RoBERTa

AbstractBERT (Devlin et al., 2018) and RoBERTa (Liu et al., 2019) has set a new state-of-the-art performance on sentence-pair regression tasks like semantic textual similarity (STS). However, it requires that …...

AbstractState-of-the-art models for multi-hop question answering typically augment large-scale language models like BERT with additional, intuitively useful capabilities such as named entity recognition, graph-based reasoning, and question decomposition. However, does …...

AbstractLexical substitution, i.e. generation of plausible words that can replace a particular target word in a given context, is an extremely powerful technology that can be used as a backbone …...

AbstractWe describe our system for SemEval-2020 Task 11 on Detection of Propaganda Techniques in News Articles. We developed ensemble models using RoBERTa-based neural architectures, additional CRF layers, transfer learning between …...

AbstractTraditional document similarity measures provide a coarse-grained distinction between similar and dissimilar documents. Typically, they do not consider in what aspects two documents are similar. This limits the granularity of …...

AbstractInnovations in annotation methodology have been a catalyst for Reading Comprehension (RC) datasets and models. One recent trend to challenge current RC models is to involve a model in the …...

AbstractWe present BlockBERT, a lightweight and efficient BERT model for better modeling long-distance dependencies. Our model extends BERT by introducing sparse block structures into the attention matrix to reduce both …...

AbstractThis paper describes BUT-FIT’s submission at SemEval-2020 Task 5: Modelling Causal Reasoning in Language: Detecting Counterfactuals. The challenge focused on detecting whether a given statement contains a counterfactual (Subtask 1) …...

AbstractPre-trained Transformers are now ubiquitous in natural language processing, but despite their high end-task performance, little is known empirically about whether they are calibrated. Specifically, do these models’ posterior probabilities …...

AbstractIn linguistics and cognitive science, Logical metonymies are defined as type clashes between an event-selecting verb and an entity-denoting noun (e.g. The editor finished the article), which are typically interpreted …...

AbstractThe current state-of-the-art task-oriented semantic parsing models use BERT or RoBERTa as pretrained encoders; these models have huge memory footprints. This poses a challenge to their deployment for voice assistants …...

AbstractWe release an urgency dataset that consists of English tweets relating to natural crises, along with annotations of their corresponding urgency status. Additionally, we release evaluation datasets for two low-resource …...

AbstractIntent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill. Few-shot learning is attracting much attention to …...

AbstractPretrained large-scale language models have increasingly demonstrated high accuracy on many natural language processing (NLP) tasks. However, the limited weight storage and computational speed on hardware platforms have impeded the …...

AbstractRecently, pre-trained language models such as BERT have shown state-of-the-art accuracies in text matching. When being applied to IR (or QA), the BERT-based matching models need to online calculate the …...

AbstractThis paper describes the Global Tone Communication Co., Ltd.’s submission of the WMT20 shared news translation task. We participate in four directions: English to (Khmer and Pashto) and (Khmer and …...

AbstractIn this paper, we show our system for SemEval-2020 task 11, where we tackle propaganda span identification (SI) and technique classification (TC). We investigate heterogeneous pre-trained language models (PLMs) such …...

AbstractTask-agnostic forms of data augmentation have proven widely effective in computer vision, even on pretrained models. In NLP similar results are reported most commonly for low data regimes, non-pretrained models, …...

AbstractMany recent studies have shown that for models trained on datasets for natural language inference (NLI), it is possible to make correct predictions by merely looking at the hypothesis while …...

AbstractWhile pretrained models such as BERT have shown large gains across natural language understanding tasks, their performance can be improved by further training the model on a data-rich intermediate task, …...

AbstractOne reason pretraining on self-supervised linguistic tasks is effective is that it teaches models features that are helpful for language understanding. However, we want pretrained models to learn not only …...

AbstractThis paper presents two different systems for the SemEval shared task 7 on Assessing Humor in Edited News Headlines, sub-task 1, where the aim was to estimate the intensity of …...

AbstractPretrained masked language models (MLMs) require finetuning for most NLP tasks. Instead, we evaluate MLMs out of the box via their pseudo-log-likelihood scores (PLLs), which are computed by masking tokens …...

AbstractWe present an efficient method of utilizing pretrained language models, where we learn selective binary masks for pretrained weights in lieu of modifying them through finetuning. Extensive evaluations of masking …...

AbstractDeepfake detection, the task of automatically discriminating machine-generated text, is increasingly critical with recent advances in natural language generative models. Existing approaches to deepfake detection typically represent documents with coarse-grained …...

AbstractThis paper describes our submissions to SemEval 2020 Task 11: Detection of Propaganda Techniques in News Articles for each of the two subtasks of Span Identification and Technique Classification. We …...

AbstractFine-tuning pre-trained contextualized embedding models has become an integral part of the NLP pipeline. At the same time, probing has emerged as a way to investigate the linguistic knowledge captured …...

AbstractWe conduct a thorough study to diagnose the behaviors of pre-trained language encoders (ELMo, BERT, and RoBERTa) when confronted with natural grammatical errors. Specifically, we collect real grammatical errors from …...

AbstractThe success of large pretrained language models (LMs) such as BERT and RoBERTa has sparked interest in probing their representations, in order to unveil what types of knowledge they implicitly …...

AbstractIn the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited and has not been …...

AbstractBidirectional Encoder Representations from Transformers (BERT) has shown marvelous improvements across various NLP tasks, and consecutive variants have been proposed to further improve the performance of the pre-trained language models. …...

AbstractThis paper describes our system (Solomon) details and results of participation in the SemEval 2020 Task 11 ”Detection of Propaganda Techniques in News Articles”. We participated in Task ”Technique Classification” …...

AbstractThere has been significant progress in recent years in the field of Natural Language Processing thanks to the introduction of the Transformer architecture. Current state-of-the-art models, via a large number …...

AbstractWe introduce a novel approach to transformers that learns hierarchical representations in multiparty dialogue. First, three language modeling tasks are used to pre-train the transformers, token- and utterance-level language modeling …...

AbstractIn this paper, we built several pre-trained models to participate SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media. In the common task of Offensive Language Identification in Social …...

AbstractEvidence retrieval is a critical stage of question answering (QA), necessary not only to improve performance, but also to explain the decisions of the QA method. We introduce a simple, …...

AbstractSemEval Task 4 Commonsense Validation and Explanation Challenge is to validate whether a system can differentiate natural language statements that make sense from those that do not make sense. Two …...

AbstractOffensive language detection is one of the most challenging problem in the natural language processing field, being imposed by the rising presence of this phenomenon in online social media. This …...

AbstractA false contract is more likely to be rejected than a contract is, yet a false key is less likely than a key to open doors. While correctly interpreting and …...

AbstractThis paper describes our approach for the automatic grading of evidence task from the Australasian Language Technology Association (ALTA) Shared Task 2021. We developed two classification models with SVM and …...

AbstractIn the domain of natural language augmentation, the eligibility of generated samples remains not well understood. To gather insights around this eligibility issue, we apply a transformer-based similarity calculation within …...

Multilingual Transformer-based language models, usually pretrained on more than 100 languages, have been shown to achieve outstanding results in a wide range of cross- lingual transfer tasks. However, it remains …...

AbstractTransformer-based language models have taken the NLP world by storm. However, their potential for addressing important questions in language acquisition research has been largely ignored. In this work, we examined …...

AbstractAnalogies play a central role in human commonsense reasoning. The ability to recognize analogies such as “eye is to seeing what ear is to hearing”, sometimes referred to as analogical …...

Pretrained language models (PLMs) perform poorly under adversarial attacks. To im- prove the adversarial robustness, adversarial data augmentation (ADA) has been widely adopted to cover more search space of adver- …...

AbstractWe explore the task of quotability identification, in which, given a document, we aim to identify which of its passages are the most quotable, i.e. the most likely to be …...

AbstractWe propose a method to learn contextualized and generalized sentence representations using contrastive self-supervised learning. In the proposed method, a model is given a text consisting of multiple sentences. One …...

AbstractRecent work learns contextual representations of source code by reconstructing tokens from their context. For downstream semantic understanding tasks like code clone detection, these representations should ideally capture program functionality. …...

AbstractLexical complexity prediction (LCP) conveys the anticipation of the complexity level of a token or a set of tokens in a sentence. It plays a vital role in the improvement …...

AbstractThis paper present a description for the ROCLING 2021 shared task in dimensional sentiment analysis for educational texts. We submitted two runs in the final test. Both runs use the …...

AbstractPretrained transformer-based language models achieve state-of-the-art performance in many NLP tasks, but it is an open question whether the knowledge acquired by the models during pretraining resembles the linguistic knowledge …...

AbstractAnalysing whether neural language models encode linguistic information has become popular in NLP. One method of doing so, which is frequently cited to support the claim that models like BERT …...

AbstractIn human-level NLP tasks, such as predicting mental health, personality, or demographics, the number of observations is often smaller than the standard 768+ hidden state sizes of each layer within …...

AbstractIntent classification is a major task in spoken language understanding (SLU). Since most models are built with pre-collected in-domain (IND) training utterances, their ability to detect unsupported out-of-domain (OOD) utterances …...

AbstractLanguage models are trained only on text despite the fact that humans learn their first language in a highly interactive and multimodal environment where the first set of learned words …...

AbstractCoarse-grained linguistic information, such as named entities or phrases, facilitates adequately representation learning in pre-training. Previous works mainly focus on extending the objective of BERT’s Masked Language Modeling (MLM) from …...

AbstractHigh-performance neural language models have obtained state-of-the-art results on a wide range of Natural Language Processing (NLP) tasks. However, results for common benchmark datasets often do not reflect model reliability …...

AbstractRecent efforts have shown that neural text processing models are vulnerable to adversarial examples, but the nature of these examples is poorly understood. In this work, we show that adversarial …...

AbstractHumor recognition is a challenging task in natural language processing. This document presents my approaches to detect and rate humor and offense from the given text. This task includes 2 …...

AbstractThis paper presents our system submission to task 5: Toxic Spans Detection of the SemEval-2021 competition. The competition aims at detecting the spans that make a toxic span toxic. In …...

AbstractIn this paper, we investigate the utility of modern pretrained language models for the evidence grading system in the medical literature based on the ALTA 2021 shared task. We benchmark …...

AbstractTransformer language models have shown remarkable ability in detecting when a word is anomalous in context, but likelihood scores offer no information about the cause of the anomaly. In this …...

AbstractTransformers-based pretrained language models achieve outstanding results in many well-known NLU benchmarks. However, while pretraining methods are very convenient, they are expensive in terms of time and resources. This calls …...

Transfer learning from pre-trained neural lan- guage models towards downstream tasks has been a predominant theme in NLP recently. Several researchers have shown that deep NLP models learn non-trivial amount …...

AbstractIn this paper, we propose a knowledge infusion mechanism to incorporate domain knowledge into language transformers. Weakly supervised data is regarded as the main source for knowledge acquisition. We pre-train …...

AbstractAlthough pretrained language models can be fine-tuned to produce state-of-the-art results for a very wide range of language understanding tasks, the dynamics of this process are not well understood, especially …...

We study the problem of injecting knowl- edge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parameters of pre-trained models when injecting knowledge. However, …...

AbstractWe developed a system for task 6 sub-task 1 for detecting propaganda in memes. An external dataset and augmentation data-set were used to extend the official competition data-set. Data augmentation …...

AbstractThe advent of large pre-trained language models has given rise to rapid progress in the field of Natural Language Processing (NLP). While the performance of these models on standard benchmarks …...

AbstractAutomated metaphor detection is a challenging task to identify the metaphorical expression of words in a sentence. To tackle this problem, we adopt pre-trained contextualized models, e.g., BERT and RoBERTa. …...

We generalize deep self-attention distillation in MINILM (Wang et al., 2020) by only us- ing self-attention relation distillation for task- agnostic compression of pretrained Transform- ers. In particular, we define …...

AbstractRecent work indicated that pretrained language models (PLMs) such as BERT and RoBERTa can be transformed into effective sentence and word encoders even via simple self-supervised techniques. Inspired by this …...

AbstractWe use the MacBERT transformers and fine-tune them to ROCLING-2021 shared tasks using the CVAT and CVAS data. We compare the performance of MacBERT with the other two transformers BERT …...

AbstractThis paper describes and examines different systems to address Task 6 of SemEval-2021: Detection of Persuasion Techniques In Texts And Images, Subtask 1. The task aims to build a model …...

AbstractWe introduce NLQuAD, the first data set with baseline methods for non-factoid long question answering, a task requiring document-level language understanding. In contrast to existing span detection question answering data …...

AbstractToxicity detection of text has been a popular NLP task in the recent years. In SemEval-2021 Task-5 Toxic Spans Detection, the focus is on detecting toxic spans within English passages. …...

AbstractWe propose an ensemble model for predicting the lexical complexity of words and multiword expressions (MWEs). The model receives as input a sentence with a target word or MWE and …...

AbstractIt is a common belief that training deep transformers from scratch requires large datasets. Consequently, for small datasets, people usually use shallow and simple additional layers on top of pre-trained …...

AbstractIn this work, we demonstrate that the contextualized word vectors derived from pretrained masked language model-based encoders share a common, perhaps undesirable pattern across layers. Namely, we find cases of …...

AbstractRecently, pre-trained language representation models such as BERT and RoBERTa have achieved significant results in a wide range of natural language processing (NLP) tasks, however, it requires extremely high computational …...

AbstractIn this paper we propose a contextual attention based model with two-stage fine-tune training using RoBERTa. First, we perform the first-stage fine-tune on corpus with RoBERTa, so that the model …...

AbstractThis paper describes our contribution to SemEval-2021 Task 5: Toxic Spans Detection. Our approach considers toxic spans detection as a segmentation problem. The system, Waw-unet, consists of a 1-D convolutional …...

AbstractUnsupervised pre-training has led to much recent progress in natural language understanding. In this paper, we study self-training as another way to leverage unlabeled data through semi-supervised learning. To obtain …...

AbstractPre-trained language models like BERT achieve superior performances in various NLP tasks without explicit consideration of syntactic information. Meanwhile, syntactic information has been proved to be crucial for the success …...

AbstractThe increment of toxic comments on online space is causing tremendous effects on other vulnerable users. For this reason, considerable efforts are made to deal with this, and SemEval-2021 Task …...

Transferring knowledge from a label-rich do- main (source domain) to a label-scarce domain (target domain) for pervasive cross-domain Text Classification (TC) is a non-trivial task. To overcome this issue, we …...

AbstractEvent time is one of the most important features for event-event temporal relation extraction. However, explicit event time information in text is sparse. For example, only about 20% of event …...

AbstractNLP is currently dominated by language models like RoBERTa which are pretrained on billions of words. But what exact knowledge or skills do Transformer LMs learn from large-scale pretraining that …...

AbstractThis study introduces and analyzes WikiTalkEdit, a dataset of conversations and edit histories from Wikipedia, for research in online cooperation and conversation modeling. The dataset comprises dialog triplets from the …...

AbstractRecent coreference resolution models rely heavily on span representations to find coreference links between word spans. As the number of spans is O(n2) in the length of text and the …...

AbstractToxic span detection requires the detection of spans that make a text toxic instead of simply classifying the text. In this paper, a transformer-based model with auxiliary information is proposed …...

AbstractIn this paper, we describe our system used for SemEval 2021 Task 5: Toxic Spans Detection. Our proposed system approaches the problem as a token classification task. We trained our …...

AbstractNatural language processing (NLP) has been shown to perform well in various tasks, such as answering questions, ascertaining natural language inference and anomaly detection. However, there are few NLP-related studies …...

AbstractPatronizing behavior is a subtle form of bullying and when directed towards vulnerable communities, it can arise inequalities. This paper describes our system for Task 4 of SemEval-2022: Patronizing and …...

AbstractWe propose an enhanced adversarial training algorithm for fine-tuning transformer-based language models (i.e., RoBERTa) and apply it to the temporal reasoning task. Current adversarial training approaches for NLP add the …...

AbstractHuman-like biases and undesired social stereotypes exist in large pretrained language models. Given the wide adoption of these models in real-world applications, mitigating such biases has become an emerging and …...

AbstractBuilding pretrained language models is considered expensive and data-intensive, but must we increase dataset size to achieve better performance? We propose an alternative to larger training sets by automatically identifying …...

AbstractAuthorship attribution infers the likely author of an unsigned, single-authored document from a pool of candidates. Despite recent advances, a lack of standard, reproducible testbeds for Chinese language documents impedes …...

AbstractThis paper introduces the result of Team Dartmouth’s experiments on each of the five subtasks for the detection of sarcasm in English and Arabic tweets. This detection was framed as …...

AbstractDeep prompt tuning (DPT) has gained great success in most natural language processing (NLP) tasks. However, it is not well-investigated in dense retrieval where fine-tuning (FT) still dominates. When deploying …...

AbstractThe widespread diffusion of medical and political claims in the wake of COVID-19 has led to a voluminous rise in misinformation and fake news. The current vogue is to employ …...

AbstractThis study is the first likelihood ratio (LR)-based forensic text comparison study in which each text is mapped onto an embedding vector using RoBERTa as the pre-trained model. The scores …...

Warning: This paper contains examples of the language that some people may find offensive. Transformer-based Language models have achieved state-of-the-art performance on a wide range of Natural Language Process- ing …...

AbstractIn this paper, we improve on existing language resources for the low-resource Filipino language in two ways. First, we outline the construction of the TLUnified dataset, a large-scale pretraining corpus …...

AbstractModern Natural Language Processing (NLP) models are known to be sensitive to input perturbations and their performance can decrease when applied to real-world, noisy data. However, it is still unclear …...

AbstractLarge Language Models (LMs) have achieved state-of-the-art performance on many Natural Language Processing (NLP) benchmarks. With the growing number of new benchmarks, we build bigger and more complex LMs. However, …...

AbstractNeural models command state-of-the-art performance across NLP tasks, including ones involving “reasoning”. Models claiming to reason about the evidence presented to them should attend to the correct parts of the …...

AbstractIn this study, we examine the ability of contextualized representations of pretrained language model to distinguish whether sequences from instructional articles are plausible or implausible. Towards this end, we compare …...

AbstractClassification of language that favors or condones vulnerable communities (e.g., refugees, homeless, widows) has been considered a challenging task and a critical step in NLP applications. Moreover, the spread of …...

AbstractWord embeddings (Mikolov et al., 2013; Pennington et al., 2014) have been used to bolster the performance of natural language processing systems in a wide variety of tasks, including information …...

AbstractNegation poses a challenge in many natural language understanding tasks. Inspired by the fact that understanding a negated statement often requires humans to infer affirmative interpretations, in this paper we …...

AbstractIn this paper, we describe the system proposed by the MilaNLP team for the Multimedia Automatic Misogyny Identification (MAMI) challenge. We use Perceiver IO as a multimodal late fusion over …...

AbstractWe study the robustness of machine reading comprehension (MRC) models to entity renaming—do models make more wrong predictions when the same questions are asked about an entity whose name has …...

AbstractThis paper describes our system used in the SemEval-2022 Task 7(Roth et al.): Identifying Plausible Clarifications of Implicit and Under-specified Phrases. Semeval Task7 is an more complex cloze task, different …...

AbstractThis paper describes our system for Task 4 of SemEval 2022: Patronizing and Condescending Language Detection. Patronizing and Condescending Language (PCL) refers to language used with respect to vulnerable communities …...

AbstractIn this paper, we evaluate several Transformer-based language models for Icelandic on four downstream tasks: Part-of-Speech tagging, Named Entity Recognition. Dependency Parsing, and Automatic Text Summarization. We pre-train four types …...

AbstractAn important task for designing QA systems is answer sentence selection (AS2): selecting the sentence containing (or constituting) the answer to a question from a set of retrieved relevant documents. …...

AbstractTargeted studies testing knowledge of subject-verb agreement (SVA) indicate that pre-trained language models encode syntactic information. We assert that if models robustly encode subject-verb agreement, they should be able to …...

AbstractWe propose PromptBERT, a novel contrastive learning method for learning better sentence representation. We firstly analysis the drawback of current sentence embedding from original BERT and find that it is …...

AbstractHumanities scholars commonly provide evidence for claims that they make about a work of literature (e.g., a novel) in the form of quotations from the work. We collect a large-scale …...

AbstractIn this study, a named entity recognition was constructed and applied to the identification of Chinese medicine names and disease names. The results can be further used in a human-machine …...

AbstractTransformers have been shown to be able to perform deductive reasoning on inputs containing rules and statements written in the English natural language. However, it is unclear if these models …...

AbstractSuicide is an important public health concern and one of the leading causes of death worldwide. Suicidal behaviors, including suicide attempts (SA) and suicide ideations (SI), are leading risk factors …...

One of the emerging research fields in Natural Language Processing is Noise Detection (ND), the process of identifying posts containing noise information on textual data. While nu- merous datasets and …...

AbstractEven though several methods have proposed to defend textual neural network (NN) models against black-box adversarial attacks, they often defend against a specific text perturbation strategy and/or require re-training the …...

AbstractIn this paper, we describe our efforts at SemEval 2022 Shared Task 4 on Patronizing and Condescending Language (PCL) Detection. This is the first shared task to detect PCL which …...

AbstractConstruction Grammar (CxG) is a paradigm from cognitive linguistics emphasising the connection between syntax and semantics. Rather than rules that operate on lexical items, it posits constructions as the central …...

AbstractThe disambiguation of causative-passive homonymy (CPH) is potentially tricky for machines, as the causative and the passive are not distinguished by the sentences’ syntactic structure. By transforming CPH disambiguation to …...

AbstractThis paper presents the approaches and systems of the UA-KO team for the Korean portion of SemEval-2022 Task 11 on Multilingual Complex Named Entity Recognition.We fine-tuned Korean and multilingual BERT …...

AbstractThe act of appearing kind or helpful via the use of but having a feeling of superiority condescending and patronizing language can have have serious mental health implications to those …...

AbstractLarge pretrained language models (LMs) have become the central building block of many NLP applications. Training these models requires ever more computational resources and most of the existing models are …...

AbstractThis paper describes the system submitted by our team (YNU-HPCC) to SemEval-2022 Task 8: Multilingual news article similarity. This task requires participants to develop a system which could evaluate the …...

AbstractThis paper presents our submission to task 10, Structured Sentiment Analysis of the SemEval 2022 competition. The task aims to extract all elements of the fine-grained sentiment in a text. …...

AbstractIn this paper, we describe the implementations of our systems for the SemEval-2023 Task 5 ‘Clickbait Spoiling’, which involves the classification of clickbait posts in sub-task 1 and the spoiler …...

AbstractPretraining has been shown to scale well with compute, data size and data diversity. Multitask learning trains on a mixture of supervised datasets and produces improved performance compared to self-supervised …...

AbstractThis work explores the feasibility of eliciting knowledge from language models (LMs) to decode symbolism, recognizing something (e.g.,roses) as a stand-in for another (e.g., love). We present our evaluative framework, …...

AbstractIn this paper, we present a possible solution to the SemEval23 shared task of generating spoilers for clickbait headlines. Using a Zero-Shot approach with two different Transformer architectures, BLOOM and …...

AbstractFor most natural language processing tasks, the dominant practice is to finetune large pretrained transformer models (e.g., BERT) using smaller downstream datasets. Despite the success of this approach, it remains …...

AbstractSexism is an injustice afflicting women and has become a common form of oppression in social media. In recent years, the automatic detection of sexist instances has been utilized to …...

AbstractRecent advances in NLP have led to the creation of powerful language models for many languages including Ancient Greek and Latin. While prior work on Classical languages unanimously uses BERT, …...

AbstractThis paper describes our participation in SemEval-2023 Task 10, whose goal is the detection of sexism in social media. We explore some of the most popular transformer models such as …...

AbstractThe task ValueEval aims at assigning a sub- set of possible human value categories under- lying a given argument. Values behind argu- ments are often determinants to evaluate the relevance …...

AbstractNatural Language Processing techniques can be leveraged to process legal proceedings for various downstream applications, such as sum- marization of a given judgement, prediction of the judgement for a given …...

AbstractDetecting harmful content on social media plat-forms is crucial in preventing the negative ef-fects these posts can have on social media users. This paper presents our methodology for tack-ling task …...

AbstractSystem Description Paper for Task 3 Subtask 1 and 2 of Semeval 2023. The paper describes our approach to handling the News Genre Categorisation and Framing Detection using RoBERTa and …...

AbstractDirectly learning from complex examples is generally problematic for humans and machines. Indeed, a better strategy is exposing learners to examples in a reasonable, pedagogically-motivated order. Curriculum Learning (CL) has …...

AbstractClickbait spoiling and spoiler type classification in the setting of the SemEval2023 shared task five was used to explore transformer based text classification in comparison to conventional, shallow learned classifying …...

AbstractProbing has become a go-to methodology for interpreting and analyzing deep neural models in natural language processing. However, there is still a lack of understanding of the limitations and weaknesses …...

AbstractThe ability to automatically recognise the rhetorical roles of sentences in a legal case judgement is a crucial challenge to tackle since it can be useful for a number of …...

AbstractThe following system description presents our approach to the detection of persuasion techniques in online news. The given task has been framed as a multi-label classification problem. In a multi-label …...

AbstractThis paper describes the system and the resulting model submitted by our team “PCJ” to the SemEval-2023 Task 10 sub-task A contest. In this task, we need to test the …...

AbstractThis paper introduces a data augmentation technique for the task of detecting human values. Our approach involves generating additional examples using metadata that describes the labels in the datasets. We …...

AbstractThis paper describes our approach for SemEval- 2023 Task 3: Detecting the category, the fram- ing, and the persuasion techniques in online news in a multilingual setup. For Subtask 1 …...

AbstractIn this paper, we discuss the methods we applied at SemEval-2023 Task 10: Towards the Explainable Detection of Online Sexism. Given an input text, we perform three classification tasks to …...

AbstractThere is no simple definition of sexism, butit can be described as prejudice, stereotyping,or discrimination, especially against women,based on their gender. In online interactions,sexism is common. One out of ten …...

AbstractAlthough pre-trained language models (PLMs) have shown impressive performance by text-only self-supervised training, they are found lack of visual semantics or commonsense. Existing solutions often rely on explicit images for …...