining performance on prior intents without mandating access to prior intent data.Several data replay-based approaches have been introduced to avoid catastrophic forgetting during continual learning, including exemplar and generative replay.Current generative replay approaches struggle to generate representative samples because the generation is conditioned solely on the class/task label.Motivated by the recent work around prompt-based generation via pretrained language models (PLMs), we employ generative replay using PLMs for incremental intent detection.Unlike exemplar replay, we only store the relevant contexts per intent in memory and use these stored contexts (with the class label) as prompts for generating intentspecific utterances.We use a common model for both generation and classification to promote optimal sharing of knowledge across both tasks.To further improve generation, we employ supervised contrastive fine-tuning of the PLM.Our proposed approach achieves stateof-the-art (SOTA) for lifelong intent detection on four public datasets and even outperforms exemplar replay-based approaches.The technique also achieves SOTA on a lifelong relation extraction task, suggesting that the approach is extendable to other continual learning tasks beyond intent detection.
On Building Spoken Language Understanding Systems for Low Resourced Languages

Spoken dialog systems are slowly becoming and integral part of the human experience due to their various advantages over textual interfaces.Spoken language understanding (SLU) systems are fundamental building blocks of spoken dialog systems.But creating SLU systems for low resourced languages is still a challenge.In a large number of low resourced language, we don't have access to enough data to build automatic speech recognition (ASR) technologies, which are fundamental to any SLU system.Also, ASR based SLU systems do not generalize to unwritten languages.In this paper, we present a series of experiments to explore extremely low-resourced settings where we perform intent classification with systems trained on as low as one data-point per intent and with only one speaker in the dataset.We also work in a low-resourced setting where we do not use language specific ASR systems to transcribe input speech, which compounds the challenge of building SLU systems to simulate a true low-resourced setting.We test our system on Belgian Dutch (Flemish) and English and find that using phonetic transcriptions to make intent classification systems in such low-resourced setting performs significantly better than using speech features.Specifically, when using a phonetic transcription based system over a feature based system, we see average improvements of 12.37% and 13.08% for binary and four-class classification problems respectively, when averaged over 49 different experimental settings.
Digital Literacy, Digital Culture and Digitalization in Europe

The main goal of the research is to analyse and disclose linkage between digital literacy, digital culture and digitalization. The literature review covers explanation of the main concepts of European culture and digital literacy and provides an overview of previous research on digital literacy and culture.
A Brief Survey on Weakly Supervised Semantic Segmentation

Semantic Segmentation is the process of assigning a label to every pixel in the image that share same semantic properties and stays a challenging task in computer vision. In recent years, and due to the large availability of training data the performance of semantic segmentation has been greatly improved by using deep learning techniques. A large number of novel methods have been proposed. However, in some crucial fields we can't assure sufficient data to learn a deep model and achieves high accuracy. This paper aims to provide a brief survey of research efforts on deep-learning-based semantic segmentation methods on limited labeled data and focus our survey on weakly-supervised methods. This survey is expected to familiarize readers with the progress and challenges of weakly supervised semantic segmentation research in the deep learning era and present several valuable growing research points in this field.
Leadership Styles on Employee Performance Among Supervisors of Petroleum Retailing Sector in Jordan: Employee Engagement as a Mediator

The purpose of this article aims to examine perceptions of employee engagement (EE) among petroleum retailing sector supervisors as a possible mediator between leadership styles (transformational, transactional, and laissez-faire) and employees’ performance (EP). We proposed a mediation model and postulated that leadership styles (LS) could increase perceptions of EE, which theoretically correspond to the three styles of leadership: transformational leadership (TSFL), transactional leadership (TSCL), and laissez-faire (LF). Subsequently, EE is linked to leadership styles and EP in keeping the extant body of social exchange theory (SET) research. In this study, a model developed based on survey research is used. Data was collected from 425 supervisors in the petroleum retailing sector in Jordan. The partial least squares (Smart-PLS) analysis is used to run the measurement and structural models. The findings show that leadership styles (transformational and transactional) are significantly and positively related to EP. In addition to the non-significantly relationship between LF leadership and EP. On the other hand, EE is not mediate the relationship between TSCL and EP.