ymoslem/acl-abstracts huggingface dataset:
UMUTeam at SemEval-2023 Task 3: Multilingual transformer-based model for detecting the Genre, the Framing, and the Persuasion Techniques in Online News (2023)

In this manuscript, we describe the participation of the UMUTeam in SemEval-2023 Task 3, a shared task on detecting different aspects of news articles and other web documents, such as document category, framing dimensions, and persuasion technique in a multilingual setup. The task has been organized into three related subtasks, and we have been involved in the first two. Our approach is based on a fine-tuned multilingual transformer-based model that uses the dataset of all languages at once and a sentence transformer model to extract the most relevant chunk of a text for subtasks 1 and 2. The input data was truncated to 200 tokens with 50 overlaps using the sentence-transformer model to obtain the subset of text most related to the articles' titles. Our system has performed good results in subtask 1 in most languages, and in some cases, such as French and German, we have archived first place in the official leader board. As for task 2, our system has also performed very well in all languages, ranking in all the top 10.
Appeal for Attention at SemEval-2023 Task 3: Data augmentation extension strategies for detection of online news persuasion techniques (2023)

In this paper, we proposed and explored the impact of four different dataset augmentation andextension strategies that we used for solving the subtask 3 of SemEval-2023 Task 3: multi-label persuasion techniques classification in a multi-lingual context. We consider two types of augmentation methods (one based on a modified version of synonym replacement and one based on translations) and two ways of extending the training dataset (using filtered data generated by GPT-3 and using a dataset from a previous competition). We studied the effects of the aforementioned techniques by using theaugmented and/or extended training dataset to fine-tune a pretrained XLM-RoBERTa-Large model. Using the augmentation methods alone, we managed to obtain 3rd place for English, 13th place for Italian and between the 5th to 9th places for the other 7 languages during the competition.
Chick Adams at SemEval-2023 Task 5: Using RoBERTa and DeBERTa to Extract Post and Document-based Features for Clickbait Spoiling (2023)

In this manuscript, we describe the participation of the UMUTeam in SemEval-2023 Task 5, namely, Clickbait Spoiling, a shared task on identifying spoiler type (i.e., a phrase or a passage) and generating short texts that satisfy curiosity induced by a clickbait post, i.e. generating spoilers for the clickbait post. Our participation in Task 1 is based on fine-tuning pre-trained models, which consists in taking a pre-trained model and tuning it to fit the spoiler classification task. Our system has obtained excellent results in Task 1: we outperformed all proposed baselines, being within the Top 10 for most measures. Foremost, we reached Top 3 in F1 score in the passage spoiler ranking.
KInITVeraAI at SemEval-2023 Task 3: Simple yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection (2023)

This paper presents the best-performing solution to the SemEval 2023 Task 3 on the subtask 3 dedicated to persuasion techniques detection. Due to a high multilingual character of the input data and a large number of 23 predicted labels (causing a lack of labelled data for some language-label combinations), we opted for fine-tuning pre-trained transformer-based language models. Conducting multiple experiments, we find the best configuration, which consists of large multilingual model (XLM-RoBERTa large) trained jointly on all input data, with carefully calibrated confidence thresholds for seen and surprise languages separately. Our final system performed the best on 6 out of 9 languages (including two surprise languages) and achieved highly competitive results on the remaining three languages.
jelenasteam at SemEval-2023 Task 9: Quantification of Intimacy in Multilingual Tweets using Machine Learning Algorithms: A Comparative Study on the MINT Dataset (2023)

Intimacy is one of the fundamental aspects of our social life. It relates to intimate interactions with others, often including verbal self-disclosure. In this paper, we researched machine learning algorithms for quantification of the intimacy in the tweets. A new multilingual textual intimacy dataset named MINT was used. It contains tweets in 10 languages, including English, Spanish, French, Portuguese, Italian, and Chinese in both training and test datasets, and Dutch, Korean, Hindi, and Arabic in test data only. In the first experiment, linear regression models combine with the features and word embedding, and XLM-T deep learning model were compared. In the second experiment, cross-lingual learning between languanges was tested. In the third experiments, data was clustered using K-means. The results indicate that XLM-T pre-trained embedding might be a good choice for an unsupervised learning algorithm for intimacy detection.
UL \& UM6P at SemEval-2023 Task 10: Semi-Supervised Multi-task Learning for Explainable Detection of Online Sexism (2023)

This paper introduces our participating system to the Explainable Detection of Online Sexism (EDOS) SemEval-2023 - Task 10: Explainable Detection of Online Sexism. The EDOS shared task covers three hierarchical sub-tasks for sexism detection, coarse-grained and fine-grained categorization. We have investigated both single-task and multi-task learning based on RoBERTa transformer-based language models. For improving the results, we have performed further pre-training of RoBERTa on the provided unlabeled data. Besides, we have employed a small sample of the unlabeled data for semi-supervised learning using the minimum class-confusion loss. Our system has achieved macro F1 scores of 82.25{\textbackslash}{\%}, 67.35{\textbackslash}{\%}, and 49.8{\textbackslash}{\%} on Tasks A, B, and C, respectively.
USTC-NELSLIP at SemEval-2023 Task 2: Statistical Construction and Dual Adaptation of Gazetteer for Multilingual Complex NER (2023)

This paper describes the system developed by the USTC-NELSLIP team for SemEval-2023 Task 2 Multilingual Complex Named Entity Recognition (MultiCoNER II). We propose a method named Statistical Construction and Dual Adaptation of Gazetteer (SCDAG) for Multilingual Complex NER. The method first utilizes a statistics-based approach to construct a gazetteer. Secondly, the representations of gazetteer networks and language models are adapted by minimizing the KL divergence between them at the sentence-level and entity-level. Finally, these two networks are then integrated for supervised named entity recognition (NER) training. The proposed method is applied to several state-of-the-art Transformer-based NER models with a gazetteer built from Wikidata, and shows great generalization ability across them. The final predictions are derived from an ensemble of these trained models. Experimental results and detailed analysis verify the effectiveness of the proposed method. The official results show that our system ranked 1st on one track (Hindi) in this task.
Rudolf Christoph Eucken at SemEval-2023 Task 4: An Ensemble Approach for Identifying Human Values from Arguments (2023)

The subtle human values we acquire through life experiences govern our thoughts and gets reflected in our speech. It plays an integral part in capturing the essence of our individuality and making it imperative to identify such values in computational systems that mimic human actions. Computational argumentation is a field that deals with the argumentation capabilities of humans and can benefit from identifying such values. Motivated by that, we present an ensemble approach for detecting human values from argument text. Our ensemble comprises three models: (i) An entailment-based model for determining the human values based on their descriptions, (ii) A Roberta-based classifier that predicts the set of human values from an argument. (iii) A Roberta-based classifier to predict a reduced set of human values from an argument. We experiment with different ways of combining the models and report our results. Furthermore, our best combination achieves an overall F1 score of 0.48 on the main test set.
YNU-HPCC at SemEval-2023 Task7: Multi-evidence Natural Language Inference for Clinical Trial Data Based a BioBERT Model (2023)

This paper describes the system for the YNU-HPCC team in subtask 1 of the SemEval-2023 Task 7: Multi-evidence Natural Language Inference for Clinical Trial Data (NLI4CT). This task requires judging the textual entailment relationship between the given CTR and the statement annotated by the expert annotator. This system is based on the fine-tuned Bi-directional Encoder Representation from Transformers for Biomedical Text Mining (BioBERT) model with supervised contrastive learning and back translation. Supervised contrastive learning is to enhance the classification, and back translation is to enhance the training data. Our system achieved relatively good results on the competition`s official leaderboard. The code of this paper is available at \url{https://github.com/facanhe/SemEval-2023-Task7}.
SRCB at SemEval-2023 Task 2: A System of Complex Named Entity Recognition with External Knowledge (2023)

The MultiCoNER II shared task aims at detecting semantically ambiguous and complex named entities in short and low-context settings for multiple languages. The lack of context makes the recognition of ambiguous named entities challenging. To alleviate this issue, our team SRCB proposes an external knowledge based system, where we utilize 3 different types of external knowledge retrieved in different ways. Given an original text, our system retrieves the possible labels and the descriptions for each potential entity detected by a mention detection model. And we also retrieve a related document as extra context from Wikipedia for each original text. We concatenate the original text with the external knowledge as the input of NER models. The informative contextual representations with external knowledge significantly improve the NER performance in both Chinese and English tracks. Our system win the 3rd place in the Chinese track and the 6th place in the English track.
PingAnLifeInsurance at SemEval-2023 Task 12: Sentiment Analysis for Low-resource African Languages with Multi-Model Fusion (2023)

This paper describes our system used in the SemEval-2023 Task12: Sentiment Analysis for Low-resource African Languages using Twit- ter Dataset (Muhammad et al., 2023c). The AfriSenti-SemEval Shared Task 12 is based on a collection of Twitter datasets in 14 African languages for sentiment classification. It con- sists of three sub-tasks. Task A is a monolin- gual sentiment classification which covered 12 African languages. Task B is a multilingual sen- timent classification which combined training data from Task A (12 African languages). Task C is a zero-shot sentiment classification. We uti- lized various strategies, including monolingual training, multilingual mixed training, and trans- lation technology, and proposed a weighted vot- ing method that combined the results of differ- ent strategies. Substantially, in the monolingual subtask, our system achieved Top-1 in two lan- guages (Yoruba and Twi) and Top-2 in four languages (Nigerian Pidgin, Algerian Arabic, and Swahili, Multilingual). In the multilingual subtask, Our system achived Top-2 in publish leaderBoard.
IRIT\_IRIS\_C at SemEval-2023 Task 6: A Multi-level Encoder-based Architecture for Judgement Prediction of Legal Cases and their Explanation (2023)

This paper describes our system used for sub-task C (1 {\&} 2) in Task 6: LegalEval: Understanding Legal Texts. We propose a three-level encoder-based classification architecture that works by fine-tuning a BERT-based pre-trained encoder, and post-processing the embeddings extracted from its last layers, using transformer encoder layers and RNNs. We run ablation studies on the same and analyze itsperformance. To extract the explanations for the predicted class we develop an explanation extraction algorithm, exploiting the idea of a model`s occlusion sensitivity. We explored some training strategies with a detailed analysis of the dataset. Our system ranks 2nd (macro-F1 metric) for its sub-task C-1 and 7th (ROUGE-2 metric) for sub-task C-2.
Walter Burns at SemEval-2023 Task 5: NLP-CIMAT - Leveraging Model Ensembles for Clickbait Spoiling (2023)

This paper describes our participation in the Clickbait challenge at SemEval 2023. In this work, we address the Clickbait classification task using transformers models in an ensemble configuration. We tackle the Spoiler Generation task using a two-level ensemble strategy of models trained for extractive QA, and selecting the best K candidates for multi-part spoilers. In the test partitions, our approaches obtained a classification accuracy of 0.716 for classification and a BLEU-4 score of 0.439 for spoiler generation.
Team INF-UFRGS at SemEval-2023 Task 7: Supervised Contrastive Learning for Pair-level Sentence Classification and Evidence Retrieval (2023)

This paper describes the EvidenceSCL system submitted by our team (INF-UFRGS) to SemEval-2023 Task 7: Multi-Evidence Natural Language Inference for Clinical Trial Data (NLI4CT). NLI4CT is divided into two tasks, one for determining the inference relation between a pair of statements in clinical trials and a second for retrieving a set of supporting facts from the premises necessary to justify the label predicted in the first task. Our approach uses pair-level supervised contrastive learning to classify pairs of sentences. We trained EvidenceSCL on two datasets created from NLI4CT and additional data from other NLI datasets. We show that our approach can address both goals of NLI4CT, and although it reached an intermediate position, there is room for improvement in the technique.
AU\_NLP at SemEval-2023 Task 10: Explainable Detection of Online Sexism Using Fine-tuned RoBERTa (2023)

Social media is a concept developed to link people and make the globe smaller. But it has recently developed into a center for sexist memes that target especially women. As a result, there are more events of hostile actions and harassing remarks present online. In this paper, we introduce our system for the task of online sexism detection, a part of SemEval 2023 task 10. We introduce fine-tuned RoBERTa model to address this specific problem. The efficiency of the proposed strategy is demonstrated by the experimental results reported in this research.
KINLP at SemEval-2023 Task 12: Kinyarwanda Tweet Sentiment Analysis (2023)

This paper describes the system entered by the author to the SemEval-2023 Task 12: Sentiment analysis for African languages. The system focuses on the Kinyarwanda language and uses a language-specific model. Kinyarwanda morphology is modeled in a two tier transformer architecture and the transformer model is pre-trained on a large text corpus using multi-task masked morphology prediction. The model is deployed on an experimental platform that allows users to experiment with the pre-trained language model fine-tuning without the need to write machine learning code. Our final submission to the shared task achieves second ranking out of 34 teams in the competition, achieving 72.50{\%} weighted F1 score. Our analysis of the evaluation results highlights challenges in achieving high accuracy on the task and identifies areas for improvement.
ACSMKRHR at SemEval-2023 Task 10: Explainable Online Sexism Detection(EDOS) (2023)

People are expressing their opinions online for a lot of years now. Although these opinions and comments provide people an opportunity of expressing their views, there is a lot of hate speech that can be found online. More specifically, sexist comments are very popular affecting and creating a negative impact on a lot of women and girls online. This paper describes the approaches of the SemEval-2023 Task 10 competition for Explainable Online Sexism Detection (EDOS). The task has been divided into 3 subtasks, introducing different classes of sexist comments. We have approached these tasks using the bert-cased and uncased models which are trained on the annotated dataset that has been provided in the competition. Task A provided the best F1 score of 80{\%} on the test set, and tasks B and C provided 58{\%} and 40{\%} respectively.
YNU-HPCC at SemEval-2023 Task 9: Pretrained Language Model for Multilingual Tweet Intimacy Analysis (2023)

This paper describes our fine-tuned pretrained language model for task 9 (Multilingual Tweet Intimacy Analysis, MTIA) of the SemEval 2023 competition. MTIA aims to quantitatively analyze tweets in 6 languages for intimacy, giving a score from 1 to 5. The challenge of MTIA is in semantically extracting information from code-mixed texts. To alleviate this difficulty, we suggested a solution that combines attention and memory mechanisms. The preprocessed tweets are input to the XLM-T layer to get sentence embeddings and subsequently to the bidirectional GRU layer to obtain intimacy ratings. Experimental results show an improvement in the overall performance of our model in both seen and unseen languages.
JCT\_DM at SemEval-2023 Task 10: Detection of Online Sexism: from Classical Models to Transformers (2023)

This paper presents the experimentation of systems for detecting online sexism relying on classical models, deep learning models, and transformer-based models. The systems aim to provide a comprehensive approach to handling the intricacies of online language, including slang and neologisms. The dataset consists of labeled and unlabeled data from Gab and Reddit, which allows for the development of unsupervised or semi-supervised models. The system utilizes TF-IDF with classical models, bidirectional models with embedding, and pre-trained transformer models. The paper discusses the experimental setup and results, demonstrating the effectiveness of the system in detecting online sexism.
PAI at SemEval-2023 Task 2: A Universal System for Named Entity Recognition with External Entity Information (2023)

The MultiCoNER II task aims to detect complex, ambiguous, and fine-grained named entities in low-context situations and noisy scenarios like the presence of spelling mistakes and typos for multiple languages. The task poses significant challenges due to the scarcity of contextual information, the high granularity of the entities(up to 33 classes), and the interference of noisy data. To address these issues, our team PAI proposes a universal Named Entity Recognition (NER) system that integrates external entity information to improve performance. Specifically, our system retrieves entities with properties from the knowledge base (i.e. Wikipedia) for a given text, then concatenates entity information with the input sentence and feeds it into Transformer-based models. Finally, our system wins 2 first places, 4 second places, and 1 third place out of 13 tracks. The code is publicly available at \url{https://github.com/diqiuzhuanzhuan/semeval-2023}.
NITS\_Legal at SemEval-2023 Task 6: Rhetorical Roles Prediction of Indian Legal Documents via Sentence Sequence Labeling Approach (2023)

Legal documents are notorious for their complexity and domain-specific language, making them challenging for legal practitioners as well as non-experts to comprehend. To address this issue, the LegalEval 2023 track proposed several shared tasks, including the task of Rhetorical Roles Prediction (Task A). We participated as NITS{\_}Legal team in Task A and conducted exploratory experiments to improve our understanding of the task. Our results suggest that sequence context is crucial in performing rhetorical roles prediction. Given the lengthy nature of legal documents, we propose a BiLSTM-based sentence sequence labeling approach that uses a local context-incorporated dataset created from the original dataset. To better represent the sentences during training, we extract legal domain-specific sentence embeddings from a Legal BERT model. Our experimental findings emphasize the importance of considering local context instead of treating each sentence independently to achieve better performance in this task. Our approach has the potential to improve the accessibility and usability of legal documents.
I2C-Huelva at SemEval-2023 Task 9: Analysis of Intimacy in Multilingual Tweets Using Resampling Methods and Transformers (2023)

Nowadays, intimacy is a fundamental aspect of how we relate to other people in social settings. The most frequent way in which we can determine a high level of intimacy is in the use of certain emoticons, curse words, verbs, etc. This paper presents the approach developed to solve SemEval 2023 task 9: Multiligual Tweet Intimacy Analysis. To address the task, a transfer learning approach was conducted by fine tuning various pre-trained languagemodels. Since the dataset supplied by the organizer was highly imbalanced, our main strategy to obtain high prediction values was the implementation of different oversampling and undersampling techniques on the training set. Our final submission achieved an overall Pearson`s r of 0.497.
I2C-Huelva at SemEval-2023 Task 10: Ensembling Transformers Models for the Detection of Online Sexism (2023)

This work details our approach for addressing Tasks A and B of the Semeval 2023 Task 10: Explainable Detection of Online Sexism (EDOS). For Task A a simple ensemble based of majority vote system was presented. To build our proposal, first a review of transformers was carried out and the 3 best performing models were selected to be part of the ensemble. Next, for these models, the best hyperpameters were searched using a reduced data set. Finally, we trained these models using more data. During the development phase, our ensemble system achieved an f1-score of 0.8403. For task B, we developed a model based on the deBERTa transformer, utilizing the hyperparameters identified for task A. During the development phase, our proposed model attained an f1-score of 0.6467. Overall, our methodology demonstrates an effective approach to the tasks, leveraging advanced machine learning techniques and hyperparameters searches to achieve high performance in detecting and classifying instances of sexism in online text.
ZBL2W at SemEval-2023 Task 9: A Multilingual Fine-tuning Model with Data Augmentation for Tweet Intimacy Analysis (2023)

This paper describes our system used in the SemEval-2023 Task 9 Multilingual Tweet Intimacy Analysis. There are two key challenges in this task: the complexity of multilingual and zero-shot cross-lingual learning, and the difficulty of semantic mining of tweet intimacy. To solve the above problems, our system extracts contextual representations from the pretrained language models, XLM-T, and employs various optimization methods, including adversarial training, data augmentation, ordinal regression loss and special training strategy. Our system ranked 14th out of 54 participating teams on the leaderboard and ranked 10th on predicting languages not in the training data. Our code is available on Github.
NCUEE-NLP at SemEval-2023 Task 7: Ensemble Biomedical LinkBERT Transformers in Multi-evidence Natural Language Inference for Clinical Trial Data (2023)

This study describes the model design of the NCUEE-NLP system for the SemEval-2023 NLI4CT task that focuses on multi-evidence natural language inference for clinical trial data. We use the LinkBERT transformer in the biomedical domain (denoted as BioLinkBERT) as our main system architecture. First, a set of sentences in clinical trial reports is extracted as evidence for premise-statement inference. This identified evidence is then used to determine the inference relation (i.e., entailment or contradiction). Finally, a soft voting ensemble mechanism is applied to enhance the system performance. For Subtask 1 on textual entailment, our best submission had an F1-score of 0.7091, ranking sixth among all 30 participating teams. For Subtask 2 on evidence retrieval, our best result obtained an F1-score of 0.7940, ranking ninth of 19 submissions.